Julia Olkhovskaya

According to our database, Julia Olkhovskaya authored at least 9 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Adversarial Contextual Bandits Go Kernelized.

Gergely Neu, Julia Olkhovskaya, Sattar Vakili

CoRR, 2023

Kernelized Reinforcement Learning with Order Optimal Regret Bounds.

Sattar Vakili, Julia Olkhovskaya

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

First- and Second-Order Bounds for Adversarial Linear Contextual Bandits.

Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Large-scale online learning under partial feedback.

Julia Olkhovskaya

PhD thesis, 2022

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits.

Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Learning to maximize global influence from local observations.

Gábor Lugosi, Gergely Neu, Julia Olkhovskaya

CoRR, 2021

Online learning in MDPs with linear function approximation and bandit feedback.

Gergely Neu, Julia Olkhovskaya

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Efficient and robust algorithms for adversarial linear contextual bandits.

Gergely Neu, Julia Olkhovskaya

Proceedings of the Conference on Learning Theory, 2020

2019

Online Influence Maximization with Local Observations.

Gábor Lugosi, Gergely Neu, Julia Olkhovskaya

Proceedings of the Algorithmic Learning Theory, 2019