2 citations · 3 across the 8 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,…