Publications related to End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Alternation makes the adversary weaker in two-player games

Volkan Cevher, Efstratios Panteleimon Skoulakis, Luca Viano, Ali Kavis

Motivated by alternating game-play in two-player games, we study an altenating variant of the Online Linear Optimization (OLO). In alternating OLO, a learner at each round t ∈[n] selects a vector xt and then an adversary selects a cost-vector ct ∈[−1,1]n. ...

2023

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Paul Thierry Yves Rolland

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

EPFL2022

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning.However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

Optimization Over Banach Spaces: A Unified View on Supervised Learning and Inverse Problems

Shayan Aziznejad

In this thesis, we reveal that supervised learning and inverse problems share similar mathematical foundations. Consequently, we are able to present a unified variational view of these tasks that we formulate as optimization problems posed over infinite-di ...

EPFL2022

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Maryam Kamgarpour, Andreas Krause

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Financial Risk Management with Machine Learning

Marc-Aurèle Antoine Divernois

This thesis consists of three applications of machine learning techniques to risk management. The first chapter proposes a deep learning approach to estimate physical forward default intensities of companies. Default probabilities are computed using artifi ...

EPFL2022

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Angelos Katharopoulos

Deep neural networks have completely revolutionized the field of machinelearning by achieving state-of-the-art results on various tasks ranging fromcomputer vision to protein folding. However, their application is hindered bytheir large computational and m ...

EPFL2022

Trace norm regularization for multi-task learning with scarce data

Nicolas Henri Bernard Flammarion, Etienne Patrice Boursier, Mikhail Konobeev

Multi-task learning leverages structural similarities between multiple tasks to learn despite very few samples. Motivated by the recent success of neural networks applied to data-scarce tasks, we consider a linear low-dimensional shared representation mode ...

2022

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

Momentum-Based Policy Gradient with Second-Order Information

Patrick Thiran, Negar Kiyavash, Mohammadsadegh Khorasani, Saber Salehkaleybar

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

2022

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Graph Chatbot

Chat with Graph Search

Alternation makes the adversary weaker in two-player games

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Optimization Over Banach Spaces: A Unified View on Supervised Learning and Inverse Problems

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Financial Risk Management with Machine Learning

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Trace norm regularization for multi-task learning with scarce data

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

Momentum-Based Policy Gradient with Second-Order Information

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Optimization Over Banach Spaces: A Unified View on Supervised Learning and Inverse Problems

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Financial Risk Management with Machine Learning

Alternation makes the adversary weaker in two-player games

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

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

Momentum-Based Policy Gradient with Second-Order Information

Trace norm regularization for multi-task learning with scarce data