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ORCIDKirthevasan Kandasamy
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2020 – today
- 2024
[c30]Joon Suk Huh, Kirthevasan Kandasamy:
Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning. ICML 2024- [i29]Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy:
Bandit Profit-maximization for Targeted Marketing. CoRR abs/2403.01361 (2024) - [i28]Joon Suk Huh, Kirthevasan Kandasamy:
Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning. CoRR abs/2407.04898 (2024) - [i27]Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy:
Learning to Price Homogeneous Data. CoRR abs/2407.05484 (2024) - [i26]Alex Clinton, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy:
Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents. CoRR abs/2407.15881 (2024) - 2023
- [j4]Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback. J. Mach. Learn. Res. 24: 53:1-53:45 (2023) - [c29]Ting Cai
, Kirthevasan Kandasamy:
Active Cost-aware Labeling of Streaming Data. AISTATS 2023: 9117-9136 - [c28]Yiding Chen, Jerry Zhu, Kirthevasan Kandasamy:
Mechanism Design for Collaborative Normal Mean Estimation. NeurIPS 2023 - [c27]Romil Bhardwaj, Kirthevasan Kandasamy, Asim Biswal, Wenshuo Guo, Benjamin Hindman, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback. OSDI 2023: 623-643 - [c26]Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik:
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. EC 2023: 816 - [i25]Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik:
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. CoRR abs/2302.09700 (2023) - [i24]Ting Cai, Kirthevasan Kandasamy:
Active Cost-aware Labeling of Streaming Data. CoRR abs/2304.06808 (2023) - [i23]Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy:
Mechanism Design for Collaborative Normal Mean Estimation. CoRR abs/2306.06351 (2023) - 2022
- [c25]Wenshuo Guo, Kirthevasan Kandasamy, Joseph Gonzalez, Michael I. Jordan, Ion Stoica:
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback. AISTATS 2022: 6200-6224 - 2021
- [c24]Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Michael I. Jordan, Ion Stoica, Joseph E. Gonzalez:
Elastic Hyperparameter Tuning on the Cloud. SoCC 2021: 33-46 - [c23]Ujval Misra, Richard Liaw, Lisa Dunlap, Romil Bhardwaj, Kirthevasan Kandasamy, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov:
RubberBand: cloud-based hyperparameter tuning. EuroSys 2021: 327-342 - [c22]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph Gonzalez:
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism. ICML 2021: 10236-10246 - [i22]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez:
PAC Best Arm Identification Under a Deadline. CoRR abs/2106.03221 (2021) - [i21]Wenshuo Guo, Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Online Learning of Competitive Equilibria in Exchange Economies. CoRR abs/2106.06616 (2021) - 2020
- [j3]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. 21: 81:1-81:27 (2020) - [c21]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS 2020: 3393-3403 - [i20]Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider:
Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR abs/2001.01793 (2020) - [i19]Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Sven Burke, Biswajit Paria, Barnabás Póczos, Jay Whitacre, Venkatasubramanian Viswanathan:
Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning. CoRR abs/2001.09938 (2020) - [i18]Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Mechanism Design with Bandit Feedback. CoRR abs/2004.08924 (2020) - [i17]Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I. Jordan, Ken Goldberg, Joseph E. Gonzalez:
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism. CoRR abs/2011.00330 (2020) - [i16]Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica:
Online Learning Demands in Max-min Fairness. CoRR abs/2012.08648 (2020)
2010 – 2019
- 2019
- [b1]Kirthevasan Kandasamy:
Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation. Carnegie Mellon University, USA, 2019 - [j2]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Gaussian Process Bandit Optimisation. J. Artif. Intell. Res. 66: 151-196 (2019) - [c20]Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai:
Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach. AISTATS 2019: 2096-2105 - [c19]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. ICML 2019: 3222-3232 - [c18]Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen:
Offline Contextual Bayesian Optimization. NeurIPS 2019: 4629-4640 - [c17]Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos:
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations. UAI 2019: 766-776 - [i15]Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR abs/1901.11515 (2019) - [i14]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR abs/1903.06694 (2019) - [i13]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR abs/1908.01425 (2019) - 2018
- [c16]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Parallelised Bayesian Optimisation via Thompson Sampling. AISTATS 2018: 133-142 - [c15]Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai:
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions. ICML 2018: 4545-4554 - [c14]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS 2018: 2020-2029 - [i12]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR abs/1802.07191 (2018) - [i11]Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming. CoRR abs/1805.09964 (2018) - [i10]Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos:
A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations. CoRR abs/1805.12168 (2018) - [i9]Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai:
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach. CoRR abs/1810.10482 (2018) - 2017
- [j1]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
Query efficient posterior estimation in scientific experiments via Bayesian active learning. Artif. Intell. 243: 45-56 (2017) - [c13]Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter:
Batch Policy Gradient Methods for Improving Neural Conversation Models. ICLR (Poster) 2017 - [c12]Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Bayesian Optimisation with Continuous Approximations. ICML 2017: 1799-1808 - [i8]Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter:
Batch Policy Gradient Methods for Improving Neural Conversation Models. CoRR abs/1702.03334 (2017) - [i7]Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff G. Schneider, Barnabás Póczos:
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling. CoRR abs/1705.09236 (2017) - 2016
- [c11]Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider:
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models. AISTATS 2016: 884-892 - [c10]Kirthevasan Kandasamy, Yaoliang Yu:
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. ICML 2016: 69-78 - [c9]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. NIPS 2016: 992-1000 - [c8]Kirthevasan Kandasamy, Gautam Dasarathy, Barnabás Póczos, Jeff G. Schneider:
The Multi-fidelity Multi-armed Bandit. NIPS 2016: 1777-1785 - [c7]Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing:
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. NIPS 2016: 2865-2873 - [i6]Kirthevasan Kandasamy, Yaoliang Yu:
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA. CoRR abs/1602.00287 (2016) - [i5]Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos:
Multi-fidelity Gaussian Process Bandit Optimisation. CoRR abs/1603.06288 (2016) - [i4]Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing:
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. CoRR abs/1609.06390 (2016) - [i3]Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos:
The Multi-fidelity Multi-armed Bandit. CoRR abs/1610.09726 (2016) - 2015
- [c6]Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
On Estimating L22 Divergence. AISTATS 2015 - [c5]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. ICML 2015: 295-304 - [c4]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper. IJCAI 2015: 3605-3611 - [c3]Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins:
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. NIPS 2015: 397-405 - [i2]Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos:
High Dimensional Bayesian Optimisation and Bandits via Additive Models. CoRR abs/1503.01673 (2015) - 2014
- [c2]Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
Nonparametric Estimation of Renyi Divergence and Friends. ICML 2014: 919-927 - [i1]Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins:
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. CoRR abs/1411.4342 (2014) - 2012
- [c1]Kirthevasan Kandasamy:
Latent Beta Topographic Mapping. ICTAI 2012: 138-145
Coauthor Index
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