
Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible models with stateoftheart perfo...
03/19/2019 ∙ by Ke Alexander Wang, et al. ∙ 32 ∙ shareread it

BoTorch: Programmable Bayesian Optimization in PyTorch
Bayesian optimization provides sampleefficient global optimization for ...
10/14/2019 ∙ by Maximilian Balandat, et al. ∙ 30 ∙ shareread it

A Simple Baseline for Bayesian Uncertainty in Deep Learning
We propose SWAGaussian (SWAG), a simple, scalable, and general purpose ...
02/07/2019 ∙ by Wesley Maddox, et al. ∙ 20 ∙ shareread it

Practical Multifidelity Bayesian Optimization for Hyperparameter Tuning
Bayesian optimization is popular for optimizing timeconsuming blackbox...
03/12/2019 ∙ by Jian Wu, et al. ∙ 12 ∙ shareread it

SWALP : Stochastic Weight Averaging in LowPrecision Training
Low precision operations can provide scalability, memory savings, portab...
04/26/2019 ∙ by Guandao Yang, et al. ∙ 10 ∙ shareread it

FunctionSpace Distributions over Kernels
Gaussian processes are flexible function approximators, with inductive b...
10/29/2019 ∙ by Gregory W. Benton, et al. ∙ 10 ∙ shareread it

Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
Identifying changes in model parameters is fundamental in machine learni...
10/28/2018 ∙ by William Herlands, et al. ∙ 8 ∙ shareread it

Simple Blackbox Adversarial Attacks
We propose an intriguingly simple method for the construction of adversa...
05/17/2019 ∙ by Chuan Guo, et al. ∙ 7 ∙ shareread it

Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many application...
10/29/2018 ∙ by David Eriksson, et al. ∙ 6 ∙ shareread it

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
The posteriors over neural network weights are high dimensional and mult...
02/11/2019 ∙ by Ruqi Zhang, et al. ∙ 6 ∙ shareread it

GPyTorch: Blackbox MatrixMatrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussi...
09/28/2018 ∙ by Jacob R. Gardner, et al. ∙ 4 ∙ shareread it

Subspace Inference for Bayesian Deep Learning
Bayesian inference was once a gold standard for learning with neural net...
07/17/2019 ∙ by Pavel Izmailov, et al. ∙ 3 ∙ shareread it

Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine ...
11/27/2017 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal po...
11/09/2017 ∙ by Kun Dong, et al. ∙ 0 ∙ shareread it

Bayesian Optimization with Gradients
Bayesian optimization has been successful at global optimization of expe...
03/13/2017 ∙ by Jian Wu, et al. ∙ 0 ∙ shareread it

Bayesian GAN
Generative adversarial networks (GANs) can implicitly learn rich distrib...
05/26/2017 ∙ by Yunus Saatchi, et al. ∙ 0 ∙ shareread it

Stochastic Variational Deep Kernel Learning
Deep kernel learning combines the nonparametric flexibility of kernel m...
11/01/2016 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Learning Scalable Deep Kernels with Recurrent Structure
Many applications in speech, robotics, finance, and biology deal with se...
10/27/2016 ∙ by Maruan AlShedivat, et al. ∙ 0 ∙ shareread it

Deep Kernel Learning
We introduce scalable deep kernels, which combine the structural propert...
11/06/2015 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Thoughts on Massively Scalable Gaussian Processes
We introduce a framework and early results for massively scalable Gaussi...
11/05/2015 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

The Human Kernel
Bayesian nonparametric models, such as Gaussian processes, provide a com...
10/26/2015 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Kernel Interpolation for Scalable Structured Gaussian Processes (KISSGP)
We introduce a new structured kernel interpolation (SKI) framework, whic...
03/03/2015 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

A la Carte  Learning Fast Kernels
Kernel methods have great promise for learning rich statistical represen...
12/19/2014 ∙ by Zichao Yang, et al. ∙ 0 ∙ shareread it

Studentt Processes as Alternatives to Gaussian Processes
We investigate the Studentt process as an alternative to the Gaussian p...
02/18/2014 ∙ by Amar Shah, et al. ∙ 0 ∙ shareread it

Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic prop...
02/14/2014 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Gaussian Process Kernels for Pattern Discovery and Extrapolation
Gaussian processes are rich distributions over functions, which provide ...
02/18/2013 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Gaussian Process Regression Networks
We introduce a new regression framework, Gaussian process regression net...
10/19/2011 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Generalised Wishart Processes
We introduce a stochastic process with Wishart marginals: the generalise...
12/31/2010 ∙ by Andrew Gordon Wilson, et al. ∙ 0 ∙ shareread it

Multimodal Word Distributions
Word embeddings provide point representations of words containing useful...
04/27/2017 ∙ by Ben Athiwaratkun, et al. ∙ 0 ∙ shareread it

Scalable Lévy Process Priors for Spectral Kernel Learning
Gaussian processes are rich distributions over functions, with generaliz...
02/02/2018 ∙ by Phillip A. Jang, et al. ∙ 0 ∙ shareread it

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
The loss functions of deep neural networks are complex and their geometr...
02/27/2018 ∙ by Timur Garipov, et al. ∙ 0 ∙ shareread it

Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed...
02/24/2018 ∙ by Jacob R. Gardner, et al. ∙ 0 ∙ shareread it

ConstantTime Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression ...
03/16/2018 ∙ by Geoff Pleiss, et al. ∙ 0 ∙ shareread it

Averaging Weights Leads to Wider Optima and Better Generalization
Deep neural networks are typically trained by optimizing a loss function...
03/14/2018 ∙ by Pavel Izmailov, et al. ∙ 0 ∙ shareread it

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Noniid Data
Identifying anomalous patterns in realworld data is essential for under...
04/04/2018 ∙ by William Herlands, et al. ∙ 0 ∙ shareread it

Hierarchical Density Order Embeddings
By representing words with probability densities rather than point vecto...
04/26/2018 ∙ by Ben Athiwaratkun, et al. ∙ 0 ∙ shareread it

Improving ConsistencyBased SemiSupervised Learning with Weight Averaging
Recent advances in deep unsupervised learning have renewed interest in s...
06/14/2018 ∙ by Ben Athiwaratkun, et al. ∙ 0 ∙ shareread it

Probabilistic FastText for MultiSense Word Embeddings
We introduce Probabilistic FastText, a new model for word embeddings tha...
06/07/2018 ∙ by Ben Athiwaratkun, et al. ∙ 0 ∙ shareread it

SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
03/29/2019 ∙ by Alexander Ratner, et al. ∙ 0 ∙ shareread it