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Pratik Gajane
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2020 – today
- 2024
- [c12]Ronald C. van den Broek, Rik Litjens, Tobias Sagis, Nina Verbeeke, Pratik Gajane:
Multi-armed Bandits with Generalized Temporally-Partitioned Rewards. IDA (1) 2024: 41-52 - [i18]Pratik Gajane, Sean Newman, John D. Piette:
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain. CoRR abs/2402.19226 (2024) - 2023
- [c11]Rosa Van Tuijn, Tianqin Lu, Emma Driesse, Koen Franken, Pratik Gajane, Emilia I. Barakova:
WeHeart: A Personalized Recommendation Device for Physical Activity Encouragement in Cardiac Rehabilitation. HHAI 2023: 440-442 - [c10]Dennis Collaris, Pratik Gajane, Joost Jorritsma, Jarke J. van Wijk, Mykola Pechenizkiy:
LEMON: Alternative Sampling for More Faithful Explanation Through Local Surrogate Models. IDA 2023: 77-90 - [c9]Pratik Gajane, Peter Auer, Ronald Ortner:
Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms. IJCAI 2023: 3714-3722 - [c8]Rosa Van Tuijn, Tianqin Lu, Emma Driesse, Koen Franken, Pratik Gajane, Emilia I. Barakova:
WeHeart: A Personalized Recommendation Device for Physical Activity Encouragement and Preventing "Cold Start" in Cardiac Rehabilitation. INTERACT (3) 2023: 191-201 - [i17]Pratik Gajane:
Local Differential Privacy for Sequential Decision Making in a Changing Environment. CoRR abs/2301.00561 (2023) - [i16]Jiong Li, Pratik Gajane:
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning. CoRR abs/2302.10825 (2023) - [i15]Ronald C. van den Broek, Rik Litjens, Tobias Sagis, Luc Siecker, Nina Verbeeke, Pratik Gajane:
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards. CoRR abs/2303.00620 (2023) - [i14]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
Provably Efficient Exploration in Constrained Reinforcement Learning: Posterior Sampling Is All You Need. CoRR abs/2309.15737 (2023) - 2022
- [c7]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Linear Bandits. ICDM 2022: 1149-1154 - [i13]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Linear Bandits. CoRR abs/2202.06657 (2022) - [i12]Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, Mykola Pechenizkiy:
Survey on Fair Reinforcement Learning: Theory and Practice. CoRR abs/2205.10032 (2022) - [i11]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning. CoRR abs/2209.03596 (2022) - [i10]Ronald C. van den Broek, Rik Litjens, Tobias Sagis, Luc Siecker, Nina Verbeeke, Pratik Gajane:
Generalizing distribution of partial rewards for multi-armed bandits with temporally-partitioned rewards. CoRR abs/2211.06883 (2022) - 2021
- [c6]Filipo Studzinski Perotto, Sattar Vakili, Pratik Gajane, Yaser Faghan, Mathieu Bourgais:
Gambler Bandits and the Regret of Being Ruined. AAMAS 2021: 1664-1667 - [i9]Danil Provodin, Pratik Gajane, Mykola Pechenizkiy, Maurits Kaptein:
The Impact of Batch Learning in Stochastic Bandits. CoRR abs/2111.02071 (2021)
2010 – 2019
- 2019
- [c5]Peter Auer, Pratik Gajane, Ronald Ortner:
Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes. COLT 2019: 138-158 - [c4]Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner, Chen-Yu Wei:
Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information. COLT 2019: 159-163 - [c3]Ronald Ortner, Pratik Gajane, Peter Auer:
Variational Regret Bounds for Reinforcement Learning. UAI 2019: 81-90 - [i8]Pratik Gajane, Ronald Ortner, Peter Auer:
Variational Regret Bounds for Reinforcement Learning. CoRR abs/1905.05857 (2019) - [i7]Pratik Gajane, Ronald Ortner, Peter Auer, Csaba Szepesvári:
Autonomous exploration for navigating in non-stationary CMPs. CoRR abs/1910.08446 (2019) - 2018
- [c2]Pratik Gajane, Tanguy Urvoy, Emilie Kaufmann:
Corrupt Bandits for Preserving Local Privacy. ALT 2018: 387-412 - [i6]Pratik Gajane, Ronald Ortner, Peter Auer:
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions. CoRR abs/1805.10066 (2018) - 2017
- [b1]Pratik Gajane:
Bandits Multi-bras avec retour d'information non-conventionnelle. (Multi-Armed Bandits with Unconventional Feedback). Charles de Gaulle University - Lille III, France, 2017 - [i5]Pratik Gajane, Tanguy Urvoy, Emilie Kaufmann:
Corrupt Bandits for Privacy Preserving Input. CoRR abs/1708.05033 (2017) - [i4]Pratik Gajane:
On formalizing fairness in prediction with machine learning. CoRR abs/1710.03184 (2017) - [i3]Carolin Lawrence, Pratik Gajane, Stefan Riezler:
Counterfactual Learning for Machine Translation: Degeneracies and Solutions. CoRR abs/1711.08621 (2017) - 2016
- [i2]Pratik Gajane, Tanguy Urvoy, Fabrice Clérot:
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits. CoRR abs/1601.03855 (2016) - 2015
- [c1]Pratik Gajane, Tanguy Urvoy, Fabrice Clérot:
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits. ICML 2015: 218-227 - [i1]Pratik Gajane, Tanguy Urvoy:
Utility-based Dueling Bandits as a Partial Monitoring Game. CoRR abs/1507.02750 (2015)
Coauthor Index
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last updated on 2024-04-25 23:36 CEST by the dblp team
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