Geoff Pleiss

According to our database1, Geoff Pleiss authored at least 36 papers between 2017 and 2024.

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Bibliography

2024
MCMC-driven learning.
CoRR, 2024

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning.
CoRR, 2024

A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
CoRR, 2024

2023
Large-Scale Gaussian Processes via Alternating Projection.
CoRR, 2023

Pathologies of Predictive Diversity in Deep Ensembles.
CoRR, 2023

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sharp Calibrated Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Convolutional Networks with Dense Connectivity.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Variational Nearest Neighbor Gaussian Processes.
CoRR, 2022

Posterior and Computational Uncertainty in Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep Ensembles Work, But Are They Necessary?
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Variational nearest neighbor Gaussian process.
Proceedings of the International Conference on Machine Learning, 2022

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization.
Proceedings of the International Conference on Machine Learning, 2022

2021
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning.
CoRR, 2021

Scalable Cross Validation Losses for Gaussian Process Models.
CoRR, 2021

Hierarchical Inducing Point Gaussian Process for Inter-domain Observations.
CoRR, 2021

The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Rectangular Flows for Manifold Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bias-Free Scalable Gaussian Processes via Randomized Truncations.
Proceedings of the 38th International Conference on Machine Learning, 2021

Hierarchical Inducing Point Gaussian Process for Inter-domian Observations.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Deep Sigma Point Processes.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Identifying Mislabeled Data using the Area Under the Margin Ranking.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Parametric Gaussian Process Regressors.
Proceedings of the 37th International Conference on Machine Learning, 2020

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving.
Proceedings of the 8th International Conference on Learning Representations, 2020

Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning.
Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019
Sparse Gaussian Process Regression Beyond Variational Inference.
CoRR, 2019

Exact Gaussian Processes on a Million Data Points.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Constant-Time Predictive Distributions for Gaussian Processes.
Proceedings of the 35th International Conference on Machine Learning, 2018

Product Kernel Interpolation for Scalable Gaussian Processes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Memory-Efficient Implementation of DenseNets.
CoRR, 2017

On Fairness and Calibration.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On Calibration of Modern Neural Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

Snapshot Ensembles: Train 1, Get M for Free.
Proceedings of the 5th International Conference on Learning Representations, 2017

Deep Feature Interpolation for Image Content Changes.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017


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