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09/06/2023
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Many areas of machine learning and science involve large linear algebra ...
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02/23/2023
Sharp Calibrated Gaussian Processes
While Gaussian processes are a mainstay for various engineering and scie...
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02/01/2023
Pathologies of Predictive Diversity in Deep Ensembles
Classical results establish that ensembles of small models benefit when ...
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05/30/2022
Posterior and Computational Uncertainty in Gaussian Processes
Gaussian processes scale prohibitively with the size of the dataset. In ...
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02/14/2022
Deep Ensembles Work, But Are They Necessary?
Ensembling neural networks is an effective way to increase accuracy, and...
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02/03/2022
Variational Nearest Neighbor Gaussian Processes
Variational approximations to Gaussian processes (GPs) typically use a s...
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07/01/2021
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning
Gaussian processes remain popular as a flexible and expressive model cla...
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06/11/2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Large width limits have been a recent focus of deep learning research: m...
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06/02/2021
Rectangular Flows for Manifold Learning
Normalizing flows are invertible neural networks with tractable change-o...
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05/24/2021
Scalable Cross Validation Losses for Gaussian Process Models
We introduce a simple and scalable method for training Gaussian process ...
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02/28/2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
We examine the general problem of inter-domain Gaussian Processes (GPs):...
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02/12/2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Scalable Gaussian Process methods are computationally attractive, yet in...
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11/10/2020
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Modern deep learning is primarily an experimental science, in which empi...
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06/19/2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learn...
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02/21/2020
Deep Sigma Point Processes
We introduce Deep Sigma Point Processes, a class of parametric models in...
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01/28/2020
Identifying Mislabeled Data using the Area Under the Margin Ranking
Not all data in a typical training set help with generalization; some sa...
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01/08/2020
Convolutional Networks with Dense Connectivity
Recent work has shown that convolutional networks can be substantially d...
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10/16/2019
Sparse Gaussian Process Regression Beyond Variational Inference
The combination of inducing point methods with stochastic variational in...
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06/14/2019
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Detecting objects such as cars and pedestrians in 3D plays an indispensa...
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03/19/2019
Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible models with state-of-the-art perfo...
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09/28/2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussi...
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03/16/2018
Constant-Time Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression ...
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02/24/2018
Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed...
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09/06/2017
On Fairness and Calibration
The machine learning community has become increasingly concerned with th...
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07/21/2017
Memory-Efficient Implementation of DenseNets
The DenseNet architecture is highly computationally efficient as a resul...
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11/16/2016