no code implementations • 20 Oct 2022 • Xinran Zhu, Leo Huang, Cameron Ibrahim, Eric Hans Lee, David Bindel

The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a joint prior over input warping and kernel hyperparameters.

no code implementations • 12 Nov 2021 • Moontae Lee, Sungjun Cho, Kun Dong, David Mimno, David Bindel

Across many data domains, co-occurrence statistics about the joint appearance of objects are powerfully informative.

1 code implementation • 30 Sep 2021 • Dongping Qi, David Bindel, Alexander Vladimirsky

We consider a task of surveillance-evading path-planning in a continuous setting.

no code implementations • NeurIPS 2021 • Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel

We demonstrate the full scalability of our approach on a variety of tasks, ranging from a high dimensional stellarator fusion regression task to training graph convolutional neural networks on Pubmed using Bayesian optimization.

no code implementations • 21 Oct 2020 • Leo Huang, Andrew Graven, David Bindel

A fundamental problem on graph-structured data is that of quantifying similarity between graphs.

no code implementations • CVPR 2020 • Kyle Wilson, David Bindel

This is an extension of the results of [24], which used local convexity as a proxy to study the difficulty of problem.

1 code implementation • 24 Feb 2020 • Eric Hans Lee, David Eriksson, Bolong Cheng, Michael McCourt, David Bindel

Non-myopic acquisition functions consider the impact of the next $h$ function evaluations and are typically computed through rollout, in which $h$ steps of BO are simulated.

no code implementations • IJCNLP 2019 • Moontae Lee, Sungjun Cho, David Bindel, David Mimno

Despite great scalability on large data and their ability to understand correlations between topics, spectral topic models have not been widely used due to the absence of reliability in real data and lack of practical implementations.

3 code implementations • 30 Jul 2019 • David Eriksson, David Bindel, Christine A. Shoemaker

This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT).

1 code implementation • 23 May 2019 • Kun Dong, Austin R. Benson, David Bindel

Much of spectral graph theory descends directly from spectral geometry, the study of differentiable manifolds through the spectra of associated differential operators.

Social and Information Networks Numerical Analysis

1 code implementation • NeurIPS 2018 • David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.

4 code implementations • NeurIPS 2018 • Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson

Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware.

2 code implementations • 13 Dec 2017 • Kun He, Pan Shi, David Bindel, John E. Hopcroft

Community detection is an important information mining task in many fields including computer science, social sciences, biology and physics.

Social and Information Networks

no code implementations • 19 Nov 2017 • Moontae Lee, David Bindel, David Mimno

Spectral topic modeling algorithms operate on matrices/tensors of word co-occurrence statistics to learn topic-specific word distributions.

3 code implementations • NeurIPS 2017 • Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson

For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations.

no code implementations • NeurIPS 2015 • Moontae Lee, David Bindel, David Mimno

Spectral inference provides fast algorithms and provable optimality for latent topic analysis.

1 code implementation • 25 Sep 2015 • Yixuan Li, Kun He, David Bindel, John Hopcroft

Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure when dealing with large networks.

Social and Information Networks Data Structures and Algorithms Physics and Society G.2.2; H.3.3

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