262 citations · 925 across the 27 of their papers we have counts for
30 papers
Mechanism Design for Collaborative Normal Mean Estimation
Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
We study collaborative normal mean estimation, where strategic agents collect i.i.d samples from a normal distribution at a cost. They all wish to estimat…
Active Cost-aware Labeling of Streaming Data
Ting Cai, Kirthevasan Kandasamy
We study actively labeling streaming data, where an active learner is faced with a stream of data points and must carefully choose which of these points to label via an expensive e…
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy +1
In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for…
PAC Best Arm Identification Under a Deadline
Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica +3
We study -PAC best arm identification, where a decision-maker must identify an -optimal arm with probability at least , while minimizing the number of arm pulls (…
Learning Competitive Equilibria in Exchange Economies with Bandit Feedback
Wenshuo Guo, Kirthevasan Kandasamy, Joseph E Gonzalez +2
The sharing of scarce resources among multiple rational agents is one of the classical problems in economics. In exchange economies, which are used to model such situations, agents…
Online Learning Demands in Max-min Fairness
Kirthevasan Kandasamy, Gur-Eyal Sela, Joseph E Gonzalez +2
We describe mechanisms for the allocation of a scarce resource among multiple users in a way that is efficient, fair, and strategy-proof, but when users do not know their resource…
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica +3
We study exploration in stochastic multi-armed bandits when we have access to a divisible resource that can be allocated in varying amounts to arm pulls. We focus in particular on…
VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback
Kirthevasan Kandasamy, Joseph E. Gonzalez, Michael I. Jordan +1
We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. On each round, a mechanism first assigns an allocation each t…
Offline Contextual Bayesian Optimization for Nuclear Fusion
Youngseog Chung, Ian Char, Willie Neiswanger +5
Nuclear fusion is regarded as the energy of the future since it presents the possibility of unlimited clean energy. One obstacle in utilizing fusion as a feasible energy source is…
Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning
Adarsh Dave, Jared Mitchell, Kirthevasan Kandasamy +5
Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and sele…
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy +4
In applications such as molecule design or drug discovery, it is desirable to have an algorithm which recommends new candidate molecules based on the results of past tests. These m…
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger +5
Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of expensive black box functions, which use introspective Bayesian models of the function to effi…
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos +2
Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum. A popular strategy is Baye…
Noisy Blackbox Optimization with Multi-Fidelity Queries: A Tree Search Approach
Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai
We study the problem of black-box optimization of a noisy function in the presence of low-cost approximations or fidelities, which is motivated by problems like hyper-parameter tun…
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang +3
We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specif…
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations
Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos
Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization technique…
Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider +2
Bayesian Optimisation (BO) refers to a class of methods for global optimisation of a function which is only accessible via point evaluations. It is typically used in settings w…
Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider +1
We design and analyse variations of the classical Thompson sampling (TS) procedure for Bayesian optimisation (BO) in settings where function evaluations are expensive, but can be p…
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider +1
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, \e…
Batch Policy Gradient Methods for Improving Neural Conversation Models
Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka +2
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 automate…
Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning
Kirthevasan Kandasamy, Jeff Schneider, Barnabás Póczos
A common problem in disciplines of applied Statistics research such as Astrostatistics is of estimating the posterior distribution of relevant parameters. Typically, the likelihood…
The Multi-fidelity Multi-armed Bandit
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider +1
We study a variant of the classical stochastic -armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available. For ex…
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing
Recently, there has been a surge of interest in using spectral methods for estimating latent variable models. However, it is usually assumed that the distribution of the observatio…
Multi-fidelity Gaussian Process Bandit Optimisation
Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva +2
In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function . Traditional settings for this problem assum…