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ORCIDGeoff Pleiss
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2020 – today
- 2024
[j2]Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham:
Pathologies of Predictive Diversity in Deep Ensembles. Trans. Mach. Learn. Res. 2024 (2024)- [c27]Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. AISTATS 2024: 2620-2628 - [c26]Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss:
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? ICML 2024 - [c25]Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss:
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. ICML 2024 - [i34]Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss:
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? CoRR abs/2402.05015 (2024) - [i33]Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss:
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. CoRR abs/2402.09542 (2024) - [i32]Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic:
MCMC-driven learning. CoRR abs/2402.09598 (2024) - [i31]Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner:
Approximation-Aware Bayesian Optimization. CoRR abs/2406.04308 (2024) - [i30]Jason Yoo, Dylan Green, Geoff Pleiss, Frank Wood:
Online Continual Learning of Video Diffusion Models From a Single Video Stream. CoRR abs/2406.04814 (2024) - [i29]Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss:
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR abs/2406.06459 (2024) - 2023
- [c24]Alexandre Capone, Sandra Hirche, Geoff Pleiss:
Sharp Calibrated Gaussian Processes. NeurIPS 2023 - [c23]Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson:
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. NeurIPS 2023 - [i28]Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham:
Pathologies of Predictive Diversity in Deep Ensembles. CoRR abs/2302.00704 (2023) - [i27]Alexandre Capone, Geoff Pleiss, Sandra Hirche:
Sharp Calibrated Gaussian Processes. CoRR abs/2302.11961 (2023) - [i26]Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson:
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. CoRR abs/2309.03060 (2023) - [i25]Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. CoRR abs/2310.17137 (2023) - 2022
- [j1]Gao Huang
, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger:
Convolutional Networks with Dense Connectivity. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 8704-8716 (2022) - [c22]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML 2022: 23751-23780 - [c21]Luhuan Wu, Geoff Pleiss, John P. Cunningham:
Variational nearest neighbor Gaussian process. ICML 2022: 24114-24130 - [c20]Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham:
Deep Ensembles Work, But Are They Necessary? NeurIPS 2022 - [c19]Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham:
Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS 2022 - [i24]Luhuan Wu, Geoff Pleiss, John P. Cunningham:
Variational Nearest Neighbor Gaussian Processes. CoRR abs/2202.01694 (2022) - [i23]Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham:
Deep Ensembles Work, But Are They Necessary? CoRR abs/2202.06985 (2022) - [i22]Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham:
Posterior and Computational Uncertainty in Gaussian Processes. CoRR abs/2205.15449 (2022) - 2021
- [c18]Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham:
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS 2021: 2926-2934 - [c17]Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham:
Bias-Free Scalable Gaussian Processes via Randomized Truncations. ICML 2021: 8609-8619 - [c16]Geoff Pleiss, John P. Cunningham:
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. NeurIPS 2021: 3349-3363 - [c15]Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Rectangular Flows for Manifold Learning. NeurIPS 2021: 30228-30241 - [i21]Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham:
Bias-Free Scalable Gaussian Processes via Randomized Truncations. CoRR abs/2102.06695 (2021) - [i20]Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham:
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR abs/2103.00393 (2021) - [i19]Martin Jankowiak, Geoff Pleiss:
Scalable Cross Validation Losses for Gaussian Process Models. CoRR abs/2105.11535 (2021) - [i18]Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Rectangular Flows for Manifold Learning. CoRR abs/2106.01413 (2021) - [i17]Geoff Pleiss, John P. Cunningham:
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. CoRR abs/2106.06529 (2021) - [i16]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR abs/2107.00243 (2021) - 2020
- [c14]Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. ICBINB@NeurIPS 2020: 1-10 - [c13]Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark E. Campbell, Kilian Q. Weinberger:
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. ICLR 2020 - [c12]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Parametric Gaussian Process Regressors. ICML 2020: 4702-4712 - [c11]Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger:
Identifying Mislabeled Data using the Area Under the Margin Ranking. NeurIPS 2020 - [c10]Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner:
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. NeurIPS 2020 - [c9]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. UAI 2020: 789-798 - [i15]Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger:
Convolutional Networks with Dense Connectivity. CoRR abs/2001.02394 (2020) - [i14]Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger:
Identifying Mislabeled Data using the Area Under the Margin Ranking. CoRR abs/2001.10528 (2020) - [i13]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. CoRR abs/2002.09112 (2020) - [i12]Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner:
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR abs/2006.11267 (2020) - [i11]Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. CoRR abs/2011.05231 (2020)
2010 – 2019
- 2019
- [c8]Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson:
Exact Gaussian Processes on a Million Data Points. NeurIPS 2019: 14622-14632 - [i10]Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson:
Exact Gaussian Processes on a Million Data Points. CoRR abs/1903.08114 (2019) - [i9]Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger:
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. CoRR abs/1906.06310 (2019) - [i8]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Sparse Gaussian Process Regression Beyond Variational Inference. CoRR abs/1910.07123 (2019) - 2018
- [c7]Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson:
Product Kernel Interpolation for Scalable Gaussian Processes. AISTATS 2018: 1407-1416 - [c6]Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson:
Constant-Time Predictive Distributions for Gaussian Processes. ICML 2018: 4111-4120 - [c5]Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Gordon Wilson:
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. NeurIPS 2018: 7587-7597 - [i7]Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson:
Product Kernel Interpolation for Scalable Gaussian Processes. CoRR abs/1802.08903 (2018) - [i6]Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson:
Constant-Time Predictive Distributions for Gaussian Processes. CoRR abs/1803.06058 (2018) - [i5]Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson:
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. CoRR abs/1809.11165 (2018) - 2017
- [c4]Paul Upchurch, Jacob R. Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Q. Weinberger:
Deep Feature Interpolation for Image Content Changes. CVPR 2017: 6090-6099 - [c3]Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger:
Snapshot Ensembles: Train 1, Get M for Free. ICLR (Poster) 2017 - [c2]Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger:
On Calibration of Modern Neural Networks. ICML 2017: 1321-1330 - [c1]Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger:
On Fairness and Calibration. NIPS 2017: 5680-5689 - [i4]Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger:
Snapshot Ensembles: Train 1, get M for free. CoRR abs/1704.00109 (2017) - [i3]Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger:
On Calibration of Modern Neural Networks. CoRR abs/1706.04599 (2017) - [i2]Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger:
Memory-Efficient Implementation of DenseNets. CoRR abs/1707.06990 (2017) - [i1]Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger:
On Fairness and Calibration. CoRR abs/1709.02012 (2017)
Coauthor Index
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last updated on 2024-10-04 21:00 CEST by the dblp team
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