Julia Olkhovskaya

22 papers A* 6B 2Journal 13

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction. CoRR Tim van Erven, Jack J. Mayo, Julia Olkhovskaya, Chen-Yu Wei
2025	J	`jnl`	Improved Regret Bounds for Bandits with Expert Advice. J. Artif. Intell. Res. Nicolò Cesa-Bianchi, Khaled Eldowa, Emmanuel Esposito, Julia Olkhovskaya
2025	J	`jnl`	Sparse Nonparametric Contextual Bandits. CoRR Hamish Flynn, Julia Olkhovskaya, Paul Rognon-Vael
2024	B	`conf`	Adversarial Contextual Bandits Go Kernelized. ALT Gergely Neu, Julia Olkhovskaya, Sattar Vakili
2024	J	`jnl`	Improved Regret Bounds for Bandits with Expert Advice. CoRR Nicolò Cesa-Bianchi, Khaled Eldowa, Emmanuel Esposito, Julia Olkhovskaya
2024	A*	`conf`	Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm. NeurIPS Sattar Vakili, Julia Olkhovskaya
2024	J	`jnl`	Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm. CoRR Sattar Vakili, Julia Olkhovskaya
2023	J	`jnl`	Adversarial Contextual Bandits Go Kernelized. CoRR Gergely Neu, Julia Olkhovskaya, Sattar Vakili
2023	A*	`conf`	First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. NeurIPS Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei
2023	J	`jnl`	First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. CoRR Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei
2023	A*	`conf`	Kernelized Reinforcement Learning with Order Optimal Regret Bounds. NeurIPS Sattar Vakili, Julia Olkhovskaya
2023	J	`jnl`	Kernelized Reinforcement Learning with Order Optimal Regret Bounds. CoRR Sattar Vakili, Julia Olkhovskaya
2022	—	`—`	Large-scale online learning under partial feedback. Julia Olkhovskaya
2022	A*	`conf`	Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. NeurIPS Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz
2022	J	`jnl`	Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. CoRR Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz
2021	J	`jnl`	Learning to maximize global influence from local observations. CoRR Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
2021	A*	`conf`	Online learning in MDPs with linear function approximation and bandit feedback. NeurIPS Gergely Neu, Julia Olkhovskaya
2020	J	`jnl`	Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits. CoRR Gergely Neu, Julia Olkhovskaya
2020	A*	`conf`	Efficient and robust algorithms for adversarial linear contextual bandits. COLT Gergely Neu, Julia Olkhovskaya
2020	J	`jnl`	Online learning in MDPs with linear function approximation and bandit feedback. CoRR Gergely Neu, Julia Olkhovskaya
2019	B	`conf`	Online Influence Maximization with Local Observations. ALT Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
2018	J	`jnl`	Online Influence Maximization with Local Observations. CoRR Julia Olkhovskaya, Gergely Neu, Gábor Lugosi