Geoff Pleiss
28 papers · 2017–2025 · 6 conferences · across top CS/AI conferences
Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot (6) 🧭 Keyword Pioneer 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (8)
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Keyword Pioneer
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Hot Topic Early Bird
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Cross-Pollinator
(14)
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(10)
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(13)
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(2)
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(103)
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(2)
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(9)
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Century Club
(28)
Conferences
NIPS (13)
ICML (9)
AISTATS (3)
CVPR (1)
ICLR (1)
UAI (1)
Top co-authors
Keywords
gaussian process
(16)
variational inference
(7)
uncertainty quantification
(5)
kernel matrix
(4)
conjugate gradient
(4)
predictive distribution
(3)
scalable inference
(3)
hyperparameter optimization
(2)
neural network
(2)
deep gaussian process
(2)
matrix multiplication
(2)
bayesian optimization
(2)
posterior covariance
(2)
maximum likelihood
(2)
inducing point
(2)
hyperparameter learning
(1)
manifold learning
(1)
gaussian processes
(1)
density estimation
(1)
model selection
(1)
Papers
Theoretical Limitations of Ensembles in the Age of Overparameterization
ICML 2025
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
ICML 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
NIPS 2024
Approximation-Aware Bayesian Optimization
NIPS 2024
Large-Scale Gaussian Processes via Alternating Projection
AISTATS 2024
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
NIPS 2023
Sharp Calibrated Gaussian Processes
NIPS 2023
Deep Ensembles Work, But Are They Necessary?
NIPS 2022
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
ICML 2022
Posterior and Computational Uncertainty in Gaussian Processes
NIPS 2022
Variational nearest neighbor Gaussian process
ICML 2022
Rectangular Flows for Manifold Learning
NIPS 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
NIPS 2021
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations
AISTATS 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations
ICML 2021
Deep Sigma Point Processes
UAI 2020
Identifying Mislabeled Data using the Area Under the Margin Ranking
NIPS 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
NIPS 2020
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
ICLR 2020
Parametric Gaussian Process Regressors
ICML 2020
Exact Gaussian Processes on a Million Data Points
NIPS 2019
Product Kernel Interpolation for Scalable Gaussian Processes
AISTATS 2018
Constant-Time Predictive Distributions for Gaussian Processes
ICML 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
NIPS 2018
On Calibration of Modern Neural Networks
ICML 2017
Deep Feature Interpolation for Image Content Changes
CVPR 2017
On Fairness and Calibration
NIPS 2017