Ishan Durugkar

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Peter StoneProfessor of Computer Science, The University of Texas at AustinVerified email at cs.utexas.edu
Sridhar MahadevanDirector, Data Science Lab, Adobe Research & Professor, University of Massachusetts, AmherstVerified email at cs.umass.edu
Ian GempDeepMindVerified email at google.com
Rajarshi DasUniversity of WashingtonVerified email at cs.washington.edu
Luke VilnisResearch Scientist, GoogleVerified email at cs.umass.edu
Shehzaad DhuliawalaETH ZurichVerified email at inf.ethz.ch
Akshay KrishnamurthyUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Andrew McCallumDistinguished Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Alex SmolaAmazon Web ServicesVerified email at smola.org
Manzil ZaheerGoogle ResearchVerified email at cmu.edu
Josiah P. HannaAssistant Professor, University of Wisconsin--MadisonVerified email at cs.wisc.edu
Dr Anand J KulkarniInstitute of Artificial Intelligence, MIT World Peace University, Pune, MH, IndiaVerified email at ntu.edu.sg
Elad Liebmanstaff research scientist, SparkCognitionVerified email at cs.utexas.edu
Garrett WarnellResearch Scientist, Army Research LaboratoryVerified email at mail.mil
Philip ThomasUniversity of Massachusetts AmherstVerified email at cs.umass.edu
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Georgios TheocharousAdobe ResearchVerified email at adobe.com
Mario ParenteAssociate Professor of Electrical and Computer Engineering, University of Massachusetts AmherstVerified email at ecs.umass.edu
Brahma S PavsePhD Student, University of Wisconsin-MadisonVerified email at wisc.edu
Clemens RosenbaumASAPP IncVerified email at asapp.com

Ishan Durugkar

Graduate Student, University of Texas at Austin

Verified email at cs.utexas.edu - Homepage

Reinforcement Learning Artificial Intelligence Machine Learning Optimization


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Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ... arXiv preprint arXiv:1711.05851, 2017	330	2017
Generative Multi-Adversarial Networks I Durugkar, I Gemp, S Mahadevan International Conference on Learning Representations, 2017, 2017	320	2017
Cohort intelligence: a self supervised learning behavior AJ Kulkarni, IP Durugkar, M Kumar 2013 IEEE international conference on systems, man, and cybernetics, 1396-1400, 2013	98	2013
Predictive off-policy policy evaluation for nonstationary decision problems, with applications to digital marketing PS Thomas, G Theocharous, M Ghavamzadeh, I Durugkar, E Brunskill Twenty-Ninth IAAI Conference, 2017	37	2017
Deep reinforcement learning with macro-actions IP Durugkar, C Rosenbaum, S Dernbach, S Mahadevan arXiv preprint arXiv:1606.04615, 2016	21	2016
TD learning with constrained gradients I Durugkar, P Stone	13	2018
An imitation from observation approach to transfer learning with dynamics mismatch S Desai, I Durugkar, H Karnan, G Warnell, J Hanna, P Stone Advances in Neural Information Processing Systems 33, 3917-3929, 2020	10	2020
Reducing sampling error in batch temporal difference learning B Pavse, I Durugkar, J Hanna, P Stone International Conference on Machine Learning, 7543-7552, 2020	9	2020
Balancing individual preferences and shared objectives in multiagent reinforcement learning I Durugkar, E Liebman, P Stone Good Systems-Published Research, 2020	9	2020
Adversarial intrinsic motivation for reinforcement learning I Durugkar, M Tec, S Niekum, P Stone Advances in Neural Information Processing Systems 34, 8622-8636, 2021	4	2021
Unmixing in the presence of nuisances with deep generative models M Parente, I Gemp, I Durugkar 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2017	4	2017
Multi-preference actor critic I Durugkar, M Hausknecht, A Swaminathan, P MacAlpine arXiv preprint arXiv:1904.03295, 2019	2	2019
Inverting variational autoencoders for improved generative accuracy I Gemp, I Durugkar, M Parente, MD Dyar, S Mahadevan arXiv preprint arXiv:1608.05983, 2016	2	2016
An imitation from observation approach to sim-to-real transfer S Desai, I Durugkar, H Karnan, G Warnell, J Hanna, P Stone, AI Sony 2nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics. RSS, 2020	1	2020
Adversarial goal generation for intrinsic motivation I Durugkar, P Stone Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	1	2018
DM: Distributed Multi-Agent Reinforcement Learning for Distribution Matching C Wang, I Durugkar, E Liebman, P Stone arXiv preprint arXiv:2206.00233, 2022		2022
Wasserstein Distance Maximizing Intrinsic Control I Durugkar, S Hansen, S Spencer, V Mnih arXiv preprint arXiv:2110.15331, 2021		2021
HR-TD: A Regularized TD Method to Avoid Over-Generalization I Durugkar, B Liu, P Stone		2018
REASONING OVER PATHS IN KNOWLEDGE BASES USING REINFORCEMENT LEARNING R Das, S Dhuliawala, M Zaheer, L Vilnis, I Durugkar, A Krishnamurthy, ... arXiv preprint arXiv:1711.05851, 2017		2017
Deep Generative Models for Spectroscopic Analysis on Mars I Gemp, I Durugkar, M Parente, S Mahadevan CoRR, 2016		2016

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