[preprints]   [publications]   [theses]   [invited talks]

 * Denotes joint lead authors,   ^αβ Denotes alphabetical order.

Preprints

Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents
Alex Clinton, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
[arxiv]

Online Learning Demands in Max-min Fairness
Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E Gonzalez, Michael I Jordan, Ion Stoica
[arxiv] [Simons talk: slides]

Publications

Learning to Price Homogeneous Data
Keran Chen, Joon Suk Huh, Kirthevasan Kandasamy
Advances in Neural Information Processing Systems (NeurIPS) 2024   [arxiv]

Bandit Profit Maximization for Targeted Marketing
Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
ACM Conference on Economics and Computation (EC) 2024   [arxiv]

Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning
Joon Suk Huh, Kirthevasan Kandasamy
International Conference on Machine Learning (ICML) 2024   [pdf]

Mechanism Design for Collaborative Normal Mean Estimation
Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
Advances in Neural Information Processing Systems (NeurIPS) 2023   [arxiv] [Stanford slides]

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
^αβ Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik
ACM Conference on Economics and Computation (EC) 2023   [arxiv] [EC talk: slides] [WSB talk: slides]
🏆 Exemplary Artificial Intelligence Track paper (link)

Cilantro: A Framework for Performance-Aware Resource Allocation for General Objectives via Online Feedback
Romil Bhardwaj *, Kirthevasan Kandasamy *, Wenshuo Guo, Benjamin Hindman, Joseph E Gonzalez, Michael I Jordan, Ion Stoica
Operating Systems Design and Implementation (OSDI) 2023   [pdf]

Active Cost-aware Labeling of Streaming Data
Ting Cai, Kirthevasan Kandasamy,
International Conference on Artificial Intelligence and Statistics (AISTATS) 2023   [pdf]

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica
Journal of Machine Learning Research (JMLR) 2022, to appear   [pdf]

Online Learning of Competitive Equilibria in Exchange Economies
Wenshuo Guo, Kirthevasan Kandasamy, Joseph E Gonzalez, Michael I Jordan, Ion Stoica
International Conference on Artificial Intelligence and Statistics (AISTATS) 2022   [arxiv]

Elastic Hyperparameter Tuning on the Cloud
Lisa Dunlap, Kirthevasan Kandasamy, Ujval Misra, Richard Liaw, Ion Stoica, Michael I Jordan Joseph E Gonzalez,
Symposium on Cloud Computing (SoCC) 2021   [pdf]

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 E Gonzalez
International Conference on Machine Learning (ICML) 2021   [arxiv] [Google talk: slides, video]

RubberBand: Cloud-based Hyperparameter Tuning
Ujval Misra, Richard Liaw, Lisa Dunlap, Romil Bhardwaj, Kirthevasan Kandasamy, Joseph E Gonzalez, Ion Stoica, Alexey Tumanov
European Conference on Computer Systems (EuroSys) 2021   [pdf]

Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning
Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy, Han Wang, Sven Burke, Biswajit Paria, Barnabas Poczos, Jay Whitacre, Venkatasubramaniam Viswanathan
Cell Reports - Physical Science 2020   [pdf] [code, data]

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, Barnabas Poczos, Eric P Xing
Journal of Machine Learning Research (JMLR) 2020   [pdf] [code, data]

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric P Xing
International Conference on Artificial Intelligence and Statistics (AISTATS) 2020   [arxiv] [code]

Offline Contextual Bayesian Optimization
Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Mark Boyer, Oak Nelson, Egemen Kolemen, Jeff Schneider
Advances in Neural Information Processing Systems (NeurIPS) 2019   [pdf - coming soon]

Multi-fidelity Gaussian Process Bandit Optimisation
Kirthevasan Kandasamy, Gautam Dasarathy, Junier B Oliva, Jeff Schneider, Barnabas Poczos
Journal of Artificial Intelligence Research (JAIR) 2019   [pdf] [code] [École slides]
Abridged version at:   Neural Information Processing Systems (NeurIPS) 2016   [pdf]

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning (ICML) 2019   [pdf] [code]

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations
Biswajit Paria, Kirthevasan Kandasamy, Barnabas Poczos
Conference on Uncertainty in Artificial Intelligence (UAI) 2019   [pdf] [code]

Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
International Conference on Artificial Intelligence and Statistics (AISTATS) 2019   [pdf]

Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric P Xing
Advances in Neural Information Processing Systems (NeurIPS) 2018   [pdf] [code, data] [spotlight video] [Uber slides]

Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
International Conference on Machine Learning (ICML) 2018   [pdf]

Parallelised Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, Barnabas Poczos
International Conference on Artificial Intelligence and Statistics (AISTATS) 2018   [pdf] [code] [AISTATS slides]

Multi-fidelity Bayesian Optimisation with Continuous Approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning (ICML) 2017   [pdf] [slides] [talk: video]

Batch Policy Gradient Methods for Improving Neural Conversation Models
Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
International Conference on Learning Representations (ICLR) 2017   [pdf]

Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos
Artificial Intelligence Journal (AIJ) 2017   [aij] [arxiv] [code (from David Fleming)]
Abridged version at:   International Joint Conference on Artificial Intelligence (IJCAI) 2015   [pdf]
🏆 IJCAI 2015 Distinguished Paper Award (Top 2 out of 1996 submissions, link)

The Multi-fidelity Multi-armed Bandit
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabas Poczos
Advances in Neural Information Processing Systems (NeurIPS) 2016   [pdf]

Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Kirthevasan Kandasamy *, Maruan Al-Shedivat *, Eric P Xing
Advances in Neural Information Processing Systems (NeurIPS) 2016   [pdf] [code]

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
Kirthevasan Kandasamy, Yaoliang Yu
International Conference on Machine Learning (ICML) 2016   [pdf] [code] [talk: video]

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models
Chun-Liang Li, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider
International Conference on Artificial Intelligence and Statistics (AISTATS) 2016   [pdf]

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, James M Robins
Advances in Neural Information Processing Systems (NeurIPS) 2015   [pdf]

High Dimensional Bayesian Optimisation and Bandits via Additive Models
Kirthevasan Kandasamy, Jeff Schneider, Barnabas Poczos
International Conference on Machine Learning (ICML) 2015   [pdf] [code] [talk: video, slides]

On Estimating L₂² Divergence
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Artificial Intelligence and Statistics (AISTATS) 2015   [pdf]

Nonparametric Estimation of Renyi-Divergence and Friends
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, Larry Wasserman
International Conference on Machine Learning (ICML) 2014   [pdf]

Latent Beta Topographic Mapping
Kirthevasan Kandasamy
International Conference on Tools with Artificial Intelligence 2012   [pdf]

Unpublished

PAC Best Arm Identification Under a Deadline
Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica, Michael I Jordan, Ken Goldberg, Joseph E Gonzalez
[arxiv] [Google talk: slides, video]

ProBO: A Framework for Using Probabilistic Programming in Bayesian Optimization
Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos Jeff Schneider, Eric P Xing
[arxiv]

Theses

Tuning Hyperparameters without Grad Students: Scaling Up Bandit Optimisation
Kirthevasan Kandasamy
School of Computer Science, Carnegie Mellon University, October 2018   [pdf]

Invited Talks

Data without Borders: Game-theoretic Challenges in Democratizing Data
Midwest Machine Learnining Symposium, Minneapolis, MN   May 2024    [slides]
Longer version at Wisconsin Statistics Seminar,   April 2024    [slides]

Mechanism Design for Collabrative Normal Mean Estimation
Stanford University, Palo Alto, CA   April 2024    [slides]

Frontiers in Machine Learning and Game Theory
University of Moratuwa, Moratuwa, Sri Lanka,   January 2024    [slides]

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Wisconsin School of Business Seminar, Madison, WI   February 2023    [slides]

Learning with (Bandit) Feedback from Strategic Stakeholders in Fair Division
Simons Institute, Berkeley, CA   May 2022    [slides]

Resource Allocation in Multi-armed Bandits
Google Research,   June 2021    [slides, video]

Bayesian Methods for Adaptive Experimentation
Symposium on Autonomous Experimentation, University of Maryland, College Park, MD   August 2019    [slides]

Scaling up Bandits and Friends
UC Berkeley, Berkeley, CA   April 2019    [slides]

Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Uber AI Labs, San Francisco, CA   November 2018    [slides]

Design of Experiments via Probabilistic Modeling: Applications in Materials Science, Model Selection, and Astrophysics
Covestro, Pittsburgh, PA,   August 2018

Active Bayesian Design of Experiments via Posterior Sampling
Machine Learning in Science & Engineering Conference, Pittsburgh, PA,   June 2018    [slides]

Bayesian Design of Experiments via Posterior Sampling
Lawrence Berkeley National Lab, Berkeley, CA,   June 2018    [slides]

Scalable Bandit Methods for Hyper-parameter Tuning
Guest Lecture - Machine Learning for Biology, University of Pittsburgh, Pittsburgh, PA,   November 2017    [slides]

Bayesian Optimisation for Materials Science
Electrochemical Energy Symposium, Pittsburgh, PA,   November 2017    [slides]

Parallelised Bayesian Optimisation via Thompson Sampling
Google Research, Mountain View, CA,   September 2017    [slides]

Multi-fidelity Bayesian Optimisation
Facebook Inc., Menlo Park, CA,   September 2017    [slides]

Stochastic Bandits
University of Moratuwa, Moratuwa, Sri Lanka,   August 2017    [slides]

Bandit Optimisation with Approximations
École Polytechnique, Paris, France,   April 2017     [slides]

Multi-fidelity Bandit Optimisation
University College London, London, UK,   July 2016     [slides]