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Publications associées (53)
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 INC2023The inductive bias of deep learning: Connecting weights and functions
Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...
EPFL2023Understanding Deep Neural Function Approximation in Reinforcement Learning via ϵ-Greedy Exploration
Volkan Cevher, Fanghui Liu, Luca Viano
This paper provides a theoretical study of deep neural function approximation in reinforcement learning (RL) with the ϵ-greedy exploration under the online setting. This problem setting is motivated by the successful deep Q-networks (DQN) framework that fa ...
2022Linear Lipschitz and C-1 extension operators through random projection
We construct a regular random projection of a metric space onto a closed doubling subset and use it to linearly extend Lipschitz and C-1 functions. This way we prove more directly a result by Lee and Naor [5] and we generalize the C-l extension theorem by ...
ACADEMIC PRESS INC ELSEVIER SCIENCE2021MATHICSE Technical Report: Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval
Fabio Nobile, Yoshihito Kazashi
An existence result is presented for the dynamical low rank (DLR) approximation for random semi-linear evolutionary equations. The DLR solution approximates the true solution at each time instant by a linear combination of products of deterministic and sto ...
MATHICSE2020