Geoff Pleiss

papers

662

total citations

papers (12)

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

NEURIPS 2020arXiv

citations

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

ICML 2024arXiv

citations

The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective

NEURIPS 2021arXiv

citations

Pathologies of Predictive Diversity in Deep Ensembles

ICLR 2024arXiv

citations

Posterior and Computational Uncertainty in Gaussian Processes

NEURIPS 2022arXiv

citations

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra

NEURIPS 2023arXiv

citations

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning

ICML 2024arXiv

citations

Sharp Calibrated Gaussian Processes

NEURIPS 2023arXiv

citations

Theoretical Limitations of Ensembles in the Age of Overparameterization

ICML 2025arXiv

citations

Geoff Pleiss

papers (12)

Identifying Mislabeled Data using the Area Under the Margin Ranking

Deep Ensembles Work, But Are They Necessary?

Rectangular Flows for Manifold Learning

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

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

The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective

Pathologies of Predictive Diversity in Deep Ensembles

Posterior and Computational Uncertainty in Gaussian Processes

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning

Sharp Calibrated Gaussian Processes

Theoretical Limitations of Ensembles in the Age of Overparameterization

papers (12)

Identifying Mislabeled Data using the Area Under the Margin Ranking

Deep Ensembles Work, But Are They Necessary?

Rectangular Flows for Manifold Learning

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

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

The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective

Pathologies of Predictive Diversity in Deep Ensembles

Posterior and Computational Uncertainty in Gaussian Processes

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning

Sharp Calibrated Gaussian Processes

Theoretical Limitations of Ensembles in the Age of Overparameterization