Articles with public access mandates - Shivaram KalyanakrishnanLearn more
Available somewhere: 15
Half field offense: An environment for multiagent learning and ad hoc teamwork
M Hausknecht, P Mupparaju, S Subramanian, S Kalyanakrishnan, ...
AAMAS Adaptive Learning Agents (ALA) Workshop 3, 2016
Mandates: US National Science Foundation
Opportunities and challenges for artificial intelligence in India
S Kalyanakrishnan, RA Panicker, S Natarajan, S Rao
Proceedings of the 2018 AAAI/ACM conference on AI, Ethics, and Society, 164-170, 2018
Mandates: Department of Science & Technology, India
PAC identification of a bandit arm relative to a reward quantile
AR Chaudhuri, S Kalyanakrishnan
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
Mandates: Department of Science & Technology, India
Pac identification of many good arms in stochastic multi-armed bandits
AR Chaudhuri, S Kalyanakrishnan
International Conference on Machine Learning, 991-1000, 2019
Mandates: Department of Science & Technology, India
Regret minimisation in multi-armed bandits using bounded arm memory
AR Chaudhuri, S Kalyanakrishnan
Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10085 …, 2020
Mandates: Department of Science & Technology, India
Quantile-Regret Minimisation in Infinitely Many-Armed Bandits.
AR Chaudhuri, S Kalyanakrishnan
UAI, 425-434, 2018
Mandates: Department of Science & Technology, India
Gev-canonical regression for accurate binary class probability estimation when one class is rare
A Agarwal, H Narasimhan, S Kalyanakrishnan, S Agarwal
International Conference on Machine Learning, 1989-1997, 2014
Mandates: Department of Science & Technology, India
Randomised procedures for initialising and switching actions in policy iteration
S Kalyanakrishnan, N Misra, A Gopalan
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
Mandates: Department of Science & Technology, India
A tighter analysis of randomised policy iteration
M Taraviya, S Kalyanakrishnan
Uncertainty in Artificial Intelligence, 519-529, 2020
Mandates: Department of Science & Technology, India
Batch-Switching Policy Iteration.
S Kalyanakrishnan, U Mall, R Goyal
IJCAI, 3147-3153, 2016
Mandates: Department of Science & Technology, India
PAC mode estimation using PPR martingale confidence sequences
SA Jain, R Shah, S Gupta, D Mehta, IJ Nair, J Vora, S Khyalia, S Das, ...
International Conference on Artificial Intelligence and Statistics, 5815-5852, 2022
Mandates: Department of Science & Technology, India
Optimising a real-time scheduler for Indian railway lines by policy search
R Prasad, H Khadilkar, S Kalyanakrishnan
2021 Seventh Indian Control Conference (ICC), 75-80, 2021
Mandates: Department of Science & Technology, India
Lower bounds for policy iteration on multi-action mdps
K Ashutosh, S Consul, B Dedhia, P Khirwadkar, S Shah, ...
2020 59th ieee conference on decision and control (cdc), 1744-1749, 2020
Mandates: Department of Science & Technology, India
Optimal Stopping Rules for Best Arm Identification in Stochastic Bandits under Uniform Sampling
V Gupta, Y Gadhia, S Kalyanakrishnan, N Karamchandani
2024 IEEE International Symposium on Information Theory (ISIT), 2299-2304, 2024
Mandates: Department of Science & Technology, India
Intelligent and Learning Agents: Four Investigations.
S Kalyanakrishnan
IJCAI, 4946-4950, 2021
Mandates: Department of Science & Technology, India
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