Willie Neiswanger

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Carnegie Mellon University

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ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization

Optimizing an expensive-to-query function is a common task in science an...
01/31/2019 ∙ by Willie Neiswanger, et al. ∙ 18 ∙ share

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

Bayesian Optimisation (BO), refers to a suite of techniques for global o...
03/15/2019 ∙ by Kirthevasan Kandasamy, et al. ∙ 14 ∙ share

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Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful

Raven's Progressive Matrices are one of the widely used tests in evaluat...
07/10/2018 ∙ by Rajesh Chidambaram, et al. ∙ 4 ∙ share

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Performance Bounds for Graphical Record Linkage

Record linkage involves merging records in large, noisy databases to rem...
03/08/2017 ∙ by Rebecca C. Steorts, et al. ∙ 0 ∙ share

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Post-Inference Prior Swapping

While Bayesian methods are praised for their ability to incorporate usef...
06/02/2016 ∙ by Willie Neiswanger, et al. ∙ 0 ∙ share

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Embarrassingly Parallel Variational Inference in Nonconjugate Models

We develop a parallel variational inference (VI) procedure for use in da...
10/14/2015 ∙ by Willie Neiswanger, et al. ∙ 0 ∙ share

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Fast Function to Function Regression

We analyze the problem of regression when both input covariates and outp...
10/27/2014 ∙ by Junier Oliva, et al. ∙ 0 ∙ share

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Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

We develop parallel and distributed Frank-Wolfe algorithms; the former o...
09/22/2014 ∙ by Yu-Xiang Wang, et al. ∙ 0 ∙ share

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Asymptotically Exact, Embarrassingly Parallel MCMC

Communication costs, resulting from synchronization requirements during ...
11/19/2013 ∙ by Willie Neiswanger, et al. ∙ 0 ∙ share

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Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures

This paper proposes a technique for the unsupervised detection and track...
10/11/2012 ∙ by Willie Neiswanger, et al. ∙ 0 ∙ share

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Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Bayesian Optimisation (BO) refers to a class of methods for global optim...
02/11/2018 ∙ by Kirthevasan Kandasamy, et al. ∙ 0 ∙ share

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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...
05/25/2018 ∙ by Kirthevasan Kandasamy, et al. ∙ 0 ∙ share

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ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

We describe ChemBO, a Bayesian Optimization framework for generating and...
08/05/2019 ∙ by Ksenia Korovina, et al. ∙ 0 ∙ share

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BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search

Neural Architecture Search (NAS) has seen an explosion of research in th...
10/25/2019 ∙ by Colin White, et al. ∙ 0 ∙ share

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