Publications related to Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Phenomenological theory of variational quantum ground-state preparation

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver algorithm aims to prepare the ground state of a Hamiltonian exploiting para ...

College Pk2023

Stochastic adaptive resampling for the estimation of discrete choice models

Michel Bierlaire, Nicola Marco Ortelli, Matthieu Marie Cochon de Lapparent

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

2023

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Rachid Guerraoui, Andrei Kucharavy, Matteo Monti

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

New York2023

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Nirupam Gupta, Shuo Liu

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

New York2023

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Rachid Guerraoui, Nirupam Gupta, John Stephan, Youssef Allouah, Rafaël Benjamin Pinot

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

2023

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

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

Approximate CVPp in time 2(0.802n)

Friedrich Eisenbrand, Moritz Andreas Venzin

We show that a constant factor approximation of the shortest and closest lattice vector problem in any l(p)-norm can be computed in time 2((0.802 + epsilon)n). This matches the currently fastest constant factor approximation algorithm for the shortest vect ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Volkan Cevher, Kimon Antonakopoulos, Ya-Ping Hsieh

We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algorithm, it is possible to achieve significantly lower regret relative to fully ...

2022

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

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Phenomenological theory of variational quantum ground-state preparation

Stochastic adaptive resampling for the estimation of discrete choice models

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Impact of Redundancy on Resilience in Distributed Optimization and Learning

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Alternation makes the adversary weaker in two-player games

Predicting in Uncertain Environments: Methods for Robust Machine Learning

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

Approximate CVPp in time 2(0.802n)

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Phenomenological theory of variational quantum ground-state preparation

Approximate CVPp in time 2(0.802n)

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Impact of Redundancy on Resilience in Distributed Optimization and Learning

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Alternation makes the adversary weaker in two-player games

Predicting in Uncertain Environments: Methods for Robust Machine Learning

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

No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation

Stochastic adaptive resampling for the estimation of discrete choice models