Latent Beta Topographic Mapping. - Kirthevasan Kandasamy (ICTAI, 2012)

Nonparametric Estimation of Renyi Divergence and Friends. - Akshay Krishnamurthy; Kirthevasan Kandasamy; Barnabás Póczos; Larry A. Wasserman (ICML, 2014)

Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Barnabás Póczos; Larry A. Wasserman; James M. Robins (CoRR, 2014)

On Estimating L22 Divergence. - Akshay Krishnamurthy; Kirthevasan Kandasamy; Barnabás Póczos; Larry A. Wasserman (AISTATS, 2015)

High Dimensional Bayesian Optimisation and Bandits via Additive Models. - Kirthevasan Kandasamy; Jeff G. Schneider; Barnabás Póczos (ICML, 2015)

Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. - Kirthevasan Kandasamy; Jeff G. Schneider; Barnabás Póczos (IJCAI, 2015)

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Barnabás Póczos; Larry A. Wasserman; James M. Robins (NIPS, 2015)

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. - Chun-Liang Li; Kirthevasan Kandasamy; Barnabás Póczos; Jeff G. Schneider (AISTATS, 2016)

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. - Kirthevasan Kandasamy; Yaoliang Yu (ICML, 2016)

Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. - Kirthevasan Kandasamy; Maruan Al-Shedivat; Eric P. Xing (NIPS, 2016)

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. - Kirthevasan Kandasamy; Gautam Dasarathy; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (NIPS, 2016)

The Multi-fidelity Multi-armed Bandit. - Kirthevasan Kandasamy; Gautam Dasarathy; Barnabás Póczos; Jeff G. Schneider (NIPS, 2016)

Batch Policy Gradient Methods for Improving Neural Conversation Models. - Kirthevasan Kandasamy; Yoram Bachrach; Ryota Tomioka; Daniel Tarlow; David Carter (ICLR (Poster), 2017)

Multi-fidelity Bayesian Optimisation with Continuous Approximations. - Kirthevasan Kandasamy; Gautam Dasarathy; Jeff G. Schneider; Barnabás Póczos (ICML, 2017)

Query efficient posterior estimation in scientific experiments via Bayesian active learning. - Kirthevasan Kandasamy; Jeff G. Schneider; Barnabás Póczos (Artif. Intell., 2017)

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Jeff G. Schneider; Barnabás Póczos (CoRR, 2017)

Parallelised Bayesian Optimisation via Thompson Sampling. - Kirthevasan Kandasamy; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (AISTATS, 2018)

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions. - Rajat Sen; Kirthevasan Kandasamy; Sanjay Shakkottai (ICML, 2018)

Neural Architecture Search with Bayesian Optimisation and Optimal Transport. - Kirthevasan Kandasamy; Willie Neiswanger; Jeff Schneider 001; Barnabás Póczos; Eric P. Xing (NeurIPS, 2018)

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (CoRR, 2018)

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations. - Biswajit Paria; Kirthevasan Kandasamy; Barnabás Póczos (CoRR, 2018)

Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach. - Rajat Sen; Kirthevasan Kandasamy; Sanjay Shakkottai (CoRR, 2018)

Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach. - Rajat Sen; Kirthevasan Kandasamy; Sanjay Shakkottai (AISTATS, 2019)

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. - Kirthevasan Kandasamy; Willie Neiswanger; Reed Zhang; Akshay Krishnamurthy; Jeff Schneider 001; Barnabás Póczos (ICML, 2019)

Offline Contextual Bayesian Optimization. - Ian Char; Youngseog Chung; Willie Neiswanger; Kirthevasan Kandasamy; Oak Nelson; Mark D. Boyer; Egemen Kolemen (NeurIPS, 2019)

A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations. - Biswajit Paria; Kirthevasan Kandasamy; Barnabás Póczos (UAI, 2019)

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. - Willie Neiswanger; Kirthevasan Kandasamy; Barnabás Póczos; Jeff Schneider 001; Eric P. Xing (CoRR, 2019)

Multi-fidelity Gaussian Process Bandit Optimisation. - Kirthevasan Kandasamy; Gautam Dasarathy; Junier B. Oliva; Jeff G. Schneider; Barnabás Póczos (J. Artif. Intell. Res., 2019)

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation. - Kirthevasan Kandasamy (2019)

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. - Ksenia Korovina; Sailun Xu; Kirthevasan Kandasamy; Willie Neiswanger; Barnabás Póczos; Jeff Schneider 001; Eric P. Xing (AISTATS, 2020)

Offline Contextual Bayesian Optimization for Nuclear Fusion. - Youngseog Chung; Ian Char; Willie Neiswanger; Kirthevasan Kandasamy; Andrew Oakleigh Nelson; Mark D. Boyer; Egemen Kolemen; Jeff Schneider 001 (CoRR, 2020)

Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning. - Adarsh Dave; Jared Mitchell; Kirthevasan Kandasamy; Sven Burke; Biswajit Paria; Barnabás Póczos; Jay Whitacre; Venkatasubramanian Viswanathan (CoRR, 2020)

Mechanism Design with Bandit Feedback. - Kirthevasan Kandasamy; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (CoRR, 2020)

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism. - Brijen Thananjeyan; Kirthevasan Kandasamy; Ion Stoica; Michael I. Jordan; Ken Goldberg; Joseph E. Gonzalez (CoRR, 2020)

Online Learning Demands in Max-min Fairness. - Kirthevasan Kandasamy; Gur-Eyal Sela; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (CoRR, 2020)

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. - Kirthevasan Kandasamy; Karun Raju Vysyaraju; Willie Neiswanger; Biswajit Paria; Christopher R. Collins; Jeff Schneider 001; Barnabás Póczos; Eric P. Xing (J. Mach. Learn. Res., 2020)

Elastic Hyperparameter Tuning on the Cloud. - Lisa Dunlap; Kirthevasan Kandasamy; Ujval Misra; Richard Liaw; Michael I. Jordan; Ion Stoica; Joseph E. Gonzalez (SoCC, 2021)

RubberBand: cloud-based hyperparameter tuning. - Ujval Misra; Richard Liaw; Lisa Dunlap; Romil Bhardwaj; Kirthevasan Kandasamy; Joseph E. Gonzalez; Ion Stoica; Alexey Tumanov (EuroSys, 2021)

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. - Brijen Thananjeyan; Kirthevasan Kandasamy; Ion Stoica; Michael I. Jordan; Ken Goldberg; Joseph Gonzalez 001 (ICML, 2021)

PAC Best Arm Identification Under a Deadline. - Brijen Thananjeyan; Kirthevasan Kandasamy; Ion Stoica; Michael I. Jordan; Ken Goldberg; Joseph E. Gonzalez (CoRR, 2021)

Online Learning of Competitive Equilibria in Exchange Economies. - Wenshuo Guo; Kirthevasan Kandasamy; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (CoRR, 2021)

Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. - Wenshuo Guo; Kirthevasan Kandasamy; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (AISTATS, 2022)

Active Cost-aware Labeling of Streaming Data. - Ting Cai 003; Kirthevasan Kandasamy (AISTATS, 2023)

Mechanism Design for Collaborative Normal Mean Estimation. - Yiding Chen; Jerry Zhu; Kirthevasan Kandasamy (NeurIPS, 2023)

Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. - Romil Bhardwaj; Kirthevasan Kandasamy; Asim Biswal; Wenshuo Guo; Benjamin Hindman; Joseph Gonzalez 001; Michael I. Jordan; Ion Stoica (OSDI, 2023)

Active Cost-aware Labeling of Streaming Data. - Ting Cai; Kirthevasan Kandasamy (CoRR, 2023)

Mechanism Design for Collaborative Normal Mean Estimation. - Yiding Chen; Xiaojin Zhu 001; Kirthevasan Kandasamy (CoRR, 2023)

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. - Kirthevasan Kandasamy; Joseph E. Gonzalez; Michael I. Jordan; Ion Stoica (J. Mach. Learn. Res., 2023)

Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning. - Joon Suk Huh; Kirthevasan Kandasamy (ICML, 2024)

Learning to Price Homogeneous Data. - Keran Chen; Joon Suk Huh; Kirthevasan Kandasamy (NeurIPS, 2024)

Bandit Profit-Maximization for Targeted Marketing. - Joon Suk Huh; Ellen Vitercik; Kirthevasan Kandasamy (EC, 2024)

Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents. - Alex Clinton; Yiding Chen; Xiaojin Zhu 001; Kirthevasan Kandasamy (CoRR, 2024)

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. - Wenshuo Guo; Nika Haghtalab; Kirthevasan Kandasamy; Ellen Vitercik (SIGecom Exch., 2024)

Incentivizing Truthful Data Contributions in a Marketplace for Mean Estimation. - Keran Chen; Alexander Clinton; Kirthevasan Kandasamy (CoRR, 2025)

Balancing Performance and Costs in Best Arm Identification. - Michael O. Harding; Kirthevasan Kandasamy (CoRR, 2025)

A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing. - Alex Clinton; Thomas Zeng 003; Yiding Chen; Xiaojin Zhu 001; Kirthevasan Kandasamy (CoRR, 2025)