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Julia Olkhovskaya
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Books and Theses
- 2022
- [b1]Julia Olkhovskaya:
Large-scale online learning under partial feedback. Pompeu Fabra University, Spain, 2022
Conference and Workshop Papers
- 2024
- [c7]Gergely Neu, Julia Olkhovskaya, Sattar Vakili:
Adversarial Contextual Bandits Go Kernelized. ALT 2024: 907-929 - 2023
- [c6]Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei:
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. NeurIPS 2023 - [c5]Sattar Vakili, Julia Olkhovskaya:
Kernelized Reinforcement Learning with Order Optimal Regret Bounds. NeurIPS 2023 - 2022
- [c4]Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz:
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. NeurIPS 2022 - 2021
- [c3]Gergely Neu, Julia Olkhovskaya:
Online learning in MDPs with linear function approximation and bandit feedback. NeurIPS 2021: 10407-10417 - 2020
- [c2]Gergely Neu, Julia Olkhovskaya:
Efficient and robust algorithms for adversarial linear contextual bandits. COLT 2020: 3049-3068 - 2019
- [c1]Gábor Lugosi, Gergely Neu, Julia Olkhovskaya:
Online Influence Maximization with Local Observations. ALT 2019: 557-580
Informal and Other Publications
- 2024
- [i10]Nicolò Cesa-Bianchi, Khaled Eldowa, Emmanuel Esposito, Julia Olkhovskaya:
Improved Regret Bounds for Bandits with Expert Advice. CoRR abs/2406.16802 (2024) - [i9]Sattar Vakili, Julia Olkhovskaya:
Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm. CoRR abs/2410.23498 (2024) - 2023
- [i8]Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei:
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. CoRR abs/2305.00832 (2023) - [i7]Sattar Vakili, Julia Olkhovskaya:
Kernelized Reinforcement Learning with Order Optimal Regret Bounds. CoRR abs/2306.07745 (2023) - [i6]Gergely Neu, Julia Olkhovskaya, Sattar Vakili:
Adversarial Contextual Bandits Go Kernelized. CoRR abs/2310.01609 (2023) - 2022
- [i5]Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz:
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. CoRR abs/2205.13924 (2022) - 2021
- [i4]Gábor Lugosi, Gergely Neu, Julia Olkhovskaya:
Learning to maximize global influence from local observations. CoRR abs/2109.11909 (2021) - 2020
- [i3]Gergely Neu, Julia Olkhovskaya:
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits. CoRR abs/2002.00287 (2020) - [i2]Gergely Neu, Julia Olkhovskaya:
Online learning in MDPs with linear function approximation and bandit feedback. CoRR abs/2007.01612 (2020) - 2018
- [i1]Julia Olkhovskaya, Gergely Neu, Gábor Lugosi:
Online Influence Maximization with Local Observations. CoRR abs/1805.11022 (2018)
Coauthor Index
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