Online Influence Maximization with Local Observations. - Gábor Lugosi; Gergely Neu; Julia Olkhovskaya (ALT, 2019)

Efficient and robust algorithms for adversarial linear contextual bandits. - Gergely Neu; Julia Olkhovskaya (COLT, 2020)

Online learning in MDPs with linear function approximation and bandit feedback. - Gergely Neu; Julia Olkhovskaya (NeurIPS, 2021)

Learning to maximize global influence from local observations. - Gábor Lugosi; Gergely Neu; Julia Olkhovskaya (CoRR, 2021)

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. - Gergely Neu; Julia Olkhovskaya; Matteo Papini; Ludovic Schwartz (NeurIPS, 2022)

Large-scale online learning under partial feedback. - Julia Olkhovskaya (2022)

First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. - Julia Olkhovskaya; Jack J. Mayo; Tim van Erven; Gergely Neu; Chen-Yu Wei (NeurIPS, 2023)

Kernelized Reinforcement Learning with Order Optimal Regret Bounds. - Sattar Vakili; Julia Olkhovskaya (NeurIPS, 2023)

Adversarial Contextual Bandits Go Kernelized. - Gergely Neu; Julia Olkhovskaya; Sattar Vakili (ALT, 2024)

Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm. - Sattar Vakili; Julia Olkhovskaya (NeurIPS, 2024)

Improved Regret Bounds for Bandits with Expert Advice. - Nicolò Cesa-Bianchi; Khaled Eldowa; Emmanuel Esposito; Julia Olkhovskaya (J. Artif. Intell. Res., 2025)

Sparse Nonparametric Contextual Bandits. - Hamish Flynn; Julia Olkhovskaya; Paul Rognon-Vael (CoRR, 2025)

An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction. - Tim van Erven; Jack J. Mayo; Julia Olkhovskaya; Chen-Yu Wei (CoRR, 2025)