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Geoff Pleiss

28 papers · 2017–2025 · 6 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

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+10 more ↓ πŸ—ΊοΈ Taxonomy Completionist (43) πŸƒ Academic Marathon Β· Sprint 🌍 Conference Polyglot Β· Silver πŸŒ‰ Interdisciplinary Bridge Β· Bronze 🐣 Hot Topic Early Bird
πŸ”¬ Deep Specialist (16) πŸ† Keyword Champion (2) 🧭 Keyword Pioneer 🀝 Dynamic Duo (10) πŸ‘‘ Triple Crown ⚑ Prolific Year Β· Productive πŸ”₯ Unstoppable Β· Bronze ❓ The Questioner (2) πŸ’Ž Century Club Β· Bronze πŸ—ƒοΈ Keyword Collector (123)

Venues

NeurIPS (13) ICML (9) AISTATS (3) CVPR (1) ICLR (1) UAI (1)

Top co-authors

Jacob Gardner (10) Kilian Q. Weinberger (6) John P. Cunningham (6) Jonathan Wenger (4) Luhuan Wu (3) Martin Jankowiak (3) Kilian Weinberger (3) Philipp Hennig (3) David Eriksson (2) John Cunningham (2)

Research topics

Machine Learning (25) Optimization & Theory Β· Machine Learning (22) Artificial Intelligence (11) Bayesian & Probabilistic Β· Machine Learning (10) Bayesian & Probabilistic Β· Artificial Intelligence (9) Core Methods (9) Deep Learning (8) Application Areas (7) Learning Types Β· Machine Learning (6) Computer Vision (3) Mathematics & Optimization (3) Optimization & Theory Β· Deep Learning (3) Analysis (2) Core AI (2) Learning Types Β· Deep Learning (2)

Keywords

gaussian process (16) variational inference (7) uncertainty quantification (5) conjugate gradient (4) kernel matrix (4) scalable inference (3) bayesian optimization (3) predictive distribution (3) neural network (2) inducing point (2) predictive variance (2) deep gaussian process (2) matrix multiplication (2) maximum likelihood (2) hyperparameter optimization (2) posterior covariance (2) hyperparameter learning (1) stereo vision (1) manifold learning (1) gaussian processes (1)

Papers

Theoretical Limitations of Ensembles in the Age of Overparameterization ICML 2025 Approximation-Aware Bayesian Optimization NeurIPS 2024 A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? ICML 2024 Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference NeurIPS 2024 Large-Scale Gaussian Processes via Alternating Projection AISTATS 2024 Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning ICML 2024 CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra NeurIPS 2023 Sharp Calibrated Gaussian Processes NeurIPS 2023 Deep Ensembles Work, But Are They Necessary? NeurIPS 2022 Posterior and Computational Uncertainty in Gaussian Processes NeurIPS 2022 Preconditioning for Scalable Gaussian Process Hyperparameter Optimization ICML 2022 Variational nearest neighbor Gaussian process ICML 2022 Bias-Free Scalable Gaussian Processes via Randomized Truncations ICML 2021 Hierarchical Inducing Point Gaussian Process for Inter-domian Observations AISTATS 2021 Rectangular Flows for Manifold Learning NeurIPS 2021 The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective NeurIPS 2021 Deep Sigma Point Processes UAI 2020 Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization NeurIPS 2020 Identifying Mislabeled Data using the Area Under the Margin Ranking NeurIPS 2020 Parametric Gaussian Process Regressors ICML 2020 Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving ICLR 2020 Exact Gaussian Processes on a Million Data Points NeurIPS 2019 Constant-Time Predictive Distributions for Gaussian Processes ICML 2018 GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration NeurIPS 2018 Product Kernel Interpolation for Scalable Gaussian Processes AISTATS 2018 Deep Feature Interpolation for Image Content Changes CVPR 2017 On Calibration of Modern Neural Networks ICML 2017 On Fairness and Calibration NeurIPS 2017