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Found 20 papers, 5 papers with code
Learning Dynamics and Generalization in Reinforcement Learning
Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations.
Understanding and Preventing Capacity Loss in Reinforcement Learning
The reinforcement learning (RL) problem is rife with sources of non-stationarity, making it a notoriously difficult problem domain for the application of neural networks.
DARTS without a Validation Set: Optimizing the Marginal Likelihood
The success of neural architecture search (NAS) has historically been limited by excessive compute requirements.
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input.
Provable Guarantees on the Robustness of Decision Rules to Causal Interventions
Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems.
Robustness to Pruning Predicts Generalization in Deep Neural Networks
Existing generalization measures that aim to capture a model's simplicity based on parameter counts or norms fail to explain generalization in overparameterized deep neural networks.
Resolving Causal Confusion in Reinforcement Learning via Robust Exploration
To address this, we present a robust exploration strategy which enables causal hypothesis-testing by interaction with the environment.
On The Effect of Auxiliary Tasks on Representation Dynamics
While auxiliary tasks play a key role in shaping the representations learnt by reinforcement learning agents, much is still unknown about the mechanisms through which this is achieved.
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
This allows us to disentangle shared features and dynamics of the environment from agent-specific rewards and policies.
Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning
The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models.
A Bayesian Perspective on Training Speed and Model Selection
This provides two major insights: first, that a measure of a model's training speed can be used to estimate its marginal likelihood.
Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).
Speedy Performance Estimation for Neural Architecture Search
Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).
On the Benefits of Invariance in Neural Networks
Many real world data analysis problems exhibit invariant structure, and models that take advantage of this structure have shown impressive empirical performance, particularly in deep learning.
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models.
Invariant Causal Prediction for Block MDPs
Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges.
A Geometric Perspective on Optimal Representations for Reinforcement Learning
We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks.
A Comparative Analysis of Expected and Distributional Reinforcement Learning
Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results relative to the standard approach which models expected values (expected RL).
Distributional Reinforcement Learning
reinforcement-learning
GAN Q-learning
Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation.
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats.
