Search Results for author: Clare Lyle

Found 29 papers, 7 papers with code

Disentangling the Causes of Plasticity Loss in Neural Networks

no code implementations29 Feb 2024 Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney

Underpinning the past decades of work on the design, initialization, and optimization of neural networks is a seemingly innocuous assumption: that the network is trained on a \textit{stationary} data distribution.

Atari Games reinforcement-learning

Mixtures of Experts Unlock Parameter Scaling for Deep RL

no code implementations13 Feb 2024 Johan Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance scales proportionally to its size.

reinforcement-learning Self-Supervised Learning

Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model

no code implementations12 Feb 2024 Mark Rowland, Li Kevin Wenliang, Rémi Munos, Clare Lyle, Yunhao Tang, Will Dabney

We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative model (up to logarithmic factors), resolving an open question of Zhang et al. (2023).

Distributional Reinforcement Learning reinforcement-learning +1

DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design

1 code implementation7 Dec 2023 Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, Patrick Schwab

The discovery of therapeutics to treat genetically-driven pathologies relies on identifying genes involved in the underlying disease mechanisms.

Experimental Design

Understanding plasticity in neural networks

no code implementations2 Mar 2023 Clare Lyle, Zeyu Zheng, Evgenii Nikishin, Bernardo Avila Pires, Razvan Pascanu, Will Dabney

Plasticity, the ability of a neural network to quickly change its predictions in response to new information, is essential for the adaptability and robustness of deep reinforcement learning systems.

Atari Games

Generalization Through the Lens of Learning Dynamics

no code implementations11 Dec 2022 Clare Lyle

A machine learning (ML) system must learn not only to match the output of a target function on a training set, but also to generalize to novel situations in order to yield accurate predictions at deployment.

reinforcement-learning Reinforcement Learning (RL)

Learning Dynamics and Generalization in Reinforcement Learning

no code implementations5 Jun 2022 Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal

Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a potentially discontinuous value function, and generalizing well to new observations.

Policy Gradient Methods reinforcement-learning +1

Understanding and Preventing Capacity Loss in Reinforcement Learning

no code implementations ICLR 2022 Clare Lyle, Mark Rowland, Will Dabney

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.

Montezuma's Revenge reinforcement-learning +1

DARTS without a Validation Set: Optimizing the Marginal Likelihood

no code implementations24 Dec 2021 Miroslav Fil, Binxin Ru, Clare Lyle, Yarin Gal

The success of neural architecture search (NAS) has historically been limited by excessive compute requirements.

Neural Architecture Search

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

3 code implementations NeurIPS 2021 Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal

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.

3D Part Segmentation

Provable Guarantees on the Robustness of Decision Rules to Causal Interventions

1 code implementation19 May 2021 Benjie Wang, Clare Lyle, Marta Kwiatkowska

Robustness of decision rules to shifts in the data-generating process is crucial to the successful deployment of decision-making systems.

Decision Making

Robustness to Pruning Predicts Generalization in Deep Neural Networks

no code implementations10 Mar 2021 Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal

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.

On The Effect of Auxiliary Tasks on Representation Dynamics

no code implementations25 Feb 2021 Clare Lyle, Mark Rowland, Georg Ostrovski, Will Dabney

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.

reinforcement-learning Reinforcement Learning (RL)

Unpacking Information Bottlenecks: Surrogate Objectives for Deep Learning

no code implementations1 Jan 2021 Andreas Kirsch, Clare Lyle, Yarin Gal

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.

Density Estimation

A Bayesian Perspective on Training Speed and Model Selection

no code implementations NeurIPS 2020 Clare Lyle, Lisa Schut, Binxin Ru, Yarin Gal, Mark van der Wilk

This provides two major insights: first, that a measure of a model's training speed can be used to estimate its marginal likelihood.

Model Selection

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

no code implementations28 Sep 2020 Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, Yarin Gal

Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).

Model Selection Neural Architecture Search

Speedy Performance Estimation for Neural Architecture Search

2 code implementations NeurIPS 2021 Binxin Ru, Clare Lyle, Lisa Schut, Miroslav Fil, Mark van der Wilk, Yarin Gal

Reliable yet efficient evaluation of generalisation performance of a proposed architecture is crucial to the success of neural architecture search (NAS).

Model Selection Neural Architecture Search

On the Benefits of Invariance in Neural Networks

no code implementations1 May 2020 Clare Lyle, Mark van der Wilk, Marta Kwiatkowska, Yarin Gal, Benjamin Bloem-Reddy

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.

Data Augmentation

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

no code implementations27 Mar 2020 Andreas Kirsch, Clare Lyle, Yarin Gal

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.

Density Estimation

Invariant Causal Prediction for Block MDPs

1 code implementation ICML 2020 Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges.

Causal Inference Variable Selection

A Comparative Analysis of Expected and Distributional Reinforcement Learning

no code implementations30 Jan 2019 Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare

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 +1

GAN Q-learning

1 code implementation13 May 2018 Thang Doan, Bogdan Mazoure, Clare Lyle

Distributional reinforcement learning (distributional RL) has seen empirical success in complex Markov Decision Processes (MDPs) in the setting of nonlinear function approximation.

Distributional Reinforcement Learning OpenAI Gym +3

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