
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning
Gaussian processes remain popular as a flexible and expressive model cla...
read it

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...
read it

Rectangular Flows for Manifold Learning
Normalizing flows are invertible neural networks with tractable changeo...
read it

Scalable Cross Validation Losses for Gaussian Process Models
We introduce a simple and scalable method for training Gaussian process ...
read it

Hierarchical Inducing Point Gaussian Process for Interdomain Observations
We examine the general problem of interdomain Gaussian Processes (GPs):...
read it

BiasFree Scalable Gaussian Processes via Randomized Truncations
Scalable Gaussian Process methods are computationally attractive, yet in...
read it

Uses and Abuses of the CrossEntropy Loss: Case Studies in Modern Deep Learning
Modern deep learning is primarily an experimental science, in which empi...
read it

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learn...
read it

Deep Sigma Point Processes
We introduce Deep Sigma Point Processes, a class of parametric models in...
read it

Identifying Mislabeled Data using the Area Under the Margin Ranking
Not all data in a typical training set help with generalization; some sa...
read it

Convolutional Networks with Dense Connectivity
Recent work has shown that convolutional networks can be substantially d...
read it

Sparse Gaussian Process Regression Beyond Variational Inference
The combination of inducing point methods with stochastic variational in...
read it

PseudoLiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Detecting objects such as cars and pedestrians in 3D plays an indispensa...
read it

Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible models with stateoftheart perfo...
read it

GPyTorch: Blackbox MatrixMatrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussi...
read it

ConstantTime Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression ...
read it

Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed...
read it

On Fairness and Calibration
The machine learning community has become increasingly concerned with th...
read it

MemoryEfficient Implementation of DenseNets
The DenseNet architecture is highly computationally efficient as a resul...
read it

Deep Feature Interpolation for Image Content Changes
We propose Deep Feature Interpolation (DFI), a new datadriven baseline ...
read it
Geoff Pleiss
is this you? claim profile