Publications related to Monte-Carlo utility estimates for Bayesian reinforcement learning

Understanding generalization and robustness in modern deep learning

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Revisiting Character-level Adversarial Attacks for Language Models

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu, Elias Abad Rocamora

Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adv ...

2024

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Colin Neil Jones, Bratislav Svetozarevic, Loris Di Natale

Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies. On the other hand, we postulate that expert knowledge of the syste ...

2023

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Nirupam Gupta

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

f

Byzantine-faulty agents, which may not follow a prescr ...

Piscataway2023

Masked Training of Neural Networks with Partial Gradients

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

State-of-the-art training algorithms for deep learning models are based on stochastic gradient descent (SGD). Recently, many variations have been explored: perturbing parameters for better accuracy (such as in Extra-gradient), limiting SGD updates to a sub ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Florent Gérard Krzakala, Lenka Zdeborová, Ludovic Théo Stephan, Bruno Loureiro

Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow net-works, which tend to get stuck in badly-generalizing loca ...

2022

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Jonathan Paul Sauder

Countless signal processing applications include the reconstruction of signals from few indirect linear measurements. The design of effective measurement operators is typically constrained by the underlying hardware and physics, posing a challenging and of ...

2022

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Maria-Luiza Vladarean

We propose a stochastic conditional gradient method (CGM) for minimizing convex finitesum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully inc ...

2022

Monte-Carlo utility estimates for Bayesian reinforcement learning

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Understanding generalization and robustness in modern deep learning

Revisiting Character-level Adversarial Attacks for Language Models

Deep Learning Generalization with Limited and Noisy Labels

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Masked Training of Neural Networks with Partial Gradients

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Understanding generalization and robustness in modern deep learning

Deep Learning Generalization with Limited and Noisy Labels

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Masked Training of Neural Networks with Partial Gradients

Revisiting Character-level Adversarial Attacks for Language Models

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function