9 citations · 17 across the 11 of their papers we have counts for
12 papers
An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
Tim van Erven, Jack Mayo, Julia Olkhovskaya +1
We present an oracle-efficient, near-optimal algorithm for linear contextual bandits with adversarial losses and stochastic action sets, only requiring a linear optimization oracle…
Sparse Nonparametric Contextual Bandits
Hamish Flynn, Julia Olkhovskaya, Paul Rognon-Vael
We study the benefits of sparsity in nonparametric contextual bandit problems, in which the set of candidate features is countably or uncountably infinite. Our contribution is two-…
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm
Sattar Vakili, Julia Olkhovskaya
Reinforcement learning utilizing kernel ridge regression to predict the expected value function represents a powerful method with great representational capacity. This setting is a…
Improved Regret Bounds for Bandits with Expert Advice
Nicolò Cesa-Bianchi, Khaled Eldowa, Emmanuel Esposito +1
In this research note, we revisit the bandits with expert advice problem. Under a restricted feedback model, we prove a lower bound of order for the worst-cas…
Adversarial Contextual Bandits Go Kernelized
Gergely Neu, Julia Olkhovskaya, Sattar Vakili
We study a generalization of the problem of online learning in adversarial linear contextual bandits by incorporating loss functions that belong to a reproducing kernel Hilbert spa…
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
Sattar Vakili, Julia Olkhovskaya
Reinforcement learning (RL) has shown empirical success in various real world settings with complex models and large state-action spaces. The existing analytical results, however,…
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
Julia Olkhovskaya, Jack Mayo, Tim van Erven +2
We consider the adversarial linear contextual bandit setting, which allows for the loss functions associated with each of arms to change over time without restriction. Assuming…
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
Gergely Neu, Julia Olkhovskaya, Matteo Papini +1
We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theo…
Learning to maximize global influence from local observations
Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
We study a family online influence maximization problems where in a sequence of rounds , a decision maker selects one from a large number of agents with the goal of m…
Online learning in MDPs with linear function approximation and bandit feedback
Gergely Neu, Julia Olkhovskaya
We consider an online learning problem where the learner interacts with a Markov decision process in a sequence of episodes, where the reward function is allowed to change between…
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits
Gergely Neu, Julia Olkhovskaya
We consider an adversarial variant of the classic -armed linear contextual bandit problem where the sequence of loss functions associated with each arm are allowed to change wit…
Online Influence Maximization with Local Observations
Julia Olkhovskaya, Gergely Neu, Gábor Lugosi
We consider an online influence maximization problem in which a decision maker selects a node among a large number of possibilities and places a piece of information at the node. T…