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Publications associées (29)
Understanding 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 ...
2022Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Andreas Loukas, Nikolaos Karalias
Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (I) not naturally amenable to gradient-based optimization, and (II) incompatible with deep lear ...
2022Nearly-Tight and Oblivious Algorithms for Explainable Clustering
Ola Nils Anders Svensson, Adam Teodor Polak, Buddhima Ruwanmini Gamlath Gamlath Ralalage, Xinrui Jia
We study the problem of explainable clustering in the setting first formalized by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). A k-clustering is said to be explainable if it is given by a decision tree where each internal node splits data point ...
2021A Note on BIBO Stability
The statements on the BIBO stability of continuoustime convolution systems found in engineering textbooks are often either too vague (because of lack of hypotheses) or mathematically incorrect. What is more troubling is that they usually exclude the identi ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020Geometric ergodicity for some space-time max-stable Markov chains
Max-stable processes are central models for spatial extremes. In this paper, we focus on some space-time max-stable models introduced in Embrechts et al. (2016). The processes considered induce discrete-time Markov chains taking values in the space of cont ...
ELSEVIER SCIENCE BV2019On two isomorphic Lie algebroids associated with feedback linearization
We present two Lie algebroids linked to the construction of the linearizing output of an input affine nonlinear system. The algorithmic development of the linearizing output proceeds inductively, and each stage has two structures, namely a codimension one ...
ELSEVIER2019Some regularity results for p-harmonic mappings between Riemannian manifolds
Let M be a C-2-smooth Riemannian manifold with boundary and N a complete C-2-smooth Riemannian manifold. We show that each stationary p-harmonic mapping u: M -> N, whose image lies in a compact subset of N, is locally C-1,C-alpha for some alpha is an eleme ...
2019Stochastic partial differential equations driven by Lévy white noises
Thomas Marie Jean-Baptiste Humeau
We study various aspects of stochastic partial differential equations driven by Lévy white noise. This driving noise, which is a generalization of Gaussian white noise, can be viewed either as a generalized random process or as an independently scattered r ...
EPFL2017On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes
The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering ...
Soc Neuroscience2017