Kirthevasan Kandasamy

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High Dimensional Bayesian Optimisation and Bandits via Additive Models

Kirthevasan Kandasamy, Jeff G. Schneider, Barnabás Póczos
ICML
2015

Bayesian Optimisation (BO) is a technique used in optimising a D-dimensional function which is typically expensive to evaluate. While there have been many successes for BO in low dimensions, scaling… (More)

Nonparametric Estimation of Renyi Divergence and Friends

Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman
ICML
2014

We consider nonparametric estimation of L2, Rényi-α and Tsallis-α divergences between continuous distributions. Our approach is to construct estimators for particular integral functionals of two… (More)

Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations

Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
NIPS
2015

We propose and analyse estimators for statistical functionals of one or more distributions under nonparametric assumptions. Our estimators are derived from the von Mises expansion and are based on… (More)

Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations

Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff G. Schneider, Barnabás Póczos
NIPS
2016

In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function f . Traditional methods for this problem assume just the… (More)

Multi-fidelity Bayesian Optimisation with Continuous Approximations

Kirthevasan Kandasamy, Gautam Dasarathy, Jeff G. Schneider, Barnabás Póczos
ICML
2017

Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design. Recently, multifidelity methods… (More)

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric Xing
ArXiv
2018

Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function f which is only accessible via point evaluations. It is typically used in settings where f is expensive… (More)

High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models

Chun-Liang Li, Kirthevasan Kandasamy, Barnabás Póczos, Jeff G. Schneider
AISTATS
2016

Bayesian Optimization (BO) is commonly used to optimize blackbox objective functions which are expensive to evaluate. A common approach is based on using Gaussian Process (GP) to model the objective… (More)

Bayesian Active Learning for Posterior Estimation

This paper studies active posterior estimation in a Bayesian setting when the likelihood is expensive to evaluate. Existing techniques for posterior estimation are based on generating samples… (More)

Additive Approximations in High Dimensional Nonparametric Regression via the SALSA

High dimensional nonparametric regression is an inherently difficult problem with known lower bounds depending exponentially in dimension. A popular strategy to alleviate this curse of dimensionality… (More)

Batch Policy Gradient Methods for Improving Neural Conversation Models

Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
ArXiv
2016

We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chatbot used in automated customer service… (More)

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