157 citations · 713 across the 56 of their papers we have counts for
6 papers · 1 filter
Online-to-PAC generalization bounds under graph-mixing dependencies
Baptiste Abélès, Eugenio Clerico, Gergely Neu
Traditional generalization results in statistical learning require a training data set made of independently drawn examples. Most of the recent efforts to relax this independence a…
Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
Sergio Calo, Anders Jonsson, Gergely Neu +2
We propose a new framework for formulating optimal transport distances between Markov chains. Previously known formulations studied couplings between the entire joint distribution…
Generalization bounds for mixing processes via delayed online-to-PAC conversions
Baptiste Abeles, Eugenio Clerico, Gergely Neu
We study the generalization error of statistical learning algorithms in a non-i.i.d. setting, where the training data is sampled from a stationary mixing process. We develop an ana…
Offline RL via Feature-Occupancy Gradient Ascent
Gergely Neu, Nneka Okolo
We study offline Reinforcement Learning in large infinite-horizon discounted Markov Decision Processes (MDPs) when the reward and transition models are linearly realizable under a…
Optimistic Information Directed Sampling
Gergely Neu, Matteo Papini, Ludovic Schwartz
We study the problem of online learning in contextual bandit problems where the loss function is assumed to belong to a known parametric function class. We propose a new analytic f…
Dealing with unbounded gradients in stochastic saddle-point optimization
Gergely Neu, Nneka Okolo
We study the performance of stochastic first-order methods for finding saddle points of convex-concave functions. A notorious challenge faced by such methods is that the gradients…