528 citations · 4.4k across the 100 of their papers we have counts for
12 papers · 1 filter
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
William Herlands, Daniel B. Neill, Hannes Nickisch +1
Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unid…
Scaling Gaussian Process Regression with Derivatives
David Eriksson, Kun Dong, Eric Hans Lee +2
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction. Fitting a G…
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Jacob R. Gardner, Geoff Pleiss, David Bindel +2
Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficie…
Probabilistic FastText for Multi-Sense Word Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar
We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we repre…
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Ben Athiwaratkun, Marc Finzi, Pavel Izmailov +1
Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its…
Hierarchical Density Order Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson
By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. T…