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ORCIDShivaram Kalyanakrishnan
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2020 – today
- 2024
[c32]Hastyn Doshi, Ayush Tripathi, Keshav Agarwal, Harshad Khadilkar, Shivaram Kalyanakrishnan:
Linear-Time Optimal Deadlock Detection for Efficient Scheduling in Multi-Track Railway Networks. IJCAI 2024: 5799-5807- [c31]Vedang Gupta, Yash Gadhia, Shivaram Kalyanakrishnan, Nikhil Karamchandani:
Optimal Stopping Rules for Best Arm Identification in Stochastic Bandits under Uniform Sampling. ISIT 2024: 2299-2304 - 2022
- [c30]Shubham Anand Jain, Rohan Shah, Sanit Gupta, Denil Mehta, Inderjeet J. Nair, Jian Vora, Sushil Khyalia, Sourav Das, Vinay J. Ribeiro, Shivaram Kalyanakrishnan:
PAC Mode Estimation using PPR Martingale Confidence Sequences. AISTATS 2022: 5815-5852 - [i7]Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David C. Parkes, William H. Press
, AnnaLee Saxenian, Julie Shah, Milind Tambe, Astro Teller:
Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence. CoRR abs/2211.06318 (2022) - [i6]Ritesh Goenka, Eashan Gupta, Sushil Khyalia, Pratyush Agarwal, Mulinti Shaik Wajid, Shivaram Kalyanakrishnan:
Some Upper Bounds on the Running Time of Policy Iteration on Deterministic MDPs. CoRR abs/2211.15602 (2022) - 2021
- [c29]Shivaram Kalyanakrishnan:
Intelligent and Learning Agents: Four Investigations. IJCAI 2021: 4946-4950 - [c28]Gino Perrotta, Ryan W. Gardner, Corey Lowman, Mohammad Taufeeque, Nitish Tongia, Shivaram Kalyanakrishnan, Gregory Clark, Kevin Wang, Eitan Rothberg, Brady P. Garrison, Prithviraj Dasgupta, Callum Canavan, Lucas McCabe:
The Second NeurIPS Tournament of Reconnaissance Blind Chess. NeurIPS (Competition and Demos) 2021: 53-65 - [c27]Ryan W. Gardner, Gino Perrotta, Anvay Shah, Shivaram Kalyanakrishnan, Kevin A. Wang, Gregory Clark, Timo Bertram, Johannes Fürnkranz, Martin Müller, Brady P. Garrison, Prithviraj Dasgupta, Saeid Rezaei:
The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022. NeurIPS (Competition and Demos) 2021: 119-132 - [i5]Shivaram Kalyanakrishnan, Siddharth Aravindan, Vishwajeet Bagdawat, Varun Bhatt, Harshith Goka, Archit Gupta, Kalpesh Krishna, Vihari Piratla:
An Analysis of Frame-skipping in Reinforcement Learning. CoRR abs/2102.03718 (2021) - 2020
- [c26]Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory. AAAI 2020: 10085-10092 - [c25]Kumar Ashutosh, Sarthak Consul, Bhishma Dedhia, Parthasarathi Khirwadkar, Sahil Shah, Shivaram Kalyanakrishnan:
Lower Bounds for Policy Iteration on Multi-action MDPs. CDC 2020: 1744-1749 - [i4]Kumar Ashutosh, Sarthak Consul, Bhishma Dedhia, Parthasarathi Khirwadkar, Sahil Shah, Shivaram Kalyanakrishnan:
Lower Bounds for Policy Iteration on Multi-action MDPs. CoRR abs/2009.07842 (2020)
2010 – 2019
- 2019
- [c24]Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits. ICML 2019: 991-1000 - [c23]Meet Taraviya, Shivaram Kalyanakrishnan:
A Tighter Analysis of Randomised Policy Iteration. UAI 2019: 519-529 - [i3]Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits. CoRR abs/1901.08386 (2019) - [i2]Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory. CoRR abs/1901.08387 (2019) - 2018
- [c22]Shivaram Kalyanakrishnan, Rahul Alex Panicker, Sarayu Natarajan, Shreya Rao:
Opportunities and Challenges for Artificial Intelligence in India. AIES 2018: 164-170 - [c21]Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
Quantile-Regret Minimisation in Infinitely Many-Armed Bandits. UAI 2018: 425-434 - 2017
- [c20]Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
PAC Identification of a Bandit Arm Relative to a Reward Quantile. AAAI 2017: 1777-1783 - [c19]Anchit Gupta, Shivaram Kalyanakrishnan:
Improved Strong Worst-case Upper Bounds for MDP Planning. IJCAI 2017: 1788-1794 - [i1]Jayvant Anantpur, Nagendra Dwarakanath Gulur, Shivaram Kalyanakrishnan, Shalabh Bhatnagar, R. Govindarajan:
RLWS: A Reinforcement Learning based GPU Warp Scheduler. CoRR abs/1712.04303 (2017) - 2016
- [j4]Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh:
AI's 10 to Watch. IEEE Intell. Syst. 31(1): 56-66 (2016) - [c18]Shivaram Kalyanakrishnan, Neeldhara Misra, Aditya Gopalan:
Randomised Procedures for Initialising and Switching Actions in Policy Iteration. AAAI 2016: 3145-3151 - [c17]Shivaram Kalyanakrishnan, Utkarsh Mall, Ritish Goyal:
Batch-Switching Policy Iteration. IJCAI 2016: 3147-3153 - 2014
- [j3]Ambarish Goswami, Seung-kook Yun, Umashankar Nagarajan, Sung-Hee Lee, KangKang Yin, Shivaram Kalyanakrishnan:
Direction-changing fall control of humanoid robots: theory and experiments. Auton. Robots 36(3): 199-223 (2014) - [c16]Shivaram Kalyanakrishnan, Deepthi Singh, Ravi Kant:
On Building Decision Trees from Large-scale Data in Applications of On-line Advertising. CIKM 2014: 669-678 - [c15]Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan, Shivani Agarwal:
GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare. ICML 2014: 1989-1997 - 2013
- [c14]Emilie Kaufmann, Shivaram Kalyanakrishnan:
Information Complexity in Bandit Subset Selection. COLT 2013: 228-251 - 2012
- [c13]Patrick MacAlpine, Daniel Urieli, Samuel Barrett, Shivaram Kalyanakrishnan, Francisco Barrera, Adrian Lopez-Mobilia, Nicolae Stiurca, Victor Vu, Peter Stone:
UT Austin Villa 2011: a champion agent in the RoboCup 3D soccer simulation competition. AAMAS 2012: 129-136 - [c12]Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, Peter Stone:
PAC Subset Selection in Stochastic Multi-armed Bandits. ICML 2012 - 2011
- [j2]Shivaram Kalyanakrishnan, Ambarish Goswami:
Learning to Predict Humanoid Fall. Int. J. Humanoid Robotics 8(2): 245-273 (2011) - [j1]Shivaram Kalyanakrishnan, Peter Stone:
Characterizing reinforcement learning methods through parameterized learning problems. Mach. Learn. 84(1-2): 205-247 (2011) - [c11]Shivaram Kalyanakrishnan, Peter Stone:
On learning with imperfect representations. ADPRL 2011: 17-24 - [c10]Daniel Urieli, Patrick MacAlpine, Shivaram Kalyanakrishnan, Yinon Bentor, Peter Stone:
On optimizing interdependent skills: a case study in simulated 3D humanoid robot soccer. AAMAS 2011: 769-776 - 2010
- [c9]Shivaram Kalyanakrishnan, Ambarish Goswami:
Predicting Falls of a Humanoid Robot through Machine Learning. IAAI 2010: 1793-1798 - [c8]Shivaram Kalyanakrishnan, Peter Stone:
Efficient Selection of Multiple Bandit Arms: Theory and Practice. ICML 2010: 511-518
2000 – 2009
- 2009
- [c7]Shivaram Kalyanakrishnan, Peter Stone:
An empirical analysis of value function-based and policy search reinforcement learning. AAMAS (2) 2009: 749-756 - [c6]Shivaram Kalyanakrishnan, Peter Stone:
Learning complementary multiagent behaviors: a case study. AAMAS (2) 2009: 1359-1360 - [c5]Shivaram Kalyanakrishnan, Todd Hester, Michael J. Quinlan, Yinon Bentor, Peter Stone:
Three Humanoid Soccer Platforms: Comparison and Synthesis. RoboCup 2009: 140-152 - [c4]Shivaram Kalyanakrishnan, Peter Stone:
Learning Complementary Multiagent Behaviors: A Case Study. RoboCup 2009: 153-165 - 2007
- [c3]Shivaram Kalyanakrishnan, Peter Stone:
Batch reinforcement learning in a complex domain. AAMAS 2007: 94 - [c2]Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu:
Model-Based Reinforcement Learning in a Complex Domain. RoboCup 2007: 171-183 - 2006
- [c1]Shivaram Kalyanakrishnan, Yaxin Liu, Peter Stone:
Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study. RoboCup 2006: 72-85
Coauthor Index
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