Shivaram Kalyanakrishnan

发表

Information Complexity in Bandit Subset Selection

Shivaram Kalyanakrishnan, Emilie Kaufmann, E. Kaufmann, 2013, COLT.

Learning Complementary Multiagent Behaviors: A Case Study

Peter Stone, Shivaram Kalyanakrishnan, P. Stone, 2009, RoboCup.

Optimizing Interdependent Skills for Simulated 3D Humanoid Robot Soccer

Daniel Urieli, Patrick MacAlpine, Peter Stone, 2011 .

Efficient Selection of Multiple Bandit Arms: Theory and Practice

Peter Stone, Shivaram Kalyanakrishnan, P. Stone, 2010, ICML.

Half Field Offense in RoboCup Soccer: A Multiagent Reinforcement Learning Case Study

Peter Stone, Shivaram Kalyanakrishnan, Yaxin Liu, 2006, RoboCup.

In Proc . UAI 2019 . To appear . A Tighter Analysis of Randomised Policy Iteration

Shivaram Kalyanakrishnan, Meet Taraviya, 2019 .

Randomised Procedures for Initialising and Switching Actions in Policy Iteration

Shivaram Kalyanakrishnan, Aditya Gopalan, N. Misra, 2016, AAAI.

Policy Iteration in POMDPs

Shivaram Kalyanakrishnan, Deep Karkhanis, 2020 .

Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence

Oren Etzioni, Julia Hirschberg, Milind Tambe, 2016 .

PAC Mode Estimation using PPR Martingale Confidence Sequences

Inderjeet Jayakumar Nair, Shivaram Kalyanakrishnan, Sourav Das, 2021, AISTATS.

Artificial Intelligence and Life in 2030: The One Hundred Year Study on Artificial Intelligence

Oren Etzioni, D. Parkes, R. Brooks, 2016, ArXiv.

Direction-changing fall control of humanoid robots: theory and experiments

KangKang Yin, A. Goswami, Shivaram Kalyanakrishnan, 2014, Auton. Robots.

Some Upper Bounds on the Running Time of Policy Iteration on Deterministic MDPs

Shivaram Kalyanakrishnan, Ritesh Goenka, Pratyush Agarwal, 2022, ArXiv.

An Analysis of Frame-skipping in Reinforcement Learning pdf

Shivaram Kalyanakrishnan, Varun Bhatt, Kalpesh Krishna, 2021, ArXiv.

Characterizing reinforcement learning methods through parameterized learning problems

Peter Stone, Shivaram Kalyanakrishnan, P. Stone, 2011, Machine Learning.

On learning with imperfect representations

Peter Stone, Shivaram Kalyanakrishnan, P. Stone, 2011, 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL).

Learning Methods for Sequential Decision Making with Imperfect Representations by Shivaram Kalyanakrishnan

Shivaram Kalyanakrishnan, 2011 .

GEV-Canonical Regression for Accurate Binary Class Probability Estimation when One Class is Rare

Arpit Agarwal, Harikrishna Narasimhan, Shivani Agarwal, 2014, ICML.

PAC Subset Selection in Stochastic Multi-armed Bandits

Ambuj Tewari, Peter Stone, Peter Auer, 2012, ICML.

On Building Decision Trees from Large-scale Data in Applications of On-line Advertising

Ravi Kant, Shivaram Kalyanakrishnan, Deepthi Singh, 2014, CIKM.

PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits

Shivaram Kalyanakrishnan, Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan, 2019, ICML.

Model-Based Reinforcement Learning in a Complex Domain

Peter Stone, Shivaram Kalyanakrishnan, Yaxin Liu, 2008, RoboCup.

Opportunities and Challenges for Artificial Intelligence in India

Shivaram Kalyanakrishnan, Rahul Alex Panicker, Sarayu Natarajan, 2018, AIES.

Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory

Shivaram Kalyanakrishnan, Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan, 2019, AAAI.

An empirical analysis of value function-based and policy search reinforcement learning

Peter Stone, Shivaram Kalyanakrishnan, P. Stone, 2009, AAMAS.

PAC Identification of a Bandit Arm Relative to a Reward Quantile

Shivaram Kalyanakrishnan, Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan, 2017, AAAI.

A Tighter Analysis of Randomised Policy Iteration

Shivaram Kalyanakrishnan, Meet Taraviya, Shivaram Kalyanakrishnan, 2019, UAI.

Batch reinforcement learning in a complex domain

Peter Stone, Shivaram Kalyanakrishnan, P. Stone, 2007, AAMAS '07.

UT Austin Villa 2011: a champion agent in the RoboCup 3D soccer simulation competition

Daniel Urieli, Patrick MacAlpine, Samuel Barrett, 2012, AAMAS.

Intelligent and Learning Agents: Four Investigations

Shivaram Kalyanakrishnan, Shivaram Kalyanakrishnan, 2021, IJCAI.

RLWS: A Reinforcement Learning based GPU Warp Scheduler pdf

R. Govindarajan, Shalabh Bhatnagar, Shivaram Kalyanakrishnan, 2017, ArXiv.

Lower Bounds for Policy Iteration on Multi-action MDPs

Parthasarathi Khirwadkar, Shivaram Kalyanakrishnan, Bhishma Dedhia, 2020, 2020 59th IEEE Conference on Decision and Control (CDC).

Quantile-Regret Minimisation in Infinitely Many-Armed Bandits

Shivaram Kalyanakrishnan, Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan, 2018, UAI.

Improved Strong Worst-case Upper Bounds for MDP Planning

Shivaram Kalyanakrishnan, Anchit Gupta, Shivaram Kalyanakrishnan, 2017, IJCAI.

Batch-Switching Policy Iteration

Shivaram Kalyanakrishnan, Utkarsh Mall, Ritish Goyal, 2016, IJCAI.