Kirthevasan Kandasamy
31 papers · 2014–2026 · 9 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (44) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Deep Specialist
(10)
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Dynamic Duo
(14)
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Keyword Champion
(5)
🧭
Keyword Pioneer
📈
Trend Setter
⚡
Prolific Year · Productive
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Keyword Collector
(125)
🔥
Unstoppable · Silver
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Century Club · Bronze
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Conference Pioneer
Top co-authors
Research topics
Machine Learning
(30)
Optimization & Theory
(25)
Mathematics & Optimization
(22)
Optimization
(17)
Artificial Intelligence
(13)
Learning Types
(11)
Core AI
(7)
Mathematics
(5)
Learning Paradigms
(4)
Bayesian & Probabilistic · Machine Learning
(3)
Bayesian & Probabilistic · Artificial Intelligence
(3)
Core Methods
(3)
Application Areas
(2)
Statistics
(2)
Keywords
regret bound
(9)
bayesian optimization
(8)
multi-armed bandit
(8)
online learning
(6)
gaussian process
(5)
hyperparameter tuning
(5)
multi-fidelity optimization
(5)
black-box optimization
(4)
sublinear regret
(3)
resource allocation
(3)
nonparametric estimation
(3)
stochastic optimization
(2)
neural architecture search
(2)
upper confidence bound
(2)
additive model
(2)
optimal transport
(2)
hierarchical partitioning
(2)
nash equilibrium
(2)
active learning
(2)
density estimation
(2)
Papers
Constrained Best Arm Identification with Tests for Feasibility
AAAI 2026
Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget (extended abstract)
COLT 2026
Strategy-robust Online Learning in Contextual Pricing
ALT 2026
Collaborative Mean Estimation Among Heterogeneous Strategic Agents: Individual Rationality, Fairness, and Truthful Contribution
ICML 2025
Learning to Price Homogeneous Data
NeurIPS 2024
Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning
ICML 2024
Active Cost-aware Labeling of Streaming Data
AISTATS 2023
Cilantro: Performance-Aware Resource Allocation for General Objectives via Online Feedback
OSDI 2023
Mechanism Design for Collaborative Normal Mean Estimation
NeurIPS 2023
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
JMLR 2023
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback
AISTATS 2022
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
ICML 2021
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
AISTATS 2020
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
JMLR 2020
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
UAI 2019
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments
ICML 2019
Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach
AISTATS 2019
Offline Contextual Bayesian Optimization
NeurIPS 2019
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
ICML 2018
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
NeurIPS 2018
Parallelised Bayesian Optimisation via Thompson Sampling
AISTATS 2018
Multi-fidelity Bayesian Optimisation with Continuous Approximations
ICML 2017
Additive Approximations in High Dimensional Nonparametric Regression via the SALSA
ICML 2016
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
NeurIPS 2016
High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models
AISTATS 2016
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
NeurIPS 2016
The Multi-fidelity Multi-armed Bandit
NeurIPS 2016
High Dimensional Bayesian Optimisation and Bandits via Additive Models
ICML 2015
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations
NeurIPS 2015
On Estimating L_2^2 Divergence
AISTATS 2015
Nonparametric Estimation of Renyi Divergence and Friends
ICML 2014