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Julia Olkhovskaya
Julia Olkhovskaya
Verified email at tudelft.nl - Homepage
Title
Cited by
Cited by
Year
Efficient and robust algorithms for adversarial linear contextual bandits
G Neu, J Olkhovskaya
Conference on Learning Theory, 3049-3068, 2020
422020
Online learning in MDPs with linear function approximation and bandit feedback.
G Neu, J Olkhovskaya
Advances in Neural Information Processing Systems 34, 10407-10417, 2021
272021
Lifting the information ratio: An information-theoretic analysis of thompson sampling for contextual bandits
G Neu, I Olkhovskaia, M Papini, L Schwartz
Advances in Neural Information Processing Systems 35, 9486-9498, 2022
112022
Online influence maximization with local observations
G Lugosi, G Neu, J Olkhovskaya
Algorithmic Learning Theory, 557-580, 2019
8*2019
First-and second-order bounds for adversarial linear contextual bandits
J Olkhovskaya, J Mayo, T van Erven, G Neu, CY Wei
Advances in Neural Information Processing Systems 36, 2024
72024
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
S Vakili, J Olkhovskaya
Advances in Neural Information Processing Systems 36, 2024
12024
Adversarial Contextual Bandits Go Kernelized
G Neu, J Olkhovskaya, S Vakili
Proceedings of Machine Learning Research vol 237, 1-23, 2024
2024
Large-scale online learning under partial feedback
I Olkhovskaia
Universitat Pompeu Fabra, 2022
2022
Learning to maximize global influence from local observations
G Lugosi, G Neu, J Olkhovskaya
arXiv preprint arXiv:2109.11909, 2021
2021
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Articles 1–9