Kirthevasan Kandasamy | DeepAI

DeepAI AI Chat

Featured Co-authors

Michael I. Jordan
283 publications
Eric P Xing
147 publications
Ken Goldberg
112 publications
Barnabas Poczos
101 publications
Joseph E. Gonzalez
99 publications
Larry Wasserman
86 publications
Akshay Krishnamurthy
75 publications
Ion Stoica
74 publications
Eric Xing
70 publications
Jeff Schneider
59 publications
Sanjay Shakkottai
49 publications

∙ 06/10/2023

Mechanism Design for Collaborative Normal Mean Estimation

We study collaborative normal mean estimation, where m strategic agents ...

0 Yiding Chen, et al. ∙

∙ 04/13/2023

Active Cost-aware Labeling of Streaming Data

We study actively labeling streaming data, where an active learner is fa...

0 Ting Cai, et al. ∙

∙ 02/20/2023

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty

In online marketplaces, customers have access to hundreds of reviews for...

0 Wenshuo Guo, et al. ∙

∙ 06/11/2021

Online Learning of Competitive Equilibria in Exchange Economies

The sharing of scarce resources among multiple rational agents is one of...

2 Wenshuo Guo, et al. ∙

∙ 06/06/2021

PAC Best Arm Identification Under a Deadline

We study (ϵ, δ)-PAC best arm identification, where a decision-maker must...

2 Brijen Thananjeyan, et al. ∙

∙ 12/15/2020

Online Learning Demands in Max-min Fairness

We describe mechanisms for the allocation of a scarce resource among mul...

5 Kirthevasan Kandasamy, et al. ∙

∙ 10/31/2020

Resource Allocation in Multi-armed Bandit Exploration: Overcoming Nonlinear Scaling with Adaptive Parallelism

We study exploration in stochastic multi-armed bandits when we have acce...

11 Brijen Thananjeyan, et al. ∙

∙ 04/19/2020

Mechanism Design with Bandit Feedback

We study a multi-round welfare-maximising mechanism design problem, wher...

4 Kirthevasan Kandasamy, et al. ∙

∙ 08/05/2019

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

We describe ChemBO, a Bayesian Optimization framework for generating and...

0 Ksenia Korovina, et al. ∙

∙ 03/15/2019

Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly

Bayesian Optimisation (BO), refers to a suite of techniques for global o...

14 Kirthevasan Kandasamy, et al. ∙

∙ 01/31/2019

ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization

Optimizing an expensive-to-query function is a common task in science an...

18 Willie Neiswanger, et al. ∙

∙ 10/24/2018

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

We study the problem of black-box optimization of a noisy function in th...

0 Rajat Sen, et al. ∙

∙ 05/30/2018

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations

Many real world applications can be framed as multi-objective optimizati...

0 Biswajit Paria, et al. ∙

∙ 05/25/2018

Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming

We design a new myopic strategy for a wide class of sequential design of...

0 Kirthevasan Kandasamy, et al. ∙

∙ 02/11/2018

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Bayesian Optimisation (BO) refers to a class of methods for global optim...

0 Kirthevasan Kandasamy, et al. ∙

∙ 05/25/2017

Asynchronous Parallel Bayesian Optimisation via Thompson Sampling

We design and analyse variations of the classical Thompson sampling (TS)...

0 Kirthevasan Kandasamy, et al. ∙

∙ 03/18/2017

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Bandit methods for black-box optimisation, such as Bayesian optimisation...

0 Kirthevasan Kandasamy, et al. ∙

∙ 02/10/2017

Batch Policy Gradient Methods for Improving Neural Conversation Models

We study reinforcement learning of chatbots with recurrent neural networ...

0 Kirthevasan Kandasamy, et al. ∙

∙ 02/03/2017

Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning

A common problem in disciplines of applied Statistics research such as A...

0 Kirthevasan Kandasamy, et al. ∙

∙ 09/21/2016

Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices

Recently, there has been a surge of interest in using spectral methods f...

0 Kirthevasan Kandasamy, et al. ∙

∙ 03/20/2016

Multi-fidelity Gaussian Process Bandit Optimisation

In many scientific and engineering applications, we are tasked with the ...

0 Kirthevasan Kandasamy, et al. ∙

∙ 01/31/2016

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

High dimensional nonparametric regression is an inherently difficult pro...

0 Kirthevasan Kandasamy, et al. ∙

∙ 03/05/2015

High Dimensional Bayesian Optimisation and Bandits via Additive Models

Bayesian Optimisation (BO) is a technique used in optimising a D-dimensi...

0 Kirthevasan Kandasamy, et al. ∙

∙ 11/17/2014

Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations

We propose and analyze estimators for statistical functionals of one or ...

0 Kirthevasan Kandasamy, et al. ∙

∙ 10/30/2014

On Estimating L_2^2 Divergence

We give a comprehensive theoretical characterization of a nonparametric ...

0 Akshay Krishnamurthy, et al. ∙

∙ 02/12/2014

Nonparametric Estimation of Renyi Divergence and Friends

We consider nonparametric estimation of L_2, Renyi-α and Tsallis-α diver...

0 Akshay Krishnamurthy, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world