  How can we enable machines to make sense of the world, and become better at
learning? To approach this goal, I believe viewing intelligence in terms of
many integral aspects, and also a universal two-term tradeoff between task
performance and complexity, provides two feasible perspectives. In this thesis,
I address several key questions in some aspects of intelligence, and study the
phase transitions in the two-term tradeoff, using strategies and tools from
physics and information. Firstly, how can we make the learning models more
flexible and efficient, so that agents can learn quickly with fewer examples?
Inspired by how physicists model the world, we introduce a paradigm and an AI
Physicist agent for simultaneously learning many small specialized models
(theories) and the domain they are accurate, which can then be simplified,
unified and stored, facilitating few-shot learning in a continual way.
Secondly, for representation learning, when can we learn a good representation,
and how does learning depend on the structure of the dataset? We approach this
question by studying phase transitions when tuning the tradeoff hyperparameter.
In the information bottleneck, we theoretically show that these phase
transitions are predictable and reveal structure in the relationships between
the data, the model, the learned representation and the loss landscape.
Thirdly, how can agents discover causality from observations? We address part
of this question by introducing an algorithm that combines prediction and
minimizing information from the input, for exploratory causal discovery from
observational time series. Fourthly, to make models more robust to label noise,
we introduce Rank Pruning, a robust algorithm for classification with noisy
labels. I believe that building on the work of my thesis we will be one step
closer to enable more intelligent machines that can make sense of the world.

Machine Learning;Data Analysis, Statistics and Probability;Machine Learning