Kirthevasan Kandasamy

According to our database, Kirthevasan Kandasamy authored at least 52 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents.

Alex Clinton, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy

CoRR, 2024

Learning to Price Homogeneous Data.

Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy

CoRR, 2024

Bandit Profit-maximization for Targeted Marketing.

Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy

CoRR, 2024

Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning.

Joon Suk Huh, Kirthevasan Kandasamy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Mechanism Design for Collaborative Normal Mean Estimation.

Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy

CoRR, 2023

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty.

Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik

Proceedings of the 24th ACM Conference on Economics and Computation, 2023

Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback.

Romil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez, Michael I. Jordan, Ion Stoica

Proceedings of the 17th USENIX Symposium on Operating Systems Design and Implementation, 2023

Mechanism Design for Collaborative Normal Mean Estimation.

Yiding Chen, Jerry Zhu, Kirthevasan Kandasamy

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Active Cost-aware Labeling of Streaming Data.

Ting Cai, Kirthevasan Kandasamy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Learning Competitive Equilibria in Exchange Economies with Bandit Feedback.

Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Online Learning of Competitive Equilibria in Exchange Economies.

Wenshuo Guo, Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica

CoRR, 2021

PAC Best Arm Identification Under a Deadline.

Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez

CoRR, 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

Proceedings of the 38th International Conference on Machine Learning, 2021

RubberBand: cloud-based hyperparameter tuning.

Ujval Misra, Richard Liaw, Lisa Dunlap, Romil Bhardwaj, Kirthevasan Kandasamy, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

Proceedings of the EuroSys '21: Sixteenth European Conference on Computer Systems, 2021

Elastic Hyperparameter Tuning on the Cloud.

Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez

Proceedings of the SoCC '21: ACM Symposium on Cloud Computing, 2021

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, Barnabás Póczos, Eric P. Xing

J. Mach. Learn. Res., 2020

Online Learning Demands in Max-min Fairness.

Kirthevasan Kandasamy, Gur-Eyal Sela, 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

Mechanism Design with Bandit Feedback.

Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica

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

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

CoRR, 2020

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations.

Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation.

Kirthevasan Kandasamy

PhD thesis, 2019

Multi-fidelity Gaussian Process Bandit Optimisation.

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

J. Artif. Intell. Res., 2019

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization.

Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing

CoRR, 2019

A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations.

Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Offline Contextual Bayesian Optimization.

Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Proceedings of the 36th International Conference on Machine Learning, 2019

Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach.

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach.

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

CoRR, 2018

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations.

Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos

CoRR, 2018

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming.

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

CoRR, 2018

Neural Architecture Search with Bayesian Optimisation and Optimal Transport.

Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions.

Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai

Proceedings of the 35th International Conference on Machine Learning, 2018

Parallelised Bayesian Optimisation via Thompson Sampling.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos

CoRR, 2017

Query efficient posterior estimation in scientific experiments via Bayesian active learning.

Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos

Artif. Intell., 2017

Multi-fidelity Bayesian Optimisation with Continuous Approximations.

Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 34th International Conference on Machine Learning, 2017

Batch Policy Gradient Methods for Improving Neural Conversation Models.

Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

The Multi-fidelity Multi-armed Bandit.

Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations.

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices.

Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA.

Kirthevasan Kandasamy, Yaoliang Yu

Proceedings of the 33nd International Conference on Machine Learning, 2016

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models.

Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations.

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper.

Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

High Dimensional Bayesian Optimisation and Bandits via Additive Models.

Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos

Proceedings of the 32nd International Conference on Machine Learning, 2015

On Estimating L22 Divergence.

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

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

Nonparametric Estimation of Renyi Divergence and Friends.

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman

Proceedings of the 31th International Conference on Machine Learning, 2014

2012

Latent Beta Topographic Mapping.

Kirthevasan Kandasamy

Proceedings of the IEEE 24th International Conference on Tools with Artificial Intelligence, 2012