Clare Lyle | DeepAI

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∙ 05/28/2023

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

We study the problem of temporal-difference-based policy evaluation in r...

0 Mark Rowland, et al. ∙

∙ 03/02/2023

Understanding plasticity in neural networks

Plasticity, the ability of a neural network to quickly change its predic...

0 Clare Lyle, et al. ∙

∙ 12/11/2022

Generalization Through the Lens of Learning Dynamics

A machine learning (ML) system must learn not only to match the output o...

0 Clare Lyle, et al. ∙

∙ 12/06/2022

Understanding Self-Predictive Learning for Reinforcement Learning

We study the learning dynamics of self-predictive learning for reinforce...

0 Yunhao Tang, et al. ∙

∙ 06/05/2022

Learning Dynamics and Generalization in Reinforcement Learning

Solving a reinforcement learning (RL) problem poses two competing challe...

0 Clare Lyle, et al. ∙

∙ 04/20/2022

Understanding and Preventing Capacity Loss in Reinforcement Learning

The reinforcement learning (RL) problem is rife with sources of non-stat...

0 Clare Lyle, et al. ∙

∙ 12/24/2021

DARTS without a Validation Set: Optimizing the Marginal Likelihood

The success of neural architecture search (NAS) has historically been li...

0 Miroslav Fil, et al. ∙

∙ 06/04/2021

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

We challenge a common assumption underlying most supervised deep learnin...

19 Jannik Kossen, et al. ∙

∙ 05/19/2021

Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

Robustness of decision rules to shifts in the data-generating process is...

0 Benjie Wang, et al. ∙

∙ 03/10/2021

Robustness to Pruning Predicts Generalization in Deep Neural Networks

Existing generalization measures that aim to capture a model's simplicit...

0 Lorenz Kuhn, et al. ∙

∙ 02/25/2021

On The Effect of Auxiliary Tasks on Representation Dynamics

While auxiliary tasks play a key role in shaping the representations lea...

5 Clare Lyle, et al. ∙

∙ 02/24/2021

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning

We study reinforcement learning (RL) with no-reward demonstrations, a se...

16 Angelos Filos, et al. ∙

∙ 10/27/2020

A Bayesian Perspective on Training Speed and Model Selection

We take a Bayesian perspective to illustrate a connection between traini...

0 Clare Lyle, et al. ∙

∙ 06/08/2020

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search

Reliable yet efficient evaluation of generalisation performance of a pro...

22 Binxin Ru, et al. ∙

∙ 05/01/2020

On the Benefits of Invariance in Neural Networks

Many real world data analysis problems exhibit invariant structure, and ...

6 Clare Lyle, et al. ∙

∙ 03/27/2020

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning

The information bottleneck (IB) principle offers both a mechanism to exp...

18 Andreas Kirsch, et al. ∙

∙ 03/12/2020

Invariant Causal Prediction for Block MDPs

Generalization across environments is critical to the successful applica...

2 Amy Zhang, et al. ∙

∙ 01/31/2019

A Geometric Perspective on Optimal Representations for Reinforcement Learning

This paper proposes a new approach to representation learning based on g...

10 Marc G. Bellemare, et al. ∙

∙ 01/30/2019

A Comparative Analysis of Expected and Distributional Reinforcement Learning

Since their introduction a year ago, distributional approaches to reinfo...

6 Clare Lyle, et al. ∙

∙ 05/13/2018

GAN Q-learning

Distributional reinforcement learning (distributional RL) has seen empir...

0 Thang Doan, et al. ∙

∙ 02/20/2018

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

This report surveys the landscape of potential security threats from mal...

3 Miles Brundage, et al. ∙

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