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Title: Improving Semantic Parsing with Neural Generator-Reranker Architecture. Abstract: Semantic parsing is the problem of deriving machine interpretable meaning representations from natural language utterances. Neural models with encoder-decoder architectures have recently achieved substantial improvements over traditional methods. Although neural semantic parsers appear to have relatively high recall using large beam sizes, there is room for improvement with respect to one-best precision. In this work, we propose a generator-reranker architecture for semantic parsing. The generator produces a list of potential candidates and the reranker, which consists of a pre-processing step for the candidates followed by a novel critic network, reranks these candidates based on the similarity between each candidate and the input sentence. We show the advantages of this approach along with how it improves the parsing performance through extensive analysis. We experiment our model on three semantic parsing datasets (GEO, ATIS, and OVERNIGHT). The overall architecture achieves the state-of-the-art results in all three datasets. | 0reject
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Title: Characterizing Missing Information in Deep Networks Using Backpropagated Gradients. Abstract: Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data. We attribute this vulnerability to the limitations inherent to activation-based representation. To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information. In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information. To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations. We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes. Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets. The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively. | 0reject
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Title: Generating Symbolic Reasoning Problems with Transformer GANs. Abstract: Constructing training data for symbolic reasoning domains is challenging: Existing instances are typically hand-crafted and too few to be trained on directly and synthetically generated instances are often hard to evaluate in terms of their meaningfulness. We study the capabilities of GANs and Wasserstein GANs equipped with Transformer encoders to generate sensible and challenging training data for symbolic reasoning domains. We conduct experiments on two problem domains where Transformers have been successfully applied recently: symbolic mathematics and temporal specifications in verification. Even without autoregression, our GAN models produce syntactically correct instances and we show that these can be used as meaningful substitutes for real training data when training a classifier. Using a GAN setting also allows us to alter the target distribution: We show that by adding a classifier uncertainty part to the generator objective, we obtain a dataset that is even harder to solve for a classifier than our original dataset. | 0reject
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Title: Variational Disentangled Attention for Regularized Visual Dialog. Abstract: One of the most important challenges in a visual dialog is to effectively extract the information from a given image and its historical conversation which are related to the current question. Many studies adopt the soft attention mechanism in different information sources due to its simplicity and ease of optimization. However, some of visual dialogs are observed in a single round. This implies that there is no substantial correlation between individual rounds of questions and answers. This paper presents a unified approach to disentangled attention to deal with context-free visual dialogs. The question is disentangled in latent representation. In particular, an informative regularization is imposed to strengthen the dependence between vision and language by pretraining on the visual question answering before transferring to visual dialog. Importantly, a novel variational attention mechanism is developed and implemented by a local reparameterization trick which carries out a discrete attention to identify the relevant conversations in a visual dialog. A set of experiments are evaluated to illustrate the merits of the proposed attention and regularization schemes for context-free visual dialogs. | 2withdrawn
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Title: Temperature Regret Matching for Imperfect-Information Games. Abstract: Counterfactual regret minimization (CFR) methods are effective for solving two player zero-sum extensive games with imperfect information. Regret matching (RM) plays a crucial role in CFR and its variants to approach Nash equilibrium. In this paper, we present Temperature Regret Matching (TRM), a novel RM algorithm that adopts a different strategy. Also, we consider not only the opponent's strategy under the current strategy but also the opponent's strategies of the several last iterations for updating the external regret of each iteration. Furthermore, we theoretically demonstrate that the update of TRM converges to Nash Equilibrium. Competitive results in imperfect-information games have verified its effectiveness and efficiency. | 2withdrawn
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Title: Representation Quality Explain Adversarial Attacks. Abstract: Neural networks have been shown vulnerable to adversarial samples. Slightly perturbed input images are able to change the classification of accurate models, showing that the representation learned is not as good as previously thought. To aid the development of better neural networks, it would be important to evaluate to what extent are current neural networks' representations capturing the existing features. Here we propose a way to evaluate the representation quality of neural networks using a novel type of zero-shot test, entitled Raw Zero-Shot. The main idea lies in the fact that some features are present on unknown classes and that unknown classes can be defined as a combination of previous learned features without representation bias (a bias towards representation that maps only current set of input-outputs and their boundary). To evaluate the soft-labels of unknown classes, two metrics are proposed. One is based on clustering validation techniques (Davies-Bouldin Index) and the other is based on soft-label distance of a given correct soft-label.
Experiments show that such metrics are in accordance with the robustness to adversarial attacks and might serve as a guidance to build better models as well as be used in loss functions to create new types of neural networks. Interestingly, the results suggests that dynamic routing networks such as CapsNet have better representation while current deeper DNNs are trading off representation quality for accuracy. | 0reject
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Title: Mirror Sample Based Distribution Alignment for Unsupervised Domain Adaption. Abstract: Unsupervised Domain Adaption has great value in both machine learning theory and applications.
The core issue is how to minimize the domain shift. Motivated by the more and more sophisticated distribution alignment methods in sample level, we introduce a novel concept named (virtual) mirror, which represents the counterpart sample in the other domains.The newly-introduced mirror loss using the virtual mirrors establishes the connection cross domains and pushes the virtual mirror pairs together in the aligned representation space. Our proposed method does not align the samples cross domains coarsely or arbitrarily, thus does not distort the internal distribution of the underline distribution and brings better asymptotic performances.
Experiments on several benchmarks validate the superior performance of our methods. | 2withdrawn
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Title: A SINGLE SHOT PCA-DRIVEN ANALYSIS OF NETWORK STRUCTURE TO REMOVE REDUNDANCY. Abstract: Deep learning models have outperformed traditional methods in many fields such
as natural language processing and computer vision. However, despite their
tremendous success, the methods of designing optimal Convolutional Neural Networks
(CNNs) are still based on heuristics or grid search. The resulting networks
obtained using these techniques are often overparametrized with huge computational
and memory requirements. This paper focuses on a structured, explainable
approach towards optimal model design that maximizes accuracy while keeping
computational costs tractable. We propose a single-shot analysis of a trained CNN
that uses Principal Component Analysis (PCA) to determine the number of filters
that are doing significant transformations per layer, without the need for retraining.
It can be interpreted as identifying the dimensionality of the hypothesis space
under consideration. The proposed technique also helps estimate an optimal number
of layers by looking at the expansion of dimensions as the model gets deeper.
This analysis can be used to design an optimal structure of a given network on
a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate
these techniques by optimizing VGG and AlexNet networks on CIFAR-10,
CIFAR-100 and ImageNet datasets. | 2withdrawn
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Title: SPP-RL: State Planning Policy Reinforcement Learning. Abstract: We introduce an algorithm for reinforcement learning, in which the actor plans for the next state provided the current state. To communicate the actor output to the environment we incorporate an inverse dynamics control model and train it using supervised learning.
We train the RL agent using off-policy state-of-the-art reinforcement learning algorithms: DDPG, TD3, and SAC. To guarantee that the target states are physically relevant, the overall learning procedure is formulated as a constrained optimization problem, solved via the classical Lagrangian optimization method. We benchmark the state planning RL approach using a varied set of continuous environments, including standard MuJoCo tasks, safety-gym level 0 environments, and AntPush. In SPP approach the optimal policy is being searched for in the space of state-state mappings, a considerably larger space than the traditional space of state-action mappings. We report that quite surprisingly SPP implementations attain superior performance to vanilla state-of-the-art off-policy RL algorithms in the tested environments. | 0reject
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Title: Kokoyi: Executable LaTeX for End-to-end Deep Learning. Abstract: Despite substantial efforts from the deep learning system community to relieve researchers and practitioners from the burden of implementing models with ever-growing complexity, a considerable lingual gap remains between developing models in the language of mathematics and implementing them in the languages of computer. The mission of Kokoyi is to close this gap by enabling automatic translation of mathematics into efficient implementations, thereby making math-in-codes and math-in-model consistent. This paper presents our first step towards the goal: kokoyi-lang, a programming language with the syntax of LaTeX and the semantics of deep learning mathematics, and a prototype kokoyi-lang compiler and runtime supporting advanced optimizations such as auto-batching. Kokoyi is integrated with Jupyter Notebook, and will be released in open-source. | 0reject
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Title: The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders . Abstract: Training and using modern neural-network based latent-variable generative models (like Variational Autoencoders) often require simultaneously training a generative direction along with an inferential (encoding) direction, which approximates the posterior distribution over the latent variables. Thus, the question arises: how complex does the inferential model need to be, in order to be able to accurately model the posterior distribution of a given generative model? In this paper, we identify an important property of the generative map impacting the required size of the encoder. We show that if the generative map is ``strongly invertible" (in a sense we suitably formalize), the inferential model need not be much more complex. Conversely, we prove that there exist non-invertible generative maps, for which the encoding direction needs to be exponentially larger (under standard assumptions in computational complexity). Importantly, we do not require the generative model to be layerwise invertible, which a lot of the related literature assumes and isn't satisfied by many architectures used in practice (e.g. convolution and pooling based networks). Thus, we provide theoretical support for the empirical wisdom that learning deep generative models is harder when data lies on a low-dimensional manifold. | 1accept
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Title: Adversarial training with perturbation generator networks. Abstract: Despite the remarkable development of recent deep learning techniques, neural networks are still vulnerable to adversarial attacks, i.e., methods that fool the neural networks with perturbations that are too small for human eyes to perceive. Many adversarial training methods were introduced as to solve this problem, using adversarial examples as a training data. However, these adversarial attack methods used in these techniques are fixed, making the model stronger only to attacks used in training, which is widely known as an overfitting problem. In this paper, we suggest a novel adversarial training approach. In addition to the classifier, our method adds another neural network that generates the most effective adversarial perturbation by finding the weakness of the classifier. This perturbation generator network is trained to produce perturbations that maximize the loss function of the classifier, and these adversarial examples train the classifier with a true label. In short, the two networks compete with each other, performing a minimax game. In this scenario, attack patterns created by the generator network are adaptively altered to the classifier, mitigating the overfitting problem mentioned above. We theoretically proved that our minimax optimization problem is equivalent to minimizing the adversarial loss after all. Beyond this, we proposed an evaluation method that could accurately compare a wide-range of adversarial algorithms. Experiments with various datasets show that our method outperforms conventional adversarial algorithms. | 0reject
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Title: Self-Supervised Generalisation with Meta Auxiliary Learning. Abstract: Auxiliary learning has been shown to improve the generalisation performance of a principal task. But typically, this requires manually-defined auxiliary tasks based on domain knowledge. In this paper, we consider that it may be possible to automatically learn these auxiliary tasks to best suit the principal task, towards optimum auxiliary tasks without any human knowledge. We propose a novel method, Meta Auxiliary Learning (MAXL), which we design for the task of image classification, where the auxiliary task is hierarchical sub-class image classification. The role of the meta learner is to determine sub-class target labels to train a multi-task evaluator, such that these labels improve the generalisation performance on the principal task. Experiments on three different CIFAR datasets show that MAXL outperforms baseline auxiliary learning methods, and is competitive even with a method which uses human-defined sub-class hierarchies. MAXL is self-supervised and general, and therefore offers a promising new direction towards automated generalisation. | 0reject
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Title: Principled Weight Initialization for Hypernetworks. Abstract: Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner. Despite extensive applications ranging from multi-task learning to Bayesian deep learning, the problem of optimizing hypernetworks has not been studied to date. We observe that classical weight initialization methods like Glorot & Bengio (2010) and He et al. (2015), when applied directly on a hypernet, fail to produce weights for the mainnet in the correct scale. We develop principled techniques for weight initialization in hypernets, and show that they lead to more stable mainnet weights, lower training loss, and faster convergence. | 1accept
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Title: Enhanced countering adversarial attacks via input denoising and feature restoring. Abstract: Despite the fact that deep neural networks (DNNs) have achieved prominent performance in various applications, it is well known that DNNs are vulnerable to adversarial examples/samples (AEs) with imperceptible perturbations in clean/original samples. To overcome the weakness of the existing defense methods against adversarial attacks, which damages the information on the original samples, leading to the decrease of the target classifier accuracy, this paper presents an enhanced countering adversarial attack method IDFR (via Input Denoising and Feature Restoring). The proposed IDFR is made up of an enhanced input denoiser (ID) and a hidden lossy feature restorer (FR) based on the convex hull optimization. Extensive experiments conducted on benchmark datasets show that the proposed IDFR outperforms the various state-of-the-art defense methods, and is highly effective for protecting target models against various adversarial black-box or white-box attacks. | 2withdrawn
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Title: How to Design Sample and Computationally Efficient VQA Models. Abstract: In multi-modal reasoning tasks, such as visual question answering (VQA), there have been many modeling and training paradigms tested. Previous models propose different methods for the vision and language tasks, but which ones perform the best while being sample and computationally efficient? Based on our experiments, we find that representing the text as probabilistic programs and images as object-level scene graphs best satisfy these desiderata. We extend existing models to leverage these soft programs and scene graphs to train on question answer pairs in an end-to-end manner. Empirical results demonstrate that this differentiable end-to-end program executor is able to maintain state-of-the-art accuracy while being sample and computationally efficient. | 0reject
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Title: QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings. Abstract: We propose an effective embedding model, named QuatRE, to learn quaternion embeddings for entities and relations in knowledge graphs. QuatRE aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. QuatRE achieves this goal by further associating each relation with two relation-aware quaternion vectors which are used to rotate the head and tail entities' quaternion embeddings, respectively. To obtain the triple score, QuatRE rotates the rotated embedding of the head entity using the normalized quaternion embedding of the relation, followed by a quaternion-inner product with the rotated embedding of the tail entity. Experimental results demonstrate that our QuatRE produces state-of-the-art performances on well-known benchmark datasets for knowledge graph completion. | 2withdrawn
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Title: Initializing ReLU networks in an expressive subspace of weights. Abstract: Using a mean-field theory of signal propagation, we analyze the evolution of correlations between two signals propagating forward through a deep ReLU network with correlated weights. Signals become highly correlated in deep ReLU networks with uncorrelated weights. We show that ReLU networks with anti-correlated weights can avoid this fate and have a chaotic phase where the signal correlations saturate below unity. Consistent with this analysis, we find that networks initialized with anti-correlated weights can train faster by taking advantage of the increased expressivity in the chaotic phase. An initialization scheme combining this with a previously proposed strategy of using an asymmetric initialization to reduce dead node probability shows consistently lower training times compared to various other initializations on synthetic and real-world datasets. Our study suggests that use of initial distributions with correlations in them can help in reducing training time. | 2withdrawn
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Title: Efficient Model Performance Estimation via Feature Histories. Abstract: An essential step in the task of model selection, such as hyper-parameter optimization (HPO) or neural architecture search (NAS), is the process of estimating a candidate model's (hyper-parameter or architecture) performance. Due to the high computational cost of training models until full convergence, it is necessary to develop efficient methods that can accurately estimate a model's best performance using only a small time budget. To this end, we propose a novel performance estimation method which uses a history of model features observed during the early stages of training to obtain an estimate of final performance. Our method is versatile. It can be combined with different search algorithms and applied to various configuration spaces in HPO and NAS. Using a sampling-based search algorithm and parallel computing, our method can find an architecture which is better than DARTS and with an 80\% reduction in search time. | 2withdrawn
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Title: Representation Degeneration Problem in Training Natural Language Generation Models. Abstract: We study an interesting problem in training neural network-based models for natural language generation tasks, which we call the \emph{representation degeneration problem}. We observe that when training a model for natural language generation tasks through likelihood maximization with the weight tying trick, especially with big training datasets, most of the learnt word embeddings tend to degenerate and be distributed into a narrow cone, which largely limits the representation power of word embeddings. We analyze the conditions and causes of this problem and propose a novel regularization method to address it. Experiments on language modeling and machine translation show that our method can largely mitigate the representation degeneration problem and achieve better performance than baseline algorithms. | 1accept
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Title: Path Auxiliary Proposal for MCMC in Discrete Space. Abstract: Energy-based Model (EBM) offers a powerful approach for modeling discrete structure, but both inference and learning of EBM are hard as it involves sampling from discrete distributions. Recent work shows Markov Chain Monte Carlo (MCMC) with the informed proposal is a powerful tool for such sampling. However, an informed proposal only allows local updates as it requires evaluating all energy changes in the neighborhood.
In this work, we present a path auxiliary algorithm that uses a composition of local moves to efficiently explore large neighborhoods. We also give a fast version of our algorithm that only queries the evaluation of energy function twice for each proposal via linearization of the energy function. Empirically, we show that our path auxiliary algorithms considerably outperform other generic samplers on various discrete models for sampling, inference, and learning. Our method can also be used to train deep EBMs for high-dimensional discrete data. | 1accept
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Title: Laplacian Eigenspaces, Horocycles and Neuron Models on Hyperbolic Spaces. Abstract: We use hyperbolic Poisson kernel to construct the horocycle neuron model on hyperbolic spaces, which is a spectral generalization of the classical neuron model. We prove a universal approximation theorem for horocycle neurons. As a corollary, this theorem leads to a state-of-the-art result on the expressivity of neurons of the hyperbolic MLR. Our experiments get state-of-the-art results on the Poincare-embedding tree classification task and the two-dimensional visualization of images. | 0reject
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Title: Feature quantization for parsimonious and interpretable predictive models. Abstract: For regulatory and interpretability reasons, the logistic regression is still widely used by financial institutions to learn the refunding probability of a loan from applicant's historical data. To improve prediction accuracy and interpretability, a preprocessing step quantizing both continuous and categorical data is usually performed: continuous features are discretized by assigning factor levels to intervals and, if numerous, levels of categorical features are grouped. However, a better predictive accuracy can be reached by embedding this quantization estimation step directly into the predictive estimation step itself. By doing so, the predictive loss has to be optimized on a huge and untractable discontinuous quantization set. To overcome this difficulty, we introduce a specific two-step optimization strategy: first, the optimization problem is relaxed by approximating discontinuous quantization functions by smooth functions; second, the resulting relaxed optimization problem is solved via a particular neural network and stochastic gradient descent. The strategy gives then access to good candidates for the original optimization problem after a straightforward maximum a posteriori procedure to obtain cutpoints. The good performances of this approach, which we call glmdisc, are illustrated on simulated and real data from the UCI library and Crédit Agricole Consumer Finance (a major European historic player in the consumer credit market). The results show that practitioners finally have an automatic all-in-one tool that answers their recurring needs of quantization for predictive tasks. | 0reject
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Title: LambdaNetworks: Modeling long-range Interactions without Attention. Abstract: We present lambda layers -- an alternative framework to self-attention -- for capturing long-range interactions between an input and structured contextual information (e.g. a pixel surrounded by other pixels). Lambda layers capture such interactions by transforming available contexts into linear functions, termed lambdas, and applying these linear functions to each input separately. Similar to linear attention, lambda layers bypass expensive attention maps, but in contrast, they model both content and position-based interactions which enables their application to large structured inputs such as images. The resulting neural network architectures, LambdaNetworks, significantly outperform their convolutional and attentional counterparts on ImageNet classification, COCO object detection and instance segmentation, while being more computationally efficient. Additionally, we design LambdaResNets, a family of hybrid architectures across different scales, that considerably improves the speed-accuracy tradeoff of image classification models. LambdaResNets reach excellent accuracies on ImageNet while being 3.2 - 4.4x faster than the popular EfficientNets on modern machine learning accelerators. In large-scale semi-supervised training with an additional 130M pseudo-labeled images, LambdaResNets achieve up to 86.7% ImageNet accuracy while being 9.5x faster than EfficientNet NoisyStudent and 9x faster than a Vision Transformer with comparable accuracies. | 1accept
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Title: Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets. Abstract: Recent progress in offline reinforcement learning (RL) has made it possible to train strong RL agents from previously-collected, static datasets. However, depending on the quality of the trained agents and the application being considered, it is often desirable to improve such offline RL agents with further online interaction. As it turns out, fine-tuning offline RL agents is a non-trivial challenge, due to distribution shift – the agent encounters out-of-distribution samples during online interaction, which may cause bootstrapping error in Q-learning and instability during fine-tuning. In order to address the issue, we present a simple yet effective framework, which incorporates a balanced replay scheme and an ensemble distillation scheme. First, we propose to keep separate offline and online replay buffers, and carefully balance the number of samples from each buffer during updates. By utilizing samples from a wider distribution, i.e., both online and offline samples, we stabilize the Q-learning. Next, we present an ensemble distillation scheme, where we train an ensemble of independent actor-critic agents, then distill the policies into a single policy. In turn, we improve the policy using the Q-ensemble during fine-tuning, which allows the policy updates to be more robust to error in each individual Q-function. We demonstrate the superiority of our method on MuJoCo datasets from the recently proposed D4RL benchmark suite.
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Title: Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue. Abstract: Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. The model named sequential knowledge transformer (SKT) can keep track of the prior and posterior distribution over knowledge; as a result, it can not only reduce the ambiguity caused from the diversity in knowledge selection of conversation but also better leverage the response information for proper choice of knowledge. Our experimental results show that the proposed model improves the knowledge selection accuracy and subsequently the performance of utterance generation. We achieve the new state-of-the-art performance on Wizard of Wikipedia (Dinan et al., 2019) as one of the most large-scale and challenging benchmarks. We further validate the effectiveness of our model over existing conversation methods in another knowledge-based dialogue Holl-E dataset (Moghe et al., 2018). | 1accept
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Title: ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA. Abstract: Deep neural networks based on unfolding an iterative algorithm, for example, LISTA (learned iterative shrinkage thresholding algorithm), have been an empirical success for sparse signal recovery. The weights of these neural networks are currently determined by data-driven “black-box” training. In this work, we propose Analytic LISTA (ALISTA), where the weight matrix in LISTA is computed as the solution to a data-free optimization problem, leaving only the stepsize and threshold parameters to data-driven learning. This significantly simplifies the training. Specifically, the data-free optimization problem is based on coherence minimization. We show our ALISTA retains the optimal linear convergence proved in (Chen et al., 2018) and has a performance comparable to LISTA. Furthermore, we extend ALISTA to convolutional linear operators, again determined in a data-free manner. We also propose a feed-forward framework that combines the data-free optimization and ALISTA networks from end to end, one that can be jointly trained to gain robustness to small perturbations in the encoding model. | 1accept
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Title: Self-Supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection. Abstract: Anomaly detection (AD) - separating anomalies from normal data - has many applications across domains, from manufacturing to healthcare. While most previous works have been shown to be effective for cases with fully or partially labeled data, that setting is in practice less common due to labeling being particularly tedious for this task. In this paper, we focus on fully unsupervised AD, in which the entire training dataset, containing both normal and anomalous samples, is unlabeled. To tackle this problem effectively, we propose to improve the robustness of one-class classification trained on self-supervised representations using a data refinement process. Our proposed data refinement approach is based on an ensemble of one-class classifiers (OCCs), each of which is trained on a disjoint subset of training data. Representations learned by self-supervised learning on the refined data are iteratively updated as the refinement improves. We demonstrate our method on various unsupervised AD tasks with image and tabular data. With a 10% anomaly ratio on CIFAR-10 image data / 2.5% anomaly ratio on Thyroid tabular data, the proposed method outperforms the state-of-the-art one-class classification method by 6.3 AUC and 12.5 average precision / 22.9 F1-score. | 0reject
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Title: Model Compression with Generative Adversarial Networks. Abstract: More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Model compression (also known as distillation) alleviates this burden by training a less expensive student model to mimic the expensive teacher model while maintaining most of the original accuracy. However, when fresh data is unavailable for the compression task, the teacher's training data is typically reused, leading to suboptimal compression. In this work, we propose to augment the compression dataset with synthetic data from a generative adversarial network (GAN) designed to approximate the training data distribution. Our GAN-assisted model compression (GAN-MC) significantly improves student accuracy for expensive models such as deep neural networks and large random forests on both image and tabular datasets. Building on these results, we propose a comprehensive metric—the Compression Score—to evaluate the quality of synthetic datasets based on their induced model compression performance. The Compression Score captures both data diversity and discriminability, and we illustrate its benefits over the popular Inception Score in the context of image classification. | 0reject
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Title: Hypersphere Face Uncertainty Learning. Abstract: An emerging line of research has found that \emph{hyperspherical} spaces better match the underlying geometry of facial images, as evidenced by the state-of-the-art facial recognition methods which benefit empirically from hyperspherical representations. Yet, these approaches rely on deterministic embeddings and hence suffer from the \emph{feature ambiguity dilemma}, whereby ambiguous or noisy images are mapped into poorly learned regions of representation space, leading to inaccuracies~\citep{shi2019probabilistic}. PFE is the first attempt to circumvent this dilemma. However, we theoretically and empirically identify two main failure cases of PFE when it is applied to hyperspherical deterministic embeddings aforementioned. To address these issues, in this paper, we propose a novel framework for face uncertainty learning in hyperspherical space. Mathematically, we extend the \emph{von Mises Fisher} density to its $r$-radius counterpart and derive an optimization objective in closed form. For feature comparison, we also derive a closed-form mutual likelihood score for latents lying on hypersphere. Extensive experimental results on multiple challenging benchmarks confirm our hypothesis and theory, and showcase the superior performance of our framework against prior probabilistic methods and conventional hyperspherical deterministic embeddings both in risk-controlled recognition tasks and in face verification and identification tasks. | 2withdrawn
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Title: AutoSlim: Towards One-Shot Architecture Search for Channel Numbers. Abstract:
We study how to set the number of channels in a neural network to achieve better accuracy under constrained resources (e.g., FLOPs, latency, memory footprint or model size). A simple and one-shot approach, named AutoSlim, is presented. Instead of training many network samples and searching with reinforcement learning, we train a single slimmable network to approximate the network accuracy of different channel configurations. We then iteratively evaluate the trained slimmable model and greedily slim the layer with minimal accuracy drop. By this single pass, we can obtain the optimized channel configurations under different resource constraints. We present experiments with MobileNet v1, MobileNet v2, ResNet-50 and RL-searched MNasNet on ImageNet classification. We show significant improvements over their default channel configurations. We also achieve better accuracy than recent channel pruning methods and neural architecture search methods with 100X lower search cost.
Notably, by setting optimized channel numbers, our AutoSlim-MobileNet-v2 at 305M FLOPs achieves 74.2% top-1 accuracy, 2.4% better than default MobileNet-v2 (301M FLOPs), and even 0.2% better than RL-searched MNasNet (317M FLOPs). Our AutoSlim-ResNet-50 at 570M FLOPs, without depthwise convolutions, achieves 1.3% better accuracy than MobileNet-v1 (569M FLOPs).
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Title: Weakly-Supervised Trajectory Segmentation for Learning Reusable Skills. Abstract: Learning useful and reusable skill, or sub-task primitives, is a long-standing problem in sensorimotor control. This is challenging because it's hard to define what constitutes a useful skill. Instead of direct manual supervision which is tedious and prone to bias, in this work, our goal is to extract reusable skills from a collection of human demonstrations collected directly for several end-tasks. We propose a weakly-supervised approach for trajectory segmentation following the classic work on multiple instance learning. Our approach is end-to-end trainable, works directly from high-dimensional input (e.g., images) and only requires the knowledge of what skill primitives are present at training, without any need of segmentation or ordering of primitives. We evaluate our approach via rigorous experimentation across four environments ranging from simulation to real world robots, procedurally generated to human collected demonstrations and discrete to continuous action space. Finally, we leverage the generated skill segmentation to demonstrate preliminary evidence of zero-shot transfer to new combinations of skills. Result videos at https://sites.google.com/view/trajectory-segmentation/ | 0reject
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Title: Robust Generalization of Quadratic Neural Networks via Function Identification. Abstract: A key challenge facing deep learning is that neural networks are often not robust to shifts in the underlying data distribution. We study this problem from the perspective of the statistical concept of parameter identification. Generalization bounds from learning theory often assume that the test distribution is close to the training distribution. In contrast, if we can identify the ``true'' parameters, then the model generalizes to arbitrary distribution shifts. However, neural networks are typically overparameterized, making parameter identification impossible. We show that for quadratic neural networks, we can identify the function represented by the model even though we cannot identify its parameters. Thus, we can obtain robust generalization bounds even in the overparameterized setting. We leverage this result to obtain new bounds for contextual bandits and transfer learning with quadratic neural networks. Overall, our results suggest that we can improve robustness of neural networks by designing models that can represent the true data generating process. In practice, the true data generating process is often very complex; thus, we study how our framework might connect to neural module networks, which are designed to break down complex tasks into compositions of simpler ones. We prove robust generalization bounds when individual neural modules are identifiable. | 0reject
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Title: Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation. Abstract: The estimation of the ratio of two probability densities has garnered attention as the density ratio is useful in various machine learning tasks, such as anomaly detection and domain adaptation. To estimate the density ratio, methods collectively known as direct density ratio estimation (DRE) have been explored. These methods are based on the minimization of the Bregman (BR) divergence between a density ratio model and the true density ratio. However, existing direct DRE suffers from serious overfitting when using flexible models such as neural networks. In this paper, we introduce a non-negative correction for empirical risk using only the prior knowledge of the upper bound of the density ratio. This correction makes a DRE method more robust against overfitting and enables the use of flexible models. In the theoretical analysis, we discuss the consistency of the empirical risk. In our experiments, the proposed estimators show favorable performance in inlier-based outlier detection and covariate shift adaptation. | 0reject
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Title: Multilevel physics informed neural networks (MPINNs). Abstract: In this paper we introduce multilevel physics informed neural networks (MPINNs). Inspired by classical multigrid methods for the solution of linear systems arising from the discretization of PDEs, our MPINNs are based on the classical correction scheme, which represents the solution as the sum of a fine and a coarse term that are optimized in an alternate way. We show that the proposed approach allows to reproduce in the neural network training the classical acceleration effect observed for classical multigrid methods, thus providing a PINN that shows improved performance compared to the state-of-the-art. Thanks to the support of the coarse model, MPINNs provide indeed a faster and improved decrease of the approximation error in the case both of elliptic and nonlinear equations.
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Title: Identifying through Flows for Recovering Latent Representations. Abstract: Identifiability, or recovery of the true latent representations from which the observed data originates, is de facto a fundamental goal of representation learning. Yet, most deep generative models do not address the question of identifiability, and thus fail to deliver on the promise of the recovery of the true latent sources that generate the observations. Recent work proposed identifiable generative modelling using variational autoencoders (iVAE) with a theory of identifiability. Due to the intractablity of KL divergence between variational approximate posterior and the true posterior, however, iVAE has to maximize the evidence lower bound (ELBO) of the marginal likelihood, leading to suboptimal solutions in both theory and practice. In contrast, we propose an identifiable framework for estimating latent representations using a flow-based model (iFlow). Our approach directly maximizes the marginal likelihood, allowing for theoretical guarantees on identifiability, thereby dispensing with variational approximations. We derive its optimization objective in analytical form, making it possible to train iFlow in an end-to-end manner. Simulations on synthetic data validate the correctness and effectiveness of our proposed method and demonstrate its practical advantages over other existing methods. | 1accept
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Title: Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network. Abstract: One of the biggest issues in deep learning theory is the generalization ability of networks with huge model size.
The classical learning theory suggests that overparameterized models cause overfitting.
However, practically used large deep models avoid overfitting, which is not well explained by the classical approaches.
To resolve this issue, several attempts have been made.
Among them, the compression based bound is one of the promising approaches.
However, the compression based bound can be applied only to a compressed network, and it is not applicable to the non-compressed original network.
In this paper, we give a unified frame-work that can convert compression based bounds to those for non-compressed original networks.
The bound gives even better rate than the one for the compressed network by improving the bias term.
By establishing the unified frame-work, we can obtain a data dependent generalization error bound which gives a tighter evaluation than the data independent ones.
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Title: Unsupervised Video-to-Video Translation via Self-Supervised Learning. Abstract: Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent. In this work, we propose a novel unsupervised video-to-video translation model. Our model decomposes the style and the content, uses specialized encoder-decoder structure and propagates the inter-frame information through bidirectional recurrent neural network (RNN) units. The style-content decomposition mechanism enables us to achieve long-term style-consistent video translation results as well as provides us with a good interface for modality flexible translation. In addition, by changing the input frames and style codes incorporated in our translation, we propose a video interpolation loss, which captures temporal information within the sequence to train our building blocks in a self-supervised manner. Our model can produce photo-realistic, spatio-temporal consistent translated videos in a multimodal way. Subjective and objective experimental results validate the superiority of our model over the existing methods. | 2withdrawn
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Title: Permutation Compressors for Provably Faster Distributed Nonconvex Optimization. Abstract: In this work we study the MARINA method of Gorbunov et al (ICML, 2021) -- the current state-of-the-art distributed non-convex optimization method in terms of theoretical communication complexity. Theoretical superiority of this method can be largely attributed to two sources: a carefully engineered biased stochastic gradient estimator, which leads to a reduction in the number of communication rounds, and the reliance on
{\em independent} stochastic communication compression, which leads to a reduction in the number of transmitted bits within each communication round. In this paper we i) extend the theory of MARINA to support a much wider class of potentially {\em correlated} compressors, extending the reach of the method beyond the classical independent compressors setting, ii) show that a new quantity, for which we coin the name {\em Hessian variance}, allows us to significantly refine the original analysis of MARINA without any additional assumptions, and iii) identify a special class of correlated compressors based on the idea of {\em random permutations}, for which we coin the term Perm$K$, the use of which leads to up to $O(\sqrt{n})$ (resp. $O(1 + d/\sqrt{n})$) improvement in the theoretical communication complexity of MARINA in the low Hessian variance regime when $d\geq n$ (resp. $d \leq n$), where $n$ is the number of workers and $d$ is the number of parameters describing the model we are learning. We corroborate our theoretical results with carefully engineered synthetic experiments with minimizing the average of nonconvex quadratics, and on autoencoder training with the MNIST dataset. | 1accept
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Title: Dense Morphological Network: An Universal Function Approximator. Abstract: Artificial neural networks are built on the basic operation of linear combination and non-linear activation function. Theoretically this structure can approximate any continuous function with three layer architecture. But in practice learning the parameters of such network can be hard. Also the choice of activation function can greatly impact the performance of the network. In this paper we are proposing to replace the basic linear combination operation with non-linear operations that do away with the need of additional non-linear activation function. To this end we are proposing the use of elementary morphological operations (dilation and erosion) as the basic operation in neurons. We show that these networks (Denoted as Morph-Net) with morphological operations can approximate any smooth function requiring less number of parameters than what is necessary for normal neural networks. The results show that our network perform favorably when compared with similar structured network. We have carried out our experiments on MNIST, Fashion-MNIST, CIFAR10 and CIFAR100. | 0reject
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Title: Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks. Abstract: Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards. However, it is very difficult to achieve similar success without relying on expert demonstrations. Recent works on self-imitation learning showed that imitating the agent's own past good experience could indirectly drive exploration in some environments, but these methods often lead to sub-optimal and myopic behavior. To address this issue, we argue that exploration in diverse directions by imitating diverse trajectories, instead of focusing on limited good trajectories, is more desirable for the hard-exploration tasks. We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards. Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various hard-exploration tasks with local optima. In particular, we report a state-of-the-art score of more than 20,000 points on Montezumas Revenge without using expert demonstrations or resetting to arbitrary states. | 0reject
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Title: Avoiding Catastrophic States with Intrinsic Fear. Abstract: Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward shaping that accelerates deep reinforcement learning and guards oscillating policies against periodic catastrophes. Our approach incorporates a second model trained via supervised learning to predict the probability of imminent catastrophe. This score acts as a penalty on the Q-learning objective. Our theoretical analysis demonstrates that the perturbed objective yields the same average return under strong assumptions and an $\epsilon$-close average return under weaker assumptions. Our analysis also shows robustness to classification errors. Equipped with intrinsic fear, our DQNs solve the toy environments and improve on the Atari games Seaquest, Asteroids, and Freeway. | 0reject
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Title: How Benign is Benign Overfitting ?. Abstract: We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models. When trained with SGD, deep neural networks essentially achieve zero training error, even in the presence of label noise, while also exhibiting good generalization on natural test data, something referred to as benign overfitting (Bartlett et al., 2020; Chatterji & Long, 2020). However, these models are vulnerable to adversarial attacks. We identify label noise as one of the causes for adversarial vulnerability, and provide theoretical and empirical evidence in support of this. Surprisingly, we find several instances of label noise in datasets such as MNIST and CIFAR, and that robustly trained models incur training error on some of these, i.e. they don’t fit the noise. However, removing noisy labels alone does not suffice to achieve adversarial robustness. We conjecture that in part sub-optimal representation learning is also responsible for adversarial vulnerability. By means of simple theoretical setups, we show how the choice of representation can drastically affect adversarial robustness. | 1accept
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Title: Neural Compositional Denotational Semantics for Question Answering. Abstract: Answering compositional questions requiring multi-step reasoning is challenging for current models. We introduce an end-to-end differentiable model for interpreting questions, which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a knowledge graph, together with a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituents, culminating in a grounding for the complete sentence which is an answer to the question. For example, to interpret ‘not green’, the model will represent ‘green’ as a set of entities, ‘not’ as a trainable ungrounded vector, and then use this vector to parametrize a composition function to perform a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent. We show the model can learn to represent a variety of challenging semantic operators, such as quantifiers, negation, disjunctions and composed relations on a synthetic question answering task. The model also generalizes well to longer sentences than seen in its training data, in contrast to LSTM and RelNet baselines. We will release our code. | 0reject
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Title: Correcting experience replay for multi-agent communication. Abstract: We consider the problem of learning to communicate using multi-agent reinforcement learning (MARL). A common approach is to learn off-policy, using data sampled from a replay buffer. However, messages received in the past may not accurately reflect the current communication policy of each agent, and this complicates learning. We therefore introduce a 'communication correction' which accounts for the non-stationarity of observed communication induced by multi-agent learning. It works by relabelling the received message to make it likely under the communicator's current policy, and thus be a better reflection of the receiver's current environment. To account for cases in which agents are both senders and receivers, we introduce an ordered relabelling scheme. Our correction is computationally efficient and can be integrated with a range of off-policy algorithms. We find in our experiments that it substantially improves the ability of communicating MARL systems to learn across a variety of cooperative and competitive tasks. | 1accept
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Title: Neural Task Graph Execution. Abstract: In order to develop a scalable multi-task reinforcement learning (RL) agent that is able to execute many complex tasks, this paper introduces a new RL problem where the agent is required to execute a given task graph which describes a set of subtasks and dependencies among them. Unlike existing approaches which explicitly describe what the agent should do, our problem only describes properties of subtasks and relationships between them, which requires the agent to perform a complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural task graph solver (NTS) which encodes the task graph using a recursive neural network. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy that performs back-propagation over a differentiable form of the task graph to compute the influence of each subtask on the other subtasks. Our NTS is pre-trained to approximate the proposed gradient-based policy and fine-tuned through actor-critic method. The experimental results on a 2D visual domain show that our method to pre-train from the gradient-based policy significantly improves the performance of NTS. We also demonstrate that our agent can perform a complex reasoning to find the optimal way of executing the task graph and generalize well to unseen task graphs. In addition, we compare our agent with a Monte-Carlo Tree Search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of our agent can be further improved by combining with MCTS. The demo video is available at https://youtu.be/e_ZXVS5VutM. | 0reject
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Title: The Foes of Neural Network’s Data Efficiency Among Unnecessary Input Dimensions. Abstract: Input dimensions are unnecessary for a given task when the target function can be expressed without such dimensions. Object's background in image recognition or redundant sentences in text classification are examples of unnecessary dimensions that are often present in datasets. Deep neural networks achieve remarkable generalization performance despite the presence of unnecessary dimensions but it is unclear whether these dimensions negatively affect neural networks or how. In this paper, we investigate the impact of unnecessary input dimensions on one of the central issues of machine learning: the number of training examples needed to achieve high generalization performance, which we refer to as the network's data efficiency. In a series of analyses with multi-layer perceptrons and deep convolutional neural networks, we show that the network's data efficiency depends on whether the unnecessary dimensions are \emph{task-unrelated} or \emph{task-related} (unnecessary due to redundancy). Namely, we demonstrate that increasing the number of \emph{task-unrelated} dimensions leads to an incorrect inductive bias and as a result degrade the data efficiency, while increasing the number of \emph{task-related} dimensions helps to alleviate the negative impact of the \emph{task-unrelated} dimensions. These results highlight the need for mechanisms that remove \emph{task-unrelated} dimensions, such as crops or foveation for image classification, to enable data efficiency gains.
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Title: Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?. Abstract: Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using the graph structures based on the relational inductive bias (homophily assumption). Though GNNs are believed to outperform NNs in real-world tasks, performance advantages of GNNs over graph-agnostic NNs seem not generally satisfactory. Heterophily has been considered as a main cause and numerous works have been put forward to address it. In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation. Then, we propose new metrics based on a similarity matrix which considers the influence of both graph structure and input features on GNNs. The metrics demonstrate advantages over the commonly used homophily metrics in tests on synthetic graphs. From the metrics and the observations, we find that some cases of harmful heterophily can be addressed by diversification operation. By using this fact and knowledge of filterbanks, we propose the Adaptive Channel Mixing (ACM) framework to adaptively exploit aggregation, diversification and identity channels in each GNN layer, in order to address harmful heterophily. We validate the ACM-augmented baselines with 10 real-world node classification tasks. They consistently achieve significant performance gain and exceed the state-of-the-art GNNs on most of the tasks without incurring significant computational burden. | 0reject
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Title: Neural Networks Playing Dough: Investigating Deep Cognition With a Gradient-Based Adversarial Attack. Abstract: Discovering adversarial examples has shaken our trust in the reliability of deep learning. Even though brilliant works have been devoted to understanding and fixing this vulnerability, fundamental questions (e.g. the mysterious generalization of adversarial examples across models and training sets) remain unanswered. This paper tests the hypothesis that it is not the neural networks failing in learning that causes adversarial vulnerability, but their different perception of the presented data. And therefore, adversarial examples should be semantic-sensitive signals which can provide us with an exceptional opening to understanding neural network learning. To investigate this hypothesis, I performed a gradient-based attack on fully connected feed-forward and convolutional neural networks, instructing them to minimally evolve controlled inputs into adversarial examples for all the classes of the MNIST and Fashion-MNIST datasets. Then I abstracted adversarial perturbations from these examples. The perturbations unveiled vivid and recurring visual structures, unique to each class and persistent over parameters of abstraction methods, model architectures, and training configurations. Furthermore, these patterns proved to be explainable and derivable from the corresponding dataset. This finding explains the generalizability of adversarial examples by, semantically, tying them to the datasets. In conclusion, this experiment not only resists interpretation of adversarial examples as deep learning failure but on the contrary, demystifies them in the form of supporting evidence for the authentic learning capacity of neural networks. | 0reject
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Title: Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks. Abstract: It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally non-linear behavior nearby input examples. Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial perturbations, which introduces the globally linear behavior in-between training examples. However, in previous work, the mixup-trained models only passively defend adversarial attacks in inference by directly classifying the inputs, where the induced global linearity is not well exploited. Namely, since the locality of the adversarial perturbations, it would be more efficient to actively break the locality via the globality of the model predictions. Inspired by simple geometric intuition, we develop an inference principle, named mixup inference (MI), for mixup-trained models. MI mixups the input with other random clean samples, which can shrink and transfer the equivalent perturbation if the input is adversarial. Our experiments on CIFAR-10 and CIFAR-100 demonstrate that MI can further improve the adversarial robustness for the models trained by mixup and its variants. | 1accept
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Title: Maximum Categorical Cross Entropy (MCCE): A noise-robust alternative loss function to mitigate racial bias in Convolutional Neural Networks (CNNs) by reducing overfitting. Abstract: Categorical Cross Entropy (CCE) is the most commonly used loss function in deep neural networks such as Convolutional Neural Networks (CNNs) for multi-class classification problems. In spite of the fact that CCE is highly susceptible to noise; CNN models trained without accounting for the unique noise characteristics of the input data, or noise introduced during model training, invariably suffer from overfitting affecting model generalizability. The lack of generalizability becomes especially apparent in the context of ethnicity/racial image classification problems encountered in the domain of computer vision. One such problem is the unintended discriminatory racial bias that CNN models trained using CCE fail to adequately address. In other words, CNN models trained using CCE offer a skewed representation of classification performance favoring lighter skin tones.
In this paper, we propose and empirically validate a novel noise-robust extension to the existing CCE loss function called Maximum Categorical Cross-Entropy (MCCE), which utilizes CCE loss and a novel reconstruction loss, calculated using the Maximum Entropy (ME) measures of the convolutional kernel weights and input training dataset. We compare the use of MCCE with CCE-trained models on two benchmarking datasets, colorFERET and UTKFace, using a Residual Network (ResNet) CNN architecture. MCCE-trained models reduce overfitting by 5.85% and 4.3% on colorFERET and UTKFace datasets respectively. In cross-validation testing, MCCE-trained models outperform CCE-trained models by 8.8% and 25.16% on the colorFERET and UTKFace datasets respectively. MCCE addresses and mitigates the persistent problem of inadvertent racial bias for facial recognition problems in the domain of computer vision. | 0reject
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Title: Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?. Abstract: Convolutional neural networks often dominate fully-connected counterparts in generalization performance, especially on image classification tasks. This is often explained in terms of \textquotedblleft better inductive bias.\textquotedblright\ However, this has not been made mathematically rigorous, and the hurdle is that the sufficiently wide fully-connected net can always simulate the convolutional net. Thus the training algorithm plays a role. The current work describes a natural task on which a provable sample complexity gap can be shown, for standard training algorithms. We construct a single natural distribution on $\mathbb{R}^d\times\{\pm 1\}$ on which any orthogonal-invariant algorithm (i.e. fully-connected networks trained with most gradient-based methods from gaussian initialization) requires $\Omega(d^2)$ samples to generalize while $O(1)$ samples suffice for convolutional architectures. Furthermore, we demonstrate a single target function, learning which on all possible distributions leads to an $O(1)$ vs $\Omega(d^2/\varepsilon)$ gap. The proof relies on the fact that SGD on fully-connected network is orthogonal equivariant. Similar results are achieved for $\ell_2$ regression and adaptive training algorithms, e.g. Adam and AdaGrad, which are only permutation equivariant. | 1accept
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Title: M^3RL: Mind-aware Multi-agent Management Reinforcement Learning. Abstract: Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly learning a policy for each agent to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not want to do. For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together. The objective of the manager is to maximize the overall productivity as well as minimize payments made to the workers for ad-hoc worker teaming. To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL), which consists of agent modeling and policy learning. We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents. The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation. | 1accept
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Title: A Rate-Distortion Approach to Domain Generalization. Abstract: Domain generalization deals with the difference in the distribution between the training and testing datasets, i.e., the domain shift problem, by extracting domain-invariant features. In this paper, we propose an information-theoretic approach for domain generalization. We first establish the domain transformation model, mapping a domain-free latent image into a domain. Then, we cast the domain generalization as a rate-distortion problem, and use the information bottleneck penalty to measure how well the domain-free latent image is reconstructed from a compressed representation of a domain-specific image compared to its direct prediction from the domain-specific image itself. We prove that the information bottleneck penalty guarantees that domain-invariant features can be learned. Lastly, we draw links of our proposed method with self-supervised contrastive learning without negative data pairs. Our empirical study on two different tasks verifies the improvement over recent baselines.
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