Kirthevasan Kandasamy

77 papers A* 18A 9B 1Journal 42Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Constrained Best Arm Identification with Tests for Feasibility. AAAI Ting Cai, Kirthevasan Kandasamy
2026	J	`jnl`	Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget. CoRR Michael O. Harding, Vikas Singh, Kirthevasan Kandasamy
2026	J	`jnl`	Pairwise Exchanges of Freely Replicable Goods with Negative Externalities. CoRR Shangyuan Yang, Kirthevasan Kandasamy
2025	J	`jnl`	A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing. CoRR Alex Clinton, Thomas Zeng, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
2025	J	`jnl`	Agora: Bridging the GPU Cloud Resource-Price Disconnect. CoRR Ian McDougall, Noah Scott, Joon Huh, Kirthevasan Kandasamy, Karthikeyan Sankaralingam
2025	J	`jnl`	Balancing Performance and Costs in Best Arm Identification. CoRR Michael O. Harding, Kirthevasan Kandasamy
2025	A*	`conf`	Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution. ICML Alex Clinton, Yiding Chen, Jerry Zhu, Kirthevasan Kandasamy
2025	J	`jnl`	Constrained Best Arm Identification with Tests for Feasibility. CoRR Ting Cai, Kirthevasan Kandasamy
2025	J	`jnl`	Incentivizing Truthful Data Contributions in a Marketplace for Mean Estimation. CoRR Keran Chen, Alexander Clinton, Kirthevasan Kandasamy
2025	J	`jnl`	Strategy-robust Online Learning in Contextual Pricing. CoRR Joon Suk Huh, Kirthevasan Kandasamy
2024	A*	`conf`	Bandit Profit-Maximization for Targeted Marketing. EC Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
2024	J	`jnl`	Bandit Profit-maximization for Targeted Marketing. CoRR Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
2024	J	`jnl`	Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents. CoRR Alex Clinton, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
2024	A*	`conf`	Learning to Price Homogeneous Data. NeurIPS Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy
2024	J	`jnl`	Learning to Price Homogeneous Data. CoRR Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy
2024	J	`jnl`	Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. SIGecom Exch. Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik
2024	A*	`conf`	Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning. ICML Joon Suk Huh, Kirthevasan Kandasamy
2024	J	`jnl`	Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning. CoRR Joon Suk Huh, Kirthevasan Kandasamy
2023	A	`conf`	Active Cost-aware Labeling of Streaming Data. AISTATS Ting Cai, Kirthevasan Kandasamy
2023	J	`jnl`	Active Cost-aware Labeling of Streaming Data. CoRR Ting Cai, Kirthevasan Kandasamy
2023	A*	`conf`	Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. OSDI Romil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez, Michael I. Jordan, Ion Stoica
2023	A*	`conf`	Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. EC Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik
2023	J	`jnl`	Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. CoRR Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik
2023	A*	`conf`	Mechanism Design for Collaborative Normal Mean Estimation. NeurIPS Yiding Chen, Jerry Zhu, Kirthevasan Kandasamy
2023	J	`jnl`	Mechanism Design for Collaborative Normal Mean Estimation. CoRR Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
2023	J	`jnl`	VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. J. Mach. Learn. Res. Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2022	A	`conf`	Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica
2021	—	`conf`	Elastic Hyperparameter Tuning on the Cloud. SoCC Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez
2021	J	`jnl`	Online Learning of Competitive Equilibria in Exchange Economies. CoRR Wenshuo Guo, Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2021	J	`jnl`	PAC Best Arm Identification Under a Deadline. CoRR Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez
2021	A*	`conf`	Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. ICML Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph Gonzalez
2021	A	`conf`	RubberBand: cloud-based hyperparameter tuning. EuroSys Ujval Misra, Richard Liaw, Lisa Dunlap, Romil Bhardwaj, Kirthevasan Kandasamy, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov
2020	J	`jnl`	Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning. CoRR Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Sven Burke, Biswajit Paria, Barnabás Póczos, Jay Whitacre, Venkatasubramanian Viswanathan
2020	A	`conf`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2020	J	`jnl`	Mechanism Design with Bandit Feedback. CoRR Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2020	J	`jnl`	Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider
2020	J	`jnl`	Online Learning Demands in Max-min Fairness. CoRR Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica
2020	J	`jnl`	Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism. CoRR Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez
2020	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2019	A	`conf`	A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations. UAI Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
2019	J	`jnl`	ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	J	`jnl`	Multi-fidelity Gaussian Process Bandit Optimisation. J. Artif. Intell. Res. Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
2019	A*	`conf`	Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2019	A	`conf`	Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach. AISTATS Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
2019	A*	`conf`	Offline Contextual Bayesian Optimization. NeurIPS Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen
2019	J	`jnl`	ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing
2019	J	`jnl`	Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2019	—	`—`	Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation. Kirthevasan Kandasamy
2018	J	`jnl`	A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations. CoRR Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
2018	A*	`conf`	Multi-Fidelity Black-Box Optimization with Hierarchical Partitions. ICML Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
2018	J	`jnl`	Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2018	A*	`conf`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing
2018	J	`jnl`	Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach. CoRR Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
2018	A	`conf`	Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos
2017	J	`jnl`	Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. CoRR Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos
2017	—	`conf`	Batch Policy Gradient Methods for Improving Neural Conversation Models. ICLR (Poster) Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
2017	J	`jnl`	Batch Policy Gradient Methods for Improving Neural Conversation Models. CoRR Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
2017	A*	`conf`	Multi-fidelity Bayesian Optimisation with Continuous Approximations. ICML Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos
2017	J	`jnl`	Query efficient posterior estimation in scientific experiments via Bayesian active learning. Artif. Intell. Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2016	A*	`conf`	Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML Kirthevasan Kandasamy, Yaoliang Yu
2016	J	`jnl`	Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. CoRR Kirthevasan Kandasamy, Yaoliang Yu
2016	—	`conf`	Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. NIPS Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
2016	A	`conf`	High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. AISTATS Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider
2016	—	`conf`	Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. NIPS Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing
2016	J	`jnl`	Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. CoRR Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing
2016	J	`jnl`	Multi-fidelity Gaussian Process Bandit Optimisation. CoRR Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
2016	—	`conf`	The Multi-fidelity Multi-armed Bandit. NIPS Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider
2016	J	`jnl`	The Multi-fidelity Multi-armed Bandit. CoRR Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos
2015	A*	`conf`	Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. IJCAI Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2015	A*	`conf`	High Dimensional Bayesian Optimisation and Bandits via Additive Models. ICML Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2015	J	`jnl`	High Dimensional Bayesian Optimisation and Bandits via Additive Models. CoRR Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
2015	—	`conf`	Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. NIPS Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
2015	A	`conf`	On Estimating L22 Divergence. AISTATS Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman
2014	J	`jnl`	Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. CoRR Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
2014	A*	`conf`	Nonparametric Estimation of Renyi Divergence and Friends. ICML Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman
2012	B	`conf`	Latent Beta Topographic Mapping. ICTAI Kirthevasan Kandasamy