8 citations · 31 across the 37 of their papers we have counts for
4 papers · 1 filter
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Gabriel Cardoso, Sergey Samsonov, Achille Thin +2
Importance Sampling (IS) is a method for approximating expectations under a target distribution using independent samples from a proposal distribution and the associated importance…
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation
Alain Durmus, Eric Moulines, Alexey Naumov +1
This paper provides a finite-time analysis of linear stochastic approximation (LSA) algorithms with fixed step size, a core method in statistics and machine learning. LSA is used t…
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
Daniil Tiapkin, Denis Belomestny, Eric Moulines +5
We propose the Bayes-UCBVI algorithm for reinforcement learning in tabular, stage-dependent, episodic Markov decision process: a natural extension of the Bayes-UCB algorithm by Kau…
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations
Denis Belomestny, Alexey Naumov, Nikita Puchkin +1
This paper investigates the approximation properties of deep neural networks with piecewise-polynomial activation functions. We derive the required depth, width, and sparsity of a…