Geoff Pleiss - RankMe

72 papers A* 23A 4Journal 42Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning. CoRR Maksym Taranukhin, Shuyue Stella Li, Evangelos E. Milios, Geoff Pleiss, Yulia Tsvetkov, Vered Shwartz
2025	J	`jnl`	Asymmetric Duos: Sidekicks Improve Uncertainty. CoRR Tim G. Zhou, Evan Shelhamer, Geoff Pleiss
2025	J	`jnl`	Limits of PRM-Guided Tree Search for Mathematical Reasoning with LLMs. CoRR Tristan Cinquin, Geoff Pleiss, Agustinus Kristiadi
2025	A*	`conf`	Theoretical Limitations of Ensembles in the Age of Overparameterization. ICML Niclas Dern, John Patrick Cunningham, Geoff Pleiss
2025	J	`jnl`	We Still Don't Understand High-Dimensional Bayesian Optimization. CoRR Colin Doumont, Donney Fan, Natalie Maus, Jacob R. Gardner, Henry B. Moss, Geoff Pleiss
2024	A*	`conf`	A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? ICML Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss
2024	J	`jnl`	A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? CoRR Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss
2024	A*	`conf`	Approximation-Aware Bayesian Optimization. NeurIPS Natalie Maus, Kyurae Kim, David Eriksson, Geoff Pleiss, John P. Cunningham, Jacob R. Gardner
2024	J	`jnl`	Approximation-Aware Bayesian Optimization. CoRR Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
2024	A*	`conf`	Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. NeurIPS Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
2024	J	`jnl`	Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. CoRR Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
2024	J	`jnl`	How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
2024	A	`conf`	Large-Scale Gaussian Processes via Alternating Projection. AISTATS Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner
2024	A*	`conf`	Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. ICML Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss
2024	J	`jnl`	Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. CoRR Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss
2024	J	`jnl`	MCMC-driven learning. CoRR Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic
2024	J	`jnl`	Online Continual Learning of Video Diffusion Models From a Single Video Stream. CoRR Jason Yoo, Dylan Green, Geoff Pleiss, Frank Wood
2024	J	`jnl`	Pathologies of Predictive Diversity in Deep Ensembles. Trans. Mach. Learn. Res. Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
2024	J	`jnl`	Theoretical Limitations of Ensembles in the Age of Overparameterization. CoRR Niclas Dern, John P. Cunningham, Geoff Pleiss
2023	A*	`conf`	CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. NeurIPS Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson
2023	J	`jnl`	CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. CoRR Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson
2023	J	`jnl`	Large-Scale Gaussian Processes via Alternating Projection. CoRR Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner
2023	J	`jnl`	Pathologies of Predictive Diversity in Deep Ensembles. CoRR Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
2023	A*	`conf`	Sharp Calibrated Gaussian Processes. NeurIPS Alexandre Capone, Sandra Hirche, Geoff Pleiss
2023	J	`jnl`	Sharp Calibrated Gaussian Processes. CoRR Alexandre Capone, Geoff Pleiss, Sandra Hirche
2022	J	`jnl`	Convolutional Networks with Dense Connectivity. IEEE Trans. Pattern Anal. Mach. Intell. Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
2022	A*	`conf`	Deep Ensembles Work, But Are They Necessary? NeurIPS Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
2022	J	`jnl`	Deep Ensembles Work, But Are They Necessary? CoRR Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
2022	A*	`conf`	Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
2022	J	`jnl`	Posterior and Computational Uncertainty in Gaussian Processes. CoRR Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
2022	A*	`conf`	Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
2022	J	`jnl`	Variational Nearest Neighbor Gaussian Processes. CoRR Luhuan Wu, Geoff Pleiss, John P. Cunningham
2022	A*	`conf`	Variational nearest neighbor Gaussian process. ICML Luhuan Wu, Geoff Pleiss, John P. Cunningham
2021	A*	`conf`	Bias-Free Scalable Gaussian Processes via Randomized Truncations. ICML Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Bias-Free Scalable Gaussian Processes via Randomized Truncations. CoRR Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
2021	A	`conf`	Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
2021	A*	`conf`	Rectangular Flows for Manifold Learning. NeurIPS Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Rectangular Flows for Manifold Learning. CoRR Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
2021	J	`jnl`	Scalable Cross Validation Losses for Gaussian Process Models. CoRR Martin Jankowiak, Geoff Pleiss
2021	A*	`conf`	The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. NeurIPS Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. CoRR Geoff Pleiss, John P. Cunningham
2020	J	`jnl`	Convolutional Networks with Dense Connectivity. CoRR Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
2020	A	`conf`	Deep Sigma Point Processes. UAI Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2020	J	`jnl`	Deep Sigma Point Processes. CoRR Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2020	A*	`conf`	Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. NeurIPS Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
2020	J	`jnl`	Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
2020	A*	`conf`	Identifying Mislabeled Data using the Area Under the Margin Ranking. NeurIPS Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger
2020	J	`jnl`	Identifying Mislabeled Data using the Area Under the Margin Ranking. CoRR Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger
2020	A*	`conf`	Parametric Gaussian Process Regressors. ICML Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2020	A*	`conf`	Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. ICLR Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark E. Campbell, Kilian Q. Weinberger
2020	—	`conf`	Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. ICBINB@NeurIPS Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2020	J	`jnl`	Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. CoRR Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2019	A*	`conf`	Exact Gaussian Processes on a Million Data Points. NeurIPS Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
2019	J	`jnl`	Exact Gaussian Processes on a Million Data Points. CoRR Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
2019	J	`jnl`	Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. CoRR Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
2019	J	`jnl`	Sparse Gaussian Process Regression Beyond Variational Inference. CoRR Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2018	A*	`conf`	Constant-Time Predictive Distributions for Gaussian Processes. ICML Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	J	`jnl`	Constant-Time Predictive Distributions for Gaussian Processes. CoRR Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	A*	`conf`	GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. NeurIPS Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Gordon Wilson
2018	J	`jnl`	GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. CoRR Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	A	`conf`	Product Kernel Interpolation for Scalable Gaussian Processes. AISTATS Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	J	`jnl`	Product Kernel Interpolation for Scalable Gaussian Processes. CoRR Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
2017	A*	`conf`	Deep Feature Interpolation for Image Content Changes. CVPR Paul Upchurch, Jacob R. Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Q. Weinberger
2017	J	`jnl`	Memory-Efficient Implementation of DenseNets. CoRR Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger
2017	A*	`conf`	On Calibration of Modern Neural Networks. ICML Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017	J	`jnl`	On Calibration of Modern Neural Networks. CoRR Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017	—	`conf`	On Fairness and Calibration. NIPS Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger
2017	J	`jnl`	On Fairness and Calibration. CoRR Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger
2017	—	`conf`	Snapshot Ensembles: Train 1, Get M for Free. ICLR (Poster) Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger
2017	J	`jnl`	Snapshot Ensembles: Train 1, get M for free. CoRR Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger