Concept
Théorie du regret
Publications associées (39)
Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks
We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023Interventionist estimands in event history analysis
The presence of competing events, such as death, makes it challenging to define causal effects on recurrent outcomes. In this thesis, I formalize causal inference for recurrent events, with and without competing events. I define several causal estimands an ...
EPFL2023Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
Giancarlo Ferrari Trecate, John Lygeros, Luca Furieri, Florian Dörfler, Andrea Martin
We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and future d ...
Elsevier2023When do Minimax-fair Learning and Empirical Risk Minimization Coincide?
Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...
2023No-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 ...
2022Alpha-NML Universal Predictors
Michael Christoph Gastpar, Marco Bondaschi
Inspired by Sibson’s alpha-mutual information, we introduce a new parametric class of universal predictors. This class interpolates two well-known predictors, the mixture estimator, that includes the Laplace and the Krichevsky-Trofimov predictors, and the ...
2022Relatively robust decisions
It is natural for humans to judge the outcome of a decision under uncertainty as a percentage of an ex-post optimal performance. We propose a robust decision-making framework based on a relative performance index. It is shown that if the decision maker's p ...
SPRINGER2022Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach
Volkan Cevher, Shaul Nadav Hallak
This paper develops a methodology for regret minimization with stochastic first-order oracle feedback in online, constrained, non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, and ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2021Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems
Maryam Kamgarpour, Andreas Krause, Ilija Bogunovic
Motivated by applications in shared mobility, we address the problem of allocating a group of agents to a set of resources to maximize a cumulative welfare objective. We model the welfare obtainable from each resource as a monotone DR-submodular function w ...
PMLR2021Minimizing Regret of Bandit Online Optimization in Unconstrained Action Spaces
We consider online convex optimization with a zero-order oracle feedback. In particular, the decision maker does not know the explicit representation of the time-varying cost functions, or their gradients. At each time step, she observes the value of the c ...
2020