95 citations · 430 across the 31 of their papers we have counts for
10 papers · 1 filter
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…
Implicit Kernel Learning
Chun-Liang Li, Wei-Cheng Chang, Youssef Mroueh +2
Kernels are powerful and versatile tools in machine learning and statistics. Although the notion of universal kernels and characteristic kernels has been studied, kernel selection…
Kernel Change-point Detection with Auxiliary Deep Generative Models
Wei-Cheng Chang, Chun-Liang Li, Yiming Yang +1
Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on t…
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…
Cautious Deep Learning
Yotam Hechtlinger, Barnabás Póczos, Larry Wasserman
Most classifiers operate by selecting the maximum of an estimate of the conditional distribution where stands for the features of the instance to be classified and …
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…