Debabrota Basu

Visar 9 publikationer

2024

Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer

Hannes Eriksson, Tommy Tram, Debabrota Basu et al
Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. Vol. 2024-May, p. 516-524
Paper i proceeding
2022

Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty

Hannes Eriksson, Debabrota Basu, Mina Alibeigi et al
Preprint
2022

SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning

Hannes Eriksson, Debabrota Basu, Mina Alibeigi et al
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. Vol. 180, p. 631-640
Paper i proceeding
2021

Justicia: A Stochastic SAT Approach to Formally Verify Fairness

Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel
35th AAAI Conference on Artificial Intelligence, AAAI 2021. Vol. 9A, p. 7554-7563
Paper i proceeding
2020

BelMan: An Information-Geometric Approach to Stochastic Bandits

Debabrota Basu, Pierre Senellart, Stéphane Bressan
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 11908 LNAI, p. 167-183
Paper i proceeding
2020

Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning

Emilio Jorge, Hannes Eriksson, Christos Dimitrakakis et al
Proceedings of Machine Learning Research. Vol. 137, p. 43-52
Paper i proceeding
2020

Bayesian Reinforcement Learning via Deep, Sparse Sampling

Divya Grover, Debabrota Basu, Christos Dimitrakakis
Proceedings of Machine Learning Research. Vol. 108, p. 3036-3045
Paper i proceeding
2020

Construction and random generation of hypergraphs with prescribed degree and dimension sequences

Naheed Anjum Arafat, Debabrota Basu, Laurent Decreusefond et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 12392 LNCS, p. 130-145
Paper i proceeding
2019

Topological Data Analysis with ϵ -net Induced Lazy Witness Complex

Naheed Anjum Arafat, Debabrota Basu, Stéphane Bressan
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 11707 LNCS, p. 376-392
Paper i proceeding

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Visar 1 forskningsprojekt

2019–2019

Förstudie: Deep Learning for Deep Water

Ida-Maja Hassellöv Maritim miljövetenskap
Alexander Schliep Data Science
Amanda Nylund Maritim miljövetenskap
Debabrota Basu Data Science
Chalmers

2 publikationer finns
Det kan finnas fler projekt där Debabrota Basu medverkar, men du måste vara inloggad som anställd på Chalmers för att kunna se dem.