summaries: The authors propose a hyperparameter optimization algorithm in meta-learning, where parameters w/o being involved in the inner loop optimization are treated as hyperparameters. The proposed algorithms approximate the second-order hypergradients via knowledge distillation. They further evaluate the effectiveness on tinyImageNet and CIFAR100.

summaries: The paper proposes a novel hyperparameter optimization algorithm in meta-learning to overcome previous limitations of only being able to see a longer horizon and not being scalable to high dimensional hyperparameters. In particular, the authors propose to distill the hypergradient second-order term into a one-step Jacobioan-vector product. The authors show that, with their approximated algorithm, it is possible to perform hyperparameter optimization for higher dimensional hyperparameters and longer horizon length even in an online setting. Empirically, the authors show the advantages of the proposed approach in several benchmark datasets.

summaries: This paper develops a practical gradient-based hyperparameter optimization method, HyperDistill, that meets the following criteria   a) scalability in hyperparameter dimension and memory constraints,    b) accuracy (hyper-gradient update terms do not depend on only the last step of gradient updates)   c) applicability to the online setting.  The main difficulty lies in estimating the gradient of the weights with respect to the hyperparameters. The authors do so by approximating it as a single Jacobian-vector product, using a "distilled" weight and dataset pair.  Experimentally, HyperDistill achieves better validation losses and higher quality true hypergradient estimates than a variety of recent, relevant baselines, and does in an efficient fashion.

summaries: This paper proposes a new online hyperparameter optimization algorithm. Applying meta-learning for hyperparameter optimization is reasonable and interesting but suffers from the second-order gradient computation. Implicit Function Theorem and Unrolled differentiation can be used to approximate the meta-gradient but also causes various problems. In this paper, the authors propose an interesting method by approximating the second-order approximation with knowledge distillation.

abstractText: Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters. Although such hyperparameters can be optimized using the existing gradientbased hyperparameter optimization (HO) methods, they suffer from the following issues. Unrolled differentiation methods do not scale well to high-dimensional hyperparameters or horizon length , Implicit Function Theorem (IFT) based methods are restrictive for online optimization, and short horizon approximations suffer from short horizon bias. In this work, we propose a novel HO method that can overcome these limitations, by approximating the second-order term with knowledge distillation. Specifically, we parameterize a single Jacobian-vector product (JVP) for each HO step and minimize the distance from the true second-order term. Our method allows online optimization and also is scalable to the hyperparameter dimension and the horizon length. We demonstrate the effectiveness of our method on two different meta-learning methods and three benchmark datasets.

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heading: 1 INTRODUCTION

text:
Meta-learning (Schmidhuber, 1987; Thrun & Pratt, 1998) aims to learn a learning process itself over a task distribution. Many gradient-based meta-learning approaches assume a set of parameters that do not participate in inner-optimization (Lee & Choi, 2018; Flennerhag et al., 2020; Raghu et al., 2020) which can be seen as hyperparameters. Those hyperparameters are important in helping the inner-learner converge faster and generalize better. As they are usually very high-dimensional such as element-wise learning rates (Li et al., 2017), we cannot meta-learn them with simple hyperparameter optimization (HO) techniques such as random search (Bergstra & Bengio, 2012) or Bayesian optimization (Snoek et al., 2012) due to the too extensive search space.
In this case, we can use gradient-based HO methods that can directly optimize the highdimensional hyperparameters by minimizing the validation loss w.r.t. the hyperparameters (Bengio, 2000). Due to the expensive computational cost of evaluating the hypergradients (i.e. the gradient w.r.t. the hyperparameters), there has been a lot of efforts to improve the effectiveness and the efficiency of the algorithms.
However, unfortunately, none of the existing algorithms satisfy the following criteria at the same time that should be met for their practical use: 1) scalable to hyperparameter dimension, 2) online optimization, 3) memory-efficient, 4) avoid short-horizon bias. Please See Table 1 for the comparison of existing gradient-based HO algorithms in the above four criteria.
Forward-Mode Differentiation (FMD) (Franceschi et al., 2017) in Table 1 is an algorithm that forward-propagates Jacobians (i.e. derivatives of the update function) from the first to the last step, which is analogous to real-time recurrent learning (RTRL) (Williams & Zipser, 1989) in recurrent neural networks. FMD allows online optimization (i.e. update hyperparameters every inner-step) with the intermediate Jacobians and also computes the hypergradients over the entire horizon. However, a critical limitation is that the time and space complexity linearly increases w.r.t. the hyperparameter dimension. Thus, we cannot use FMD for solving many practical meta-learning problems that come with millions of hyperparameters, which is the main problem we tackle in this paper.
Secondly, Reverse-Mode Differentiation (RMD) (Maclaurin et al., 2015) back-propagates the Jacobian-vector products (JVPs) from the last to the initial step, which is structurally identical to backprop through time (BPTT) (Werbos, 1990). RMD is scalable to the hyperparameter dimension, but the space complexity linearly increases w.r.t. the horizon length (i.e., the number of innergradient steps used to compute the hypergradient). It is possible to reduce the memory burden by checkpointing some of the previous weights and further interpolating between the weights to approximate the trajectory (Fu et al., 2016). However, RMD and its variants are not scalable for online optimization. This is because they do not retain the intermediate Jacobians unlike FMD and thus need to recompute the whole second-order term for every online HO step.
Thirdly, algorithms based on Implicit Function Theorem (IFT) are applicable to high-dimensional HO (Bengio, 2000; Pedregosa, 2016). Under the assumption that the main model parameters have arrived at convergence, the best-response Jacobian, i.e. how the converged model parameters change w.r.t. the hyperparameters, can be expressed by only the information available at the last step, such as the inverse of Hessian at convergence. Thus, we do not have to explicitly unroll the previous update steps. Due to the heavy cost of computing inverse-Hessian-vector product, Lorraine et al. (2020a) propose to approximate it by an iterative method, which works well for high-dimensional HO problems. However, still it is not straightforward to use the method for online optimization because of the convergence assumption. That is, computing hypergradients before convergence does not guarantee the quality of the hypergradients.
To our knowledge, the short horizon approximation such as one-step lookahead (1-step in Table 1) (Luketina et al., 2016) is the only existing method that fully supports online optimization, while being scalable to the hyperparameter dimension at the same time. It computes hypergradients only over a single update step and ignores the past learning trajectory, which is computationally efficient as only a single JVP is computed per each online HO step. However, this approximation suffers from the short horizon bias (Wu et al., 2018) by definition.
In this paper, we propose a novel HO algorithm that can simultaneously satisfy all the aforementioned criteria for practical HO. The key idea is to distill the entire second-order term into a single JVP. As a result, we only need to compute the single JVP for each online HO step, and at the same time the distilled JVP can consider longer horizons than short horizon approximations such as one-step lookahead or first-order method. We summarize the contribution of this paper as follows:
• We propose HyperDistill, a novel HO algorithm that satisfies the aforementioned four criteria for practical HO, each of which is crucial for a HO algorithm to be applied to the current meta-learning frameworks.
• We show how to efficiently distill the hypergradient second-order term into a single JVP. • We empirically demonstrate that our algorithm converges faster and provides better gener-
alization performance at convergence, with three recent meta-learning models and on two benchmark image datasets.

heading: 2 RELATED WORK

text:
Hyperparameter optimization When the hyperparameter dimension is small (e.g. less than 100), random search (Bergstra & Bengio, 2012) or Bayesian optimization (Snoek et al., 2012) works well. However, when the hyperparameter is high-dimensional, gradient-based HO is often preferred since random or Bayesian search could become infeasible. One of the most well known methods for gradient-based HO are based on Implicit Function Theorem which compute or approximate the inverse Hessian only at convergence. Bengio (2000) computes the exact inverse Hessian, and Luketina et al. (2016) approximate the inverse Hessian with the identity matrix, which is identical to the onestep lookahead approximation. Pedregosa (2016) approximates the inverse Hessian with conjugate gradients (CG) method. Lorraine et al. (2020b) propose Neumann approximation, which is numerically more stable than CG approximation. On the other hand, Domke (2012) proposes unrolled differentiation for solving bi-level optimization, and Shaban et al. (2019) analyzes the truncated unrolled differentiation, which is computationally more efficient. Unrolled diffrentiation can be further categorized into forward (FMD) and reverse mode (RMD) (Franceschi et al., 2017). FMD is more suitable for optimizing low-dimensional hyperparamters (Im et al., 2021; Micaelli & Storkey, 2020), but RMD is more scalable to the hyperparameter dimension. Maclaurin et al. (2015) proposes a more memory-efficient RMD, which reverses the SGD trajectory with momentum. Fu et al. (2016)
further reduce memory burden of RMD by approximating the learning trajectory with linear interpolation. Luketina et al. (2016) can also be understood as a short horizon approximation of RMD for online optimization. Our method also supports online optimization, but the critical difference is that our algorithm can alleviate the short horizon bias (Wu et al., 2018). RMD is basically a type of backpropagation and it is available in deep learning libraries (Grefenstette et al., 2020).
Meta-learning Meta-learning (Schmidhuber, 1987; Thrun & Pratt, 1998) aims to learn a model that generalizes over a distribution of tasks (Vinyals et al., 2016; Ravi & Larochelle, 2017). While there exists a variety of approaches, in this paper we focus on gradient-based meta-learning (Finn et al., 2017b), especially the methods with high-dimensional hyperparameters that do not participate in inner-optimization. For instance, there have been many attempts to precondition the innergradients for faster inner-optimization, either by warping the parameter space with every pair of consecutive layers interleaved with a warp layer (Lee & Choi, 2018; Flennerhag et al., 2020) or directly modulating the inner-gradients with diagonal (Li et al., 2017) or block-diagonal matrix (Park & Oliva, 2019). Perturbation function is another form of hyperparameters that help the inner-learner generalize better (Lee et al., 2020; Ryu et al., 2020; Tseng et al., 2020). It is also possible to let the whole feature extractor be hyperparameters and only adapt the last fully-connected layer (Raghu et al., 2020). On the other hand, some of the meta-learning literatures do not assume a task distribution, but tune their hyperparameters with a holdouot validation set, similarly to the conventional HO setting. In this case, the one-step lookahead method (Luketina et al., 2016) is mostly used for scalable online HO, in context of domain generalization (Li et al., 2018), handling class imbalance (Ren et al., 2018; Shu et al., 2019), gradient-based neural architecture search (Liu et al., 2019), and coefficient of norm-based regularizer (Balaji et al., 2018). Although we mainly focus on metalearning setting in this work, whose goal is to transfer knowledge through a task distribution, it is straightforward to apply our method to conventional HO problems.

heading: 3 BACKGROUND

text: In this section, we first introduce RMD and its approximations for efficient computation. We then introduce our novel algorithm that supports high-dimensional online HO over the entire horizon.

heading: 3.1 HYPERPARAMETER UNROLLED DIFFERENTIATION

text:
We first introduce notations. Throughout this paper, we will specifiy w as weight and λ as hyperparameter. The series of weights w0, w1, w2 . . . , wT evolve with the update function wt = Φ(wt−1, λ;Dt) over steps t = 1, . . . , T . The function Φ takes the previous weight wt−1 and the hyperparameter λ as inputs and its form depends on the current mini-batch Dt. Note that w1, w2, . . . , wT are functions w.r.t. the hyperparameter λ. The question is how to find a good hyperparameter λ that yields a good response wT at the last step. In gradient-based HO, we find the optimal λ by minimizing the validation loss Lval as a function of λ.
min λ Lval(wT (λ), λ) (1)
Note that we let the loss function Lval(·, ·) itself be modulated by λ for generality. According to the chain rule, the hypergradient is decomposed into
dLval(wT , λ) dλ = ∂Lval(wT , λ)
∂λ︸ ︷︷ ︸ gFOT : First-order term
+ ∂Lval(wT , λ)
∂wT dwT dλ︸ ︷︷ ︸
gSOT : Second-order term
(2)
On the right hand side, the first-order (FO) term gFOT directly computes the gradient w.r.t λ by fixing wT . The second-order (SO) term gSOT computes the indirect effect of λ through the response wT . αT = ∂Lval(wT ,λ)
∂wT can be easily computed similarly to gFOT , but the response Jacobian dwT dλ is more
computationally challenging as it is unrolled into the following form.
dwT dλ = T∑ t=1
( T∏
s=t+1
As ) Bt, where As = ∂Φ(ws−1, λ;Ds)
∂ws−1 , Bt =
∂Φ(wt−1, λ;Dt)
∂λ (3)
Algorithm 1 Reverse-HG (RMD) 1: Input: The last weight wT and all the previous
weights w0, . . . , wT−1. 2: Output: Hypergradient gFO + gSO. 3: α← ∂L(wT ,λ)
∂wT , gFO ← ∂L(wT ,λ) ∂λ , gSO ← 0
4: for t = T downto 1 do 5: gSO ← gSO + αBt 6: α← αAt 7: end for 8: return gFO + gSO
Algorithm 2 DrMAD (Fu et al., 2016) 1: Input: The last weight wT and the initial weight w0. 2: Output: Approximated hypergradient gFO + gSO. 3: α← ∂L(wT ,λ)
∂wT , gFO ← ∂L(wT ,λ) ∂λ , gSO ← 0
4: for t = T downto 1 do 5: ŵt−1 ← ( 1− t−1
T
) w0 + t−1 T wT
6: gSO ← gSO + αB̂t 7: α← αÂt 8: end for 9: return gFO + gSO
Eq. (3) involves the Jacobians {A} and {B} at the intermediate steps. Evaluating them or their vector products are computationally expensive in terms of either time (FMD) or space (FMD, RMD) (Franceschi et al., 2017). Therefore, how to approximate Eq. (3) is the key to developing an efficient and effective HO algorithm.

heading: 3.2 REVERSE-MODE DIFFERENTIATION AND ITS APPROXIMATIONS

text:
Basically, RMD is structurally analogous to backpropagation through time (BPTT) (Werbos, 1990). In RMD, we first obtain αT = ∂Lval(wT ,λ) ∂wT
and back-propagate A and B from the last to the first step in the form of JVPs (See Algorithm 1). Whereas RMD is much faster than FMD as we only need to compute one or two JVPs per each step, it usually requires to store all the previous weights w0, . . . , wT−1 to compute the previous-step JVPs, unless we consider reversible training with momentum optimizer (Maclaurin et al., 2015). Therefore, when w is high-dimensional, RMD is only applicable to short-horizon problems such as few-shot learning (e.g. T = 5 in Finn et al. (2017a)).
Trajectory approximation. Instead of storing all the previous weights for computingA andB, we can approximate the learning trajectory by linearly interpolating between the last weight wT and the initial weight w0. Algorithm 2 illustrates the procedure called DrMAD (Fu et al., 2016), where each intermediate weight wt is approximated by ŵt = ( 1− tT ) w0 + t T wT for t = 1, . . . , T − 1. A and B are also approximated by Âs = ∂Φ(ŵs−1,λ;Ds)
∂ŵs−1 and B̂t = ∂Φ(ŵt−1,λ;Dt) ∂λ , respectively. However,
although DrMAD dramatically lower the space complexity, it does not reduce the number of JVPs per each hypergradient step. For each online HO step t = 1, . . . , T we need to compute 2t−1 JVPs, thus the number of total JVPs to complete a single trajectory accumulates up to ∑T t=1(2t−1) = T 2, which is definitely not scalable as an online optimization algorithm.
Short-horizon approximations. One-step lookahead approximation (Luketina et al., 2016) is currently one of the most popular high-dimensional online HO method that can avoid computing the excessive number of JVPs (Li et al., 2018; Ren et al., 2018; Shu et al., 2019; Liu et al., 2019; Balaji et al., 2018). The idea is very simple; for each online HO step we only care about the last previous step and ignore the rest of the learning trajectory for computational efficiency. Specifically, for each step t = 1, . . . , T we compute the hypergradient by viewing wt−1 as constant, which yields dwt dλ ≈ ∂wt ∂λ ∣∣ wt−1 = Bt (See Eq. (3)). Or, we may completely ignore all the second-order derivatives for computational efficiency, such that dwtdλ ≈ 0 (Flennerhag et al., 2020; Ryu et al., 2020). While those approximations enable online HO with low cost, they are intrinsically vulnerable to short-horizon bias (Wu et al., 2018) by definition.

heading: 4 APPROACH

text: We next introduce our novel online HO method based on knowledge distillation. Our method can overcome all the aforementioned limitations at the same time.

heading: 4.1 HYPERGRADIENT DISTILLATION

text:
The key idea is to distill the whole second-order term gSOt = αt ∑t i=1 (∏t j=i+1Aj ) Bi in Eq. (2) into a single JVP evaluated at a distilled weight point w and with a distilled dataset D. We denote the normalized JVP as ft(w,D) := σ(αt ∂Φ(w,λ;D) ∂λ ) with σ(x) = x ‖x‖2 . Specifically, we want to solve the following knowledge distillation problem for each online HO step t = 1, . . . , T :
π∗t , w ∗ t , D ∗ t = arg min
π,w,D ∥∥πft(w,D)− gSOt ∥∥2 (4) so that we use π∗t ft(w ∗ t , D ∗ t ) instead of g SO t . Online optimization is now feasible because for each online HO step t = 1, . . . , T we only need to compute the single JVP ft(w∗t , D ∗ t ) rather than computing 2t− 1 JVPs for RMD or DrMAD. Also, unlike short horizon approximations, the whole trajectory information is distilled into the JVP, alleviating the short horizon bias (Wu et al., 2018).
Notice that solving Eq. (4) only w.r.t. π is simply a vector projection.
π̃t(w,D) = ft(w,D) TgSOt . (5)
Then, plugging Eq. (5) into π in Eq. (4) and making use of ‖ft(w,D)‖2 = 1, we can easily convert the optimization problem Eq. (4) into the following equivalent problem (See Appendix A).
w∗t , D ∗ t = arg max
w,D π̃t(w,D), π
∗ t = π̃t(w ∗ t , D ∗ t ). (6)
w∗t and D ∗ t match the hypergradient direction and π ∗ t matches the size.
Technical challenge. However, solving Eq. (6) requires to evaluate gSOt for t = 1, . . . , T , which is tricky as gSOt is the target we aim to approximate. We next show how to roughly solve Eq. (6) even without evaluting gSOt (for w ∗ t , D ∗ t ) or by sparsely evaluating an approximation of g SO t (for π ∗ t ).

heading: 4.2 DISTILLING THE HYPERGRADIENT DIRECTION

text:
Hessian approximation. In order to circumvent the technical difficulty, we start from making the optimization objective π̃t(w,D) = ft(w,D)TgSOt in Eq. (5) simpler. We approximate g SO t as
gSOt = αt t∑ i=1  t∏ j=i+1 Aj Bi ≈ t∑ i=1 γt−iαtBi. (7)
with γ ≥ 0, which we tune on a meta-validation set. Note that Eq. (7) is yet too expensive to use for online optimization as it consists of t JVPs. We thus need further distillation, which we will explain later. Eq. (7) is simply a Hessian identity approximation. For instance, vanilla SGD with learning rate ηInner corresponds to Aj = ∇wj−1(wj−1− ηInner∇Ltrain(wj−1, λ)). Approximating the Hessian as ∇2wj−1L
train(wj−1, λ) ≈ kI , we have Aj = I − ηInner · kI ≈ (1 − ηInnerk)I = γI . Plugging Eq. (7) to Eq. (5) and letting ft(wi−1, Di) := σ(αt ∂Φ(wi−1,λ;Di) ∂λ ) = σ(αtBi), we have
π̃t(w,D) ≈ π̂t(w,D) = t∑ i=1 δt,i · ft(w,D)Tft(wi−1, Di) (8)
where δt,i = γt−i‖αtBi‖2 ≥ 0. Instead of maximizing π̃t directly, we now maximize π̂t w.r.t. w and D as a proxy objective.
Lipschitz continuity assumption. Now we are ready to see how to distill the hypergradient direction w∗t and D ∗ t without evaluating g SO t . The important observation is that the maximum of π̂t in Eq. (8) is achieved when ft(w,D) is well-aligned to the other ft(w0, D1), . . . , ft(wt−1, Dt). This intuition is directly related to the following Lipschitz continuity assumption on ft.
‖ft(w,D)− ft(wi−1, Di)‖2 ≤ K‖(w,D)− (wi−1, Di)‖X , for i = 1, . . . , t. (9) where K ≥ 0 is the Lipschitz constant. Eq. (9) captures which (w,D) can minimize ‖ft(w,D) − ft(wi−1, Di)‖2 over i = 1, . . . , t, which is equivalent to maximizing ft(w,D)Tft(wi−1, Di) since ‖f(·, ·)‖2 = 1. For the metric ‖ · ‖X , we let K2‖(w,D)‖2X = K21‖w‖22 + K22‖D‖22 where K1,K2 ≥ 0 are additional constants that we introduce for notational convenience. Taking square of the both sides of Eq. (9) and summing over all i = 1, . . . , t, we can easily derive the following lower bound of π̂t (See Appendix B).
2 t∑ i=1 δt,i −K21 t∑ i=1 δt,i‖w − wi−1‖22 −K22 t∑ i=1 δt,i‖D −Di‖22 ≤ π̂t(w,D) (10)
We now maximize this lower bound instead of directly maximizing π̂t. Interestingly, it corresponds to the following simple minimization problems for w and D.
min w t∑ i=1 δt,i‖w − wi−1‖22, min D t∑ i=1 δt,i‖D −Di‖22. (11)
Algorithm 3 HyperDistill 1: Input: γ ∈ [0, 1], initial λ, and initial φ. 2: Output: Learned hyperparameter λ. 3: for m = 1 to M do 4: if m ∈ EstimationPeriod then 5: θ ← LinearEstimation(γ, λ, φ) 6: end if 7: w0 ← φ 8: for t = 1 to T do 9: w∗t , D∗t ← Eq. (13), Eq. (14). 10: π∗t ← cγ(t; θ) in Eq. (15) 11: wt ← Φ(wt−1, λ;Dt) 12: g ← gFOt + π∗t ft(w∗t , D∗t ) 13: λ← λ− ηHyperg 14: end for 15: φ← φ− ηReptile(φ− wT ) 16: end for
Algorithm 4 LinearEstimation(γ, λ, φ) 1: Input: w0 ← φ 2: for t = 1 to T do 3: wt ← Φ(wt−1, λ;Dt) 4: end for 5: α, αT ← ∂L
val(wT ,λ) ∂wT , gSO ← 0 6: for t = T downto 1 do 7: ŵt−1 ← ( 1− t−1
T
) w0 + t−1 T wT
8: gSO ← gSO + αB̂t (Eq. (16)) 9: α← αÂt 10: s← T − t+ 1 11: w∗s , D∗s ← Eq. (17), Eq. (18) 12: vs ← αT ∂Φ(w ∗ s ,λ;D ∗ s )
∂λ
13: xs ← ‖vs‖2 · 1−γ s 1−γ , ys ← σ(vs) TgSO 14: end for 15: return (xTy)/(xTx)
Efficient sequential update. Eq. (11) tells how to determine the distilled w∗t and D∗t for each HO step t = 1, . . . , T . Since ‖αtBi‖2 is expensive to compute, we approximate as ‖αtB0‖2 ≈ ‖αtB1‖2 ≈ · · · ≈ ‖αtBt‖2 , yielding the following weighted average as the approximated solution for w∗t .
w∗t ≈ γt−1∑t i=1 γ t−i w0 + γt−2∑t i=1 γ t−i w1 + · · ·+ γ0∑t i=1 γ t−i wt−1 (12)
The following sequential update allows to efficiently evaluate Eq. (12) for each HO step. Denoting pt = (γ − γt)/(1− γt) ∈ [0, 1), we have t = 1 : w∗1 ← w0, t ≥ 2 : w∗t ← ptw∗t−1 + (1− pt)wt−1 (13) Note that the online update in Eq. (13) does not require to evaluate gSOt . It only requires to incorporate the past learning trajectory w0, w1, . . . , wt−1 through the sequential updates. Therefore, the only additional cost is the memory for storing and updating the weighted running average w∗t .
For D, we have assumed Euclidean distance metric as with w, but it is not straightforward to think of Euclidean distance between datasets. Instead, we simply interpret pt and 1 − pt as probabilities with which we proportionally subsample each dataset.
t = 1 : D∗1 ← D1, t ≥ 2 : D∗t ← SS ( D∗t−1, pt ) ∪ SS (Dt, 1− pt) (14)
where SS(D, p) denotes random SubSampling of round off(|D|p) instances fromD. There may be a better distance metric for datasets and a corresponding solution, but we leave it as a future work. See Algorithm 3 for the overall description of our algorithm, which we name as HyperDistill.
Role of γ Note that Eq. (12) tells us the role of γ as a decaying factor. The larger the γ, the longer the past learning trajectory we consider. In this sense, our method is a generalization of the one-step lookahead approximation, i.e. γ = 0, which yields w∗t = wt−1 and D ∗ t = Dt, ignoring the whole information about the past learning trajectory except the last step. γ = 0 may be too pessimistic for most of the cases, so we need to find better performing γ for each task carefully.

heading: 4.3 DISTILLING THE HYPERGRADIENT SIZE

text:
Now we need to plug the distilled w∗t and D ∗ t into π̃t(w,D) in Eq. (5) to obtain the scaling factor π∗t = ft(w ∗ t , D ∗ t )
TgSOt , for online HO steps t = 1, . . . , T . However, whereas evaluating the single JVP ft(w∗t , D ∗ t ) is tolerable, again, evaluating g SO t is misleading as it is the target we aim to approximate. Also, it is not straightforward for π∗t to apply a similar trick we used in Sec. 4.2. Linear estimator. We thus introduce a linear function cγ(t; θ) that estimates π∗t by periodically fitting θ ∈ R, the parameter of the estimator. Then for each HO step t we could use cγ(t; θ) instead of fully evaluating π∗t . Based on the observation that the form of lower bound in Eq. (10) is roughly proportional to ∑t i=1 δt,i = ∑t i=1 γ t−i ‖αtBi‖2, we conveniently set cγ(t; θ) to as follows:
cγ(t; θ) = θ · ‖vt‖2 · t∑ i=1 γt−i, where vt := αt ∂Φ(w∗t , λ;D ∗ t ) ∂λ . (15)
𝑤! "𝑤" 𝑤#"𝑤$
𝑔#%& 𝑔$%& 𝑔"%&
Forward pass
Back-prop. (DrMAD)
𝛼'
Collecting samples. We next see how to collect samples {(xs, ys)}Ts=1 for fitting the parameter θ, where xs = ‖vs‖2 · 1−γs 1−γ and ys = π ∗ s = fs(w ∗ s , D ∗ s) TgSOs = σ(vs) TgSOs . For this, we need to efficiently collect:
1) gSOs : Note that DrMAD in Algorithm 2 (line 6) sequentially back-propagates gSO for t = T, . . . , 1. The important observation is that, at step t, this incomplete second-order term gSO =∑T i=t αT ÂT ÂT−1 · · · Âi+1B̂i can be seen as the valid second-order term computed over the horizon of size s = T − t+ 1. This is because the reparameterization s = T − t+ 1 gives
gSOs = s∑ i=1 αs+(T−s)Âs+(T−s)Âs−1+(T−s) · · · Âi+1+(T−s)B̂i+(T−s) (16)
for s = 1, . . . , T , nothing but shifting the trajectory index by T−s steps so that the last step is always T . Therefore, we can efficiently obtain the valid second-order term gSOs for all s = 1, . . . , T through the single backward travel along the interpolated trajectory (See Figure 1). Each gSOs requires to compute only one or two additional JVPs. Also, as we use DrMAD instead of RMD, we only store w0 such that the memory cost is constant w.r.t. the total horizon size T .
2) vs: For computing the distilled JVP vs, we first compute the distilled w∗s and D∗s as below, similarly to Eq. (13) and (14). Denoting ps = (1− γs−1)/(1− γs), we have
s = 1 : w∗1 ← wT−1, s ≥ 2 : w∗s ← psw∗s−1 + (1− ps)wT−s (17) s = 1 : D∗1 ← DT , s ≥ 2 : D∗s ← SS ( D∗s−1, ps ) ∪ SS (DT−s+1, 1− ps) (18)
We then compute the unnormalized distilled JVP as vs = αs+(T−s) ∂Φ(w∗s ,λ;D ∗ s ) ∂λ .
Estimating θ. Now we are ready to estimate θ. For x, we have xs = ‖vs‖2 · 1−γ s
1−γ and collect x = (x1, . . . , xT ). For y, we have ys = σ(vs)TgSOs and collect y = (y1, . . . , yT ). Finally, we estimate θ = (xTy)/(xTx). See Algorithm 3 and Algorithm 4 for the details. Practically, we set EstimationPeriod in Algorithm 3 to every 50 completions of the inner-optimizations, i.e. {1, 51, 101, . . . }. Thus, the computational cost of LinearEstimation is marginal in terms of the wall-clock time (see Table 3).

heading: 5 EXPERIMENTS

text:
Baselines. We demonstrate the efficacy of our algorithm by comparing to the following baselines. 1) First-Order Approximation (FO). Computationally the most efficient HO algorithm that completely ignores the second-order term, i.e. gSO = 0. 2) One-step Look-ahead Approximation (1-step). (Luketina et al., 2016) The short-horizon approximation where only a single step is unrolled to compute each hypergradient. 3) DrMAD. (Fu et al., 2016) An approximation of RMD that linearly interpolates between the initial and the last weight to save memory (see Algorithm 2). 4) Neumann IFT (N.IFT). (Lorraine et al., 2020a) An IFT based method that approximates the inverse-Hessian-vector product by Neumann series. Note that this method supports online optimization around convergence. Specifically, among total T = 100 inner-steps, N.IFT(N,K) means for the last K steps we perform online HO each with N inversion steps. It requires total (N + 1)×K JVPs. We tune (N,K) among {(2, 25), (5, 10), (10, 5)}, roughly computing 50 JVPs per inner-opt. 5) HyperDistill. Our high-dimensional online HO algorithm based on the idea of knowledge distillation. We tune the decaying factor within γ ∈ {0.9, 0.99, 0.999, 0.9999}. The linear regression is done every 50 inner-optimization problems.
Target meta-learning models. We test on the following three meta-learning models. 1) Almost No Inner Loop (ANIL). (Raghu et al., 2020). The intuition of ANIL is that the need for task-specific adaptation diminishes when the task distribution is homogeneous. Following this intuition, based on a typical 4-layer convolutional network with 32 channels (Finn et al., 2017a), we designate the three bottom layers as the high-dimensional hyperparameter and the 4th convolutional layer and the last fully connected layer as the weight, similarly to Javed & White (2019).

heading: FO O 0 53.62±0.06 58.16±0.52 53.54±0.74 N/A

text:
2) WarpGrad. (Flennerhag et al., 2020) Secondly, we consider WarpGrad, whose goal is to metalearn non-linear warp layers that facilitate fast inner-optimization and better generalization. We use 3-layer convolutional network with 32 channels. Every layer is interleaved with two warp layers that do not participate in the inner-optimization, which is the high-dimensional hyperparameter. 3) MetaWeightNet. (Shu et al., 2019) Lastly, we consider solving the label corruption problem with MetaWeightNet, which meta-learns a small MLP taking a 1D loss as an input and output a reweighted loss. The parameter of the MLP is considered as a high-dimensional hyperparameter. Labels are independently corrupted to random classes with probability 0.4. Note that we aim to meta-learn the MLP over a task distribution and apply to diverse unseen tasks, instead of solving a single task. Also, in this meta model the direct gradient is zero, gFO = 0. In this case, π∗ in HyperDistill has a meaning of nothing but rescaling the learning rate, so we simply set π∗ = 1.
Use of Reptile. Note that for all the above meta-learning models, we meta-learn the weight initialization with Reptile (Nichol et al., 2018) as well, representing a more practical meta-learning scenario than learning from random initialization. We use the Reptile learning rate ηReptile = 1. Note that φ in Algorithm 3 and Algorithm 4 denotes the Reptile initialization parameter.
Task distribution. We consider the following two datasets. To generate each task, we randomly sample 10 classes from each dataset (5000 examples) and randomly split them into 2500 training and 2500 test examples. 1) TinyImageNet. (Le & Yang, 2015) This dataset contains 200 classes of general categories. We split them into 100, 40, and 60 classes for meta-training, meta-validation, and meta-test. Each class has 500 examples of size 64×64. 2) CIFAR100. (Krizhevsky et al., 2009) This dataset contains 100 classes of general categories. We split them into 50, 20, and 30 classes for meta-training, meta-validation, and meta-test. Each class has 500 examples of size 32× 32.
Experimental setup. Meta-training: For inner-optimization of the weights, we use SGD with momentum 0.9 and set the learning rate µInner = 0.1 for MetaWeightNet and µInner = 0.01 for the others. The number of inner-steps is T = 100 and batchsize is 100. We use random cropping and horizontal flipping as data augmentations. For the hyperparameter optimization, we also use SGD with momentum 0.9 with learning rate µHyper = 0.01 for MetaWeightNet and µHyper = 0.001 for the others, which we linearly decay toward 0 over total M = 1000 inner-optimizations. We perform parallel meta-learning with meta-batchsize set to 4. Meta-testing: We solve 500 tasks to measure average performance, with exactly the same inner-optimization setup as meta-training. We repeat this over 5 different meta-training runs and report mean and 95% confidence intervals (see Table 2).

heading: 5.1 ANALYSIS

text:
We perform the following analysis together with the WarpGrad model and CIFAR100 dataset. HyperDistill provides faster convergence and better generalization. Figure 2 shows that HyperDistill shows much faster meta-training convergence than the baselines for all the meta-learning
models and datasets we considered. We see that the convergence of offline method such as DrMAD is significantly worse than a simple first-order method, demonstrating the importance of frequent update via online optimization. HyperDistill shows significantly better convergence than FO and 1-step because it is online and at the same time alleviates the short horizon bias. As a result, Table 2 shows that the meta-test performance of HyperDistill is significantly better than the baselines, although it requires comparable number of JVPs per each inner-optimiztion. HyperDistill is a reasonable approximation of the true hypergradient. We see from Figure 3(a) that the hypergradient obtained from HyperDistill is more similar to the exact RMD than those obtained from FO and 1-step, demonstrating that HyperDistill can actually alleviate the short horizon bias. HyperDistill is even comparable to N.IFT(10, 1) that computes 11 JVPs, whereas HyperDistill computes only a single JVP. Such results indicate that the approximation we used in Eq. (7) and DrMAD in Eq. (16) are accurate enough. Figure 3(b) shows that with careful tuning of γ (e.g. 0.99), the direction of the approximated second-order term in Eq. (7) can be much more accurate than the second-order term of 1-step (γ = 0). In Figure 3(c), as HyperDistill distills such a good approximation, it can provide a better direction of the second-order term than 1-step. Although the gap may seem marginal, even N.IFT(10, 1) performs similarly, showing that matching the direction of the second-order term without unrolling the full gradient steps is inherently a challenging problem. Figure 4(a) and 4(b) show that the samples collected according to Algorithm 4 is largely linear, supporting our choice of Eq. (15). Figure 4(c) and 4(d) show that the range of fitted θ is accurate and stable, explaining why we do not have to perform the estimation frequently. Note that DrMAD approximation (Eq. (16)) is accurate (Figure 3(a) and 3(c)), helping to predict the hypergradient size. HyperDistill is compuatationally efficient. Figure 4(e) shows the superior computational efficiency of HyperDistill in terms of the trade-off between meta-test performance and the amount of JVP computations. Note that wall-clock time is roughly proportional to the number of JVPs per inner-optimization. In Appendix F, we can see that the actual increase in memory cost and wallcock time is very marginal compared to 1-step approximation.

heading: 6 CONCLUSION

text: In this work, we proposed a novel HO method, HyperDistill, that can optimize high-dimensional hyperparameters in an online manner. It was done by approximating the exact second-order term with knowledge distillation. We demonstrated that HyperDistill provides faster meta-convergence and better generalization performance based on realistic meta-learning methods and datasets. We also verified that it is thanks to the accurate approximations we proposed.

heading: A DERIVATION OF EQUATION (6)

text:
Let f := ft(w,D) and g := gSOt for notational simplicity. Note that ‖f‖ = 1. Then,
π̃t(w,D) = arg min π
‖πf − g‖
= arg min π
‖πf − g‖2
= arg min π
π2fTf − 2πfTg + gTg (19)
= fTg
Plugging this into π in Eq. (19) and with the assumption π̃(w,D) = fTg ≥ 0, we have
w∗, D∗ = arg min w,D (fTg)2 · fTf − 2(fTg) · fTg + gTg
= arg max w,D
(fTg)2
= arg max w,D
fTg
= arg max w,D π̃(w,D). (20)
Note that Eq. (20) results from encoding the closed-form solution π̃t(w,D) already. Therefore, the above is a joint optimization so that we do not have to repeat alternating optimizations between (w,D) and π.

heading: B DERIVATION OF EQUATION (10)

text:
Let f := ft(w,D) and fi := ft(wi−1, Di) for notational simplicity. Note that ‖f‖ = ‖f1‖ = · · · = ‖ft‖ = 1 and we are given the following t inequalities.
‖f − fi‖ ≤ K‖(w,D)− (wi−1, Di)‖X , for i = 1, . . . , t.
Taking square of both sides and multiplying δt,i,
2δt,i − δt,ifTfi ≤ K21δt,i‖w − wi−1‖2 +K22δt,i‖D −Di‖2, for i = 1, . . . , t.
Summing the t inequalities over all i = 1, . . . , t,
2 t∑ i=1 δt,i − t∑ i=1 δt,if Tfi
≤ K21 t∑ i=1 δt,i‖w − wi−1‖2 +K22 t∑ i=1 δt,i‖D −Di‖2.
Rearranging the terms,
2 t∑ i=1 δt,i −K21 t∑ i=1 δt,i‖w − wi−1‖2 −K22 t∑ i=1 δt,i‖D −Di‖2
≤ t∑ i=1 δt,if Tfi
= π̂(w,D)

heading: C META-VALIDATION PERFORMANCE

text: Figure 5 shows the meta-validation performance as the meta-training proceeds. We can see that our HyperDistill shows much faster meta-convergence and shows better generalization at convergence than the baselines, which is consistent with the meta-training convergence shown in Figure 2.

heading: D HYPER-HYPERPARAMETER ANALYSIS

text: Our algorithm, HyperDistill has a hyper-hyperparamter γ that we tune with a meta-validation set in the range {0.9, 0.99, 0.999, 0.9999}. Figure 6 shows that with all the values of γ and for all the experimental setups we consider, HyperDistill outperforms all the baselines with significant margins. This demonstrates that the performance of HyperDistill is not much sensitive to the value of γ.

heading: E MORE DETAILS OF METAWEIGHTNET EXPERIMENTS

text: We provide the additional experimental setup for the MetaWeightNet (Shu et al., 2019) experiments. We use 1− 200(ReLU)− 1 loss weighting network architecture, following the original paper. Also, we found that lower bounding the output of the weighting function with 0.1 can stabilize the training. Figure 7 shows the resultant loss weighting function learned with each algorithm. We see that the learned weighting function with HyperDistill tend to output lower values than the baselines.

heading: F COMPUTATIONAL EFFICIENCY

text: Table 3 shows the computational efficiency measured in actual memory usage and average wallclock time required to complete a single inner-optimization. We can see from the table that whereas our HyperDistill requires slightly more memory and wall-clock time than 1-step or Neumann IFT method, the additional cost is definitely tolerable considering the superior meta-test performance shown in Table 2.

heading: G SINUSOIDAL REGRESSION

text:
0 5 10 15 20 25 30 # Inner-opt. Solved
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Te st
M SE
Sinusoidal Regression FO 1-step DrMAD N.IFT (3, 5) HyperDistill
Figure 8: Meta-convergence
MSE FO 0.567±0.193 1-step 0.670±0.283 DrMAD 1.086±0.176 N.IFT 0.502±0.146 HyperDistill 0.327±0.052
Table 4: Meta-test performance.
In this section, we conduct sinusoidal experiments to demonstrate the efficacy of our method on a regression task.
Task distribution. Each task is to regress a curve sampled from the following distribution of sinusoidal functions; the amplitude and phase is sampled from U(0.1, 5) and U(0, π), respectively. The range of input is [−5, 5], and the input and output dimensions are both 1 (Finn et al., 2017a). We consider 10-shot regression problems.
Meta-model and network architecture. We set the meta-model to ANIL (Raghu et al., 2020). Given the 4-layer fully-connected ReLU network (1-100-100-100-1), the first three layers are set to the hyperparameter, and only the last layer is adapted to given tasks.
Experimental setup. For inner-optimization of the weights, we use SGD with momentum 0.9 and set the learning rate to µInner = 0.01 The number of inner-steps is T = 30. For the hyperparameter optimization, we use Adam optimizer (Kingma & Ba, 2014) with the learning rate µHyper = 0.001. The number of inner-optimizations solved per each meta-convergence is M = 30. We perform parallel meta-learning with the meta-batchsize set to 10. Meta-testing: We solve 1000 tasks to measure average mean squared error (MSE), with exactly the same inner-optimization setup as meta-training. We repeat this over 5 different meta-training runs and report mean and 95% confidence intervals (see Table 4).
Results. In Figure 8 and Table 4, we see that HyperDistill shows better meta-convergence and meta-test performance than all the baselines. Comparing to FO and 1-step baselines, we see that it is still important to consider longer horizons even in this relatively fewer-shot learning scenario. Also, DrMAD shows poor performance, demonstrating the importance of online optimization.

heading: H STANDARD LEARNING SCENARIO

text:
0 2K 4K 6K 8K 10K # Steps
0.8
1.0
1.2
1.4
1.6 1.8 Va l L os s First-stage 1-step N.IFT HyperDistill
Figure 9: Meta-train convergence 0 2K 4K 6K 8K 10K # Steps
0.45
0.5
0.55
0.6
0.65
0.7
0.75
Te st
A CC
Second-stage
1-step N.IFT HyperDistill
Figure 10: Meta-test convergence
Test ACC 1-step 70.15±1.24 N.IFT 70.85±0.63 HyperDistill 72.68±1.15
Table 5: Meta-test performance.
In this section, instead of meta-learning setting which involves some task distribution, we consider standard learning scenario where we are given only a single classification task.
Two-stage learning. We consider the following two-stage learning scenario, which is a reasonable way to cast a standard classification task into a meta-learning problem (Liu et al., 2019). In the firststage we split the whole CIFAR10 (Krizhevsky et al., 2009) training dataset into two sets with equal number of instances (each with 25,000 instances). We then use one as a training set to optimize the weight w and the other as a validation set to optimize the hyperparameter λ. In the second stage, we merge the two datasets into the original one and re-train w with it from the random initialization, while the learned hyperparameter λ in the first stage is fixed.
MetaWeightNet (Shu et al., 2019). Again, we consider MetaWeightNet which we used in the experimental section 5. We use the same network structure for the loss weighting network and the same label corruption strategy. Note that the original paper uses 1-step strategy.
Experimental setup. In the first-stage, for both weight w and hyperparameter λ, we use SGD with momentum 0.9 and set the learning rate to 0.01 The number of training steps is set to T = 10, 000. In the second-stage, as mentioned above, we reinitialize and train w with the merged dataset, while fixing λ obtained from the first stage. We repeat this two-stage process 5 times and report mean and 95% confidence intervals (see Table 5). Hyper-hyperparameters: For Neumann IFT, we compute the hypergradients (each with 5 inversion steps) for every 5 gradient steps. For HyperDistill, we set γ = 0.9.
Results. In Figure 9, 10 and Table 5, we see that HyperDistill shows much better metaconvergence and meta-test performance than all the baselines. The results demonstrate the effectiveness of our method for solving standard HO problems. Note that we cannot consider FO because there is no direct gradient, i.e. gFO = 0 for this MetaWeightNet model. Also, we do not consider the offline methods like DrMAD because now the horizon length became too long to backpropagate all the way through the learning process.

heading: I FURTHER ANALYSIS ON SHORT HORIZON BIAS

text: In this section, we demonstrate the effect of short horizon bias by showing the convergence plots with the varying decaying factor γ (see Figure 11). Note that in this controlled experiment we want to see the effect of γ only, so we fix the scaling factor as π = 1. In the right Figure 11, γ = 0 corresponds to 1-step which computes the hypergradients by unrolling only a single step, thus suffers from short horizon bias. As we increase γ, the convergence roughly improves as well, demonstrating that the short horizon bias can be alleviated by increasing γ. Also, see Figure 3(b) which shows how well the different values of γ can recover the true hypergradients. The best performing γ seems 0.99 in terms of the cosine similarity to the true hypergradients.

heading: J EXPERIMENTS WITH FOMAML

text:
0 200 400 600 800 1K # Inner-opt. Solved
1.2 1.25
1.3 1.35
1.4 1.45
1.5 1.55
Te st
L os
s WarpGrad / CIFAR100
FO 1-step HyperDistill
Figure 12: Meta-train convergence 0 200 400 600 800 1K # Inner-opt. Solved
44
46
48
50
52
54
Te st
A CC
WarpGrad / CIFAR100
Figure 13: Meta-val. convergence
Test ACC FO 45.01±1.29 1-step 46.03±0.59 HyperDistill 51.43±0.98
Table 6: Meta-test performance.
In order to demonstrate that our HyperDistill works well with other meta-learning algorithms than Reptile (Nichol et al., 2018), we consider first-order MAML (FOMAML) (Finn et al., 2017b). Note that we need first-order approximation of MAML because the original MAML with second order derivative is too expensive with the long horizon T = 100. We can see from Figure 12 and Figure 13 that our method provides much faster and better meta-convergence than FO and 1-step. As a result, in Table 6 our HyperDistill achieves significantly better meta-test performance than the baselines. Note that we do not report the performance of DrMAD and Neumann IFT becuase they fail to meta-converge with FOMAML.
Note that the performance with FOMAML is much worse than with Reptile, which is well known results from the previous literature (Flennerhag et al., 2019; Shin et al., 2021). This is because FOMAML ignores the whole learning process except the very last step’s gradient information. This becomes more critical with longer horizons as the last step gradient becomes arbitrary uninformative to the initialization. We thus recommend using Reptile instead of FOMAML.