Andrew Gordon Wilson

266 papers A* 80A 17Journal 148Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence. CoRR Marc Finzi, Shikai Qiu, Yiding Jiang, Pavel Izmailov, J. Zico Kolter, Andrew Gordon Wilson
2026	J	`jnl`	Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables. CoRR Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Shankar Ramasubramanian, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Mengfei Cao, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov
2026	J	`jnl`	Zero-shot Forecasting by Simulation Alone. CoRR Boris N. Oreshkin, Mayank Jauhari, Ravi Kiran Selvam, Malcolm Wolff, Wenhao Pan, Shankar Ramasubramanian, Kin G. Olivares, Tatiana Konstantinova, Andres Potapczynski, Mengfei Cao, Dmitry Efimov, Michael W. Mahoney, Andrew Gordon Wilson
2025	J	`jnl`	A Diffusion Model to Shrink Proteins While Maintaining Their Function. CoRR Ethan Baron, Alan Nawzad Amin, Ruben Weitzman, Debora S. Marks, Andrew Gordon Wilson
2025	J	`jnl`	A Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models. CoRR Kin G. Olivares, Malcolm Wolff, Tatiana Konstantinova, Shankar Ramasubramanian, Andrew Gordon Wilson, Andres Potapczynski, Willa Potosnak, Mengfei Cao, Boris N. Oreshkin, Dmitry Efimov
2025	J	`jnl`	A Unification of Discrete, Gaussian, and Simplicial Diffusion. CoRR Nuria Alina Chandra, Yucen Lily Li, Alan Nawzad Amin, Alex Ali, Joshua Rollins, Sebastian W. Ober, Aniruddh Raghu, Andrew Gordon Wilson
2025	A*	`conf`	Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences. ICLR Alan Nawzad Amin, Nate Gruver, Yilun Kuang, Yucen Lily Li, Hunter Elliott, Calvin McCarter, Aniruddh Raghu, Peyton Greenside, Andrew Gordon Wilson
2025	J	`jnl`	Compute-Optimal LLMs Provably Generalize Better With Scale. CoRR Marc Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson
2025	A*	`conf`	Compute-Optimal LLMs Provably Generalize Better with Scale. ICLR Marc Anton Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson
2025	A*	`conf`	Customizing the Inductive Biases of Softmax Attention using Structured Matrices. ICML Yilun Kuang, Noah Amsel, Sanae Lotfi, Shikai Qiu, Andres Potapczynski, Andrew Gordon Wilson
2025	J	`jnl`	Customizing the Inductive Biases of Softmax Attention using Structured Matrices. CoRR Yilun Kuang, Noah Amsel, Sanae Lotfi, Shikai Qiu, Andres Potapczynski, Andrew Gordon Wilson
2025	J	`jnl`	Deep Learning is Not So Mysterious or Different. CoRR Andrew Gordon Wilson
2025	J	`jnl`	Does Multimodality Lead to Better Time Series Forecasting? CoRR Xiyuan Zhang, Boran Han, Haoyang Fang, Abdul Fatir Ansari, Shuai Zhang, Danielle C. Maddix, Cuixiong Hu, Andrew Gordon Wilson, Michael W. Mahoney, Hao Wang, Yan Liu, Huzefa Rangwala, George Karypis, Bernie Wang
2025	J	`jnl`	Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models. CoRR Ethan Baron, Boris N. Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova
2025	A*	`conf`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. ICML Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Sundar Rangapuram, Danielle C. Maddix, Bernie Wang
2025	A	`conf`	Fine-Tuning with Uncertainty-Aware Priors Makes Vision and Language Foundation Models More Reliable. AISTATS Tim G. J. Rudner, Xiang Pan, Yucen Lily Li, Ravid Shwartz-Ziv, Andrew Gordon Wilson
2025	J	`jnl`	Hyperparameter Transfer Enables Consistent Gains of Matrix-Preconditioned Optimizers Across Scales. CoRR Shikai Qiu, Zixi Chen, Hoang Phan, Qi Lei, Andrew Gordon Wilson
2025	J	`jnl`	In-Context Clustering with Large Language Models. CoRR Ying Wang, Mengye Ren, Andrew Gordon Wilson
2025	J	`jnl`	Is Sequence Information All You Need for Bayesian Optimization of Antibodies? CoRR Sebastian W. Ober, Calvin McCarter, Aniruddh Raghu, Yucen Lily Li, Alan Nawzad Amin, Andrew Gordon Wilson, Hunter Elliott
2025	J	`jnl`	Out-of-Distribution Detection Methods Answer the Wrong Questions. CoRR Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson
2025	—	`conf`	Position: Deep Learning is Not So Mysterious or Different. ICML (Position Papers) Andrew Gordon Wilson
2025	—	`conf`	Position: Supervised Classifiers Answer the Wrong Questions for OOD Detection. ICML (Position Papers) Yucen Lily Li, Daohan Lu, Polina Kirichenko, Shikai Qiu, Tim G. J. Rudner, C. Bayan Bruss, Andrew Gordon Wilson
2025	J	`jnl`	Reliable and Responsible Foundation Models. Trans. Mach. Learn. Res. Xinyu Yang, Junlin Han, Rishi Bommasani, Jinqi Luo, Wenjie Qu, Wangchunshu Zhou, Adel Bibi, Xiyao Wang, Jaehong Yoon, Elias Stengel-Eskin, Shengbang Tong, Lingfeng Shen, Rafael Rafailov, Runjia Li, Zhaoyang Wang, Yiyang Zhou, Chenhang Cui, Yu Wang, Wenhao Zheng, Huichi Zhou, Jindong Gu, Zhaorun Chen, Peng Xia, Tony Lee, Thomas P. Zollo, Vikash Sehwag, Jixuan Leng, Jiuhai Chen, Yuxin Wen, Huan Zhang, Zhun Deng, Linjun Zhang, Pavel Izmailov, Pang Wei Koh, Yulia Tsvetkov, Andrew Gordon Wilson, Jiaheng Zhang, James Zou, Cihang Xie, Hao Wang, Philip Torr, Julian J. McAuley, David Alvarez-Melis, Florian Tramèr, Kaidi Xu, Suman Jana, Chris Callison-Burch, René Vidal, Filippos Kokkinos, Mohit Bansal, Beidi Chen, Huaxiu Yao
2025	J	`jnl`	Response to Promises and Pitfalls of Deep Kernel Learning. CoRR Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing
2025	A*	`conf`	Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks. ICML Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson, Jeffrey Pennington, Atish Agarwala
2025	J	`jnl`	Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks. CoRR Shikai Qiu, Lechao Xiao, Andrew Gordon Wilson, Jeffrey Pennington, Atish Agarwala
2025	J	`jnl`	Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful. CoRR Martin Marek, Sanae Lotfi, Aditya Somasundaram, Andrew Gordon Wilson, Micah Goldblum
2025	J	`jnl`	The Forecast Critic: Leveraging Large Language Models for Poor Forecast Identification. CoRR Luke Bhan, Hanyu Zhang, Andrew Gordon Wilson, Michael W. Mahoney, Chuck Arvin
2025	A*	`conf`	Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra. ICML Alan Nawzad Amin, Andres Potapczynski, Andrew Gordon Wilson
2025	J	`jnl`	Training Flexible Models of Genetic Variant Effects from Functional Annotations using Accelerated Linear Algebra. CoRR Alan Nawzad Amin, Andres Potapczynski, Andrew Gordon Wilson
2025	J	`jnl`	Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes. CoRR Mayuka Jayawardhana, Renbo Tu, Samuel Dooley, Valeriia Cherepanova, Andrew Gordon Wilson, Frank Hutter, Colin White, Tom Goldstein, Micah Goldblum
2025	J	`jnl`	Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility. CoRR Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, Yuyang Wang
2025	J	`jnl`	Understanding the Implicit Biases of Design Choices for Time Series Foundation Models. CoRR Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, Yuyang Wang
2025	J	`jnl`	When Should We Orchestrate Multiple Agents? CoRR Umang Bhatt, Sanyam Kapoor, Mihir Upadhyay, Ilia Sucholutsky, Francesco Quinzan, Katherine M. Collins, Adrian Weller, Andrew Gordon Wilson, Muhammad Bilal Zafar
2025	J	`jnl`	Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion. CoRR Alan Nawzad Amin, Nate Gruver, Andrew Gordon Wilson
2024	A*	`conf`	A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. ICLR Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
2024	J	`jnl`	Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences. CoRR Alan Nawzad Amin, Nate Gruver, Yilun Kuang, Yucen Lily Li, Hunter Elliott, Calvin McCarter, Aniruddh Raghu, Peyton Greenside, Andrew Gordon Wilson
2024	J	`jnl`	Chronos: Learning the Language of Time Series. CoRR Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
2024	J	`jnl`	Chronos: Learning the Language of Time Series. Trans. Mach. Learn. Res. Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Hao Wang, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Bernie Wang
2024	A*	`conf`	Compute Better Spent: Replacing Dense Layers with Structured Matrices. ICML Shikai Qiu, Andres Potapczynski, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
2024	J	`jnl`	Compute Better Spent: Replacing Dense Layers with Structured Matrices. CoRR Shikai Qiu, Andres Potapczynski, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson
2024	A*	`conf`	Controllable Prompt Tuning For Balancing Group Distributional Robustness. ICML Hoang Phan, Andrew Gordon Wilson, Qi Lei
2024	J	`jnl`	Controllable Prompt Tuning For Balancing Group Distributional Robustness. CoRR Hoang Phan, Andrew Gordon Wilson, Qi Lei
2024	J	`jnl`	Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. CoRR Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Sundar Rangapuram, Danielle C. Maddix, Yuyang Wang
2024	A*	`conf`	Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. ICLR Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary W. Ulissi
2024	J	`jnl`	Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. CoRR Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary W. Ulissi
2024	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2024	J	`jnl`	Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. CoRR Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum
2024	J	`jnl`	Just How Flexible are Neural Networks in Practice? CoRR Ravid Shwartz-Ziv, Micah Goldblum, Arpit Bansal, C. Bayan Bruss, Yann LeCun, Andrew Gordon Wilson
2024	J	`jnl`	LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data. CoRR Hanyu Zhang, Chuck Arvin, Dmitry Efimov, Michael W. Mahoney, Dominique Perrault-Joncas, Shankar Ramasubramanian, Andrew Gordon Wilson, Malcolm Wolff
2024	A*	`conf`	Large Language Models Must Be Taught to Know What They Don't Know. NeurIPS Sanyam Kapoor, Nate Gruver, Manley Roberts, Katie Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson
2024	J	`jnl`	Large Language Models Must Be Taught to Know What They Don't Know. CoRR Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine M. Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson
2024	A	`conf`	Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. AISTATS Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
2024	J	`jnl`	Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. CoRR Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
2024	A*	`conf`	Modeling Caption Diversity in Contrastive Vision-Language Pretraining. ICML Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mido Assran, Andrew Gordon Wilson, Aaron C. Courville, Nicolas Ballas
2024	J	`jnl`	Modeling Caption Diversity in Contrastive Vision-Language Pretraining. CoRR Samuel Lavoie, Polina Kirichenko, Mark Ibrahim, Mahmoud Assran, Andrew Gordon Wilson, Aaron C. Courville, Nicolas Ballas
2024	A*	`conf`	Non-Vacuous Generalization Bounds for Large Language Models. ICML Sanae Lotfi, Marc Anton Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
2024	J	`jnl`	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	A*	`conf`	Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
2024	A*	`conf`	Position: The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. ICML Micah Goldblum, Marc Anton Finzi, Keefer Rowan, Andrew Gordon Wilson
2024	A*	`conf`	Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency. ICML Alan Nawzad Amin, Andrew Gordon Wilson
2024	J	`jnl`	Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency. CoRR Alan Nawzad Amin, Andrew Gordon Wilson
2024	A*	`conf`	Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. NeurIPS Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Charlie Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson
2024	J	`jnl`	Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. CoRR Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Zixi Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson
2024	A*	`conf`	Transferring Knowledge From Large Foundation Models to Small Downstream Models. ICML Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Bernie Wang, Andrew Gordon Wilson
2024	J	`jnl`	Transferring Knowledge from Large Foundation Models to Small Downstream Models. CoRR Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Yuyang Wang, Andrew Gordon Wilson
2024	J	`jnl`	Understanding the Detrimental Class-level Effects of Data Augmentation. CoRR Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson
2024	A*	`conf`	Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. NeurIPS Sanae Lotfi, Yilun Kuang, Marc Finzi, Brandon Amos, Micah Goldblum, Andrew Gordon Wilson
2024	J	`jnl`	Unlocking Tokens as Data Points for Generalization Bounds on Larger Language Models. CoRR Sanae Lotfi, Yilun Kuang, Brandon Amos, Micah Goldblum, Marc Finzi, Andrew Gordon Wilson
2023	J	`jnl`	A Cookbook of Self-Supervised Learning. CoRR Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Grégoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum
2023	A*	`conf`	A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
2023	J	`jnl`	A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum
2023	A*	`conf`	A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. ICLR Marc Anton Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson
2023	J	`jnl`	A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. CoRR Marc Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson
2023	J	`jnl`	A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. CoRR Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
2023	—	`conf`	Automated Few-Shot Classification with Instruction-Finetuned Language Models. EMNLP (Findings) Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson
2023	J	`jnl`	Automated Few-shot Classification with Instruction-Finetuned Language Models. CoRR Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson
2023	A*	`conf`	Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein
2023	J	`jnl`	Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein
2023	A	`conf`	Bayesian Optimization with Conformal Prediction Sets. AISTATS Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson
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`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2023	A*	`conf`	Function-Space Regularization in Neural Networks: A Probabilistic Perspective. ICML Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
2023	J	`jnl`	Function-Space Regularization in Neural Networks: A Probabilistic Perspective. CoRR Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
2023	A*	`conf`	How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
2023	A*	`conf`	Large Language Models Are Zero-Shot Time Series Forecasters. NeurIPS Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson
2023	J	`jnl`	Large Language Models Are Zero-Shot Time Series Forecasters. CoRR Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson
2023	A*	`conf`	Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. ICLR Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
2023	A*	`conf`	Learning Multimodal Data Augmentation in Feature Space. ICLR Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
2023	J	`jnl`	Materials Expert-Artificial Intelligence for Materials Discovery. CoRR Yanjun Liu, Milena Jovanovic, Krishnanand Mallayya, Wesley J. Maddox, Andrew Gordon Wilson, Sebastian Klemenz, Leslie M. Schoop, Eun-Ah Kim
2023	J	`jnl`	Non-Vacuous Generalization Bounds for Large Language Models. CoRR Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson
2023	J	`jnl`	Perspectives on the State and Future of Deep Learning - 2023. CoRR Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson
2023	A*	`conf`	Protein Design with Guided Discrete Diffusion. NeurIPS Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
2023	J	`jnl`	Protein Design with Guided Discrete Diffusion. CoRR Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
2023	A*	`conf`	Should We Learn Most Likely Functions or Parameters? NeurIPS Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
2023	J	`jnl`	Should We Learn Most Likely Functions or Parameters? CoRR Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
2023	A*	`conf`	Simple and Fast Group Robustness by Automatic Feature Reweighting. ICML Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson
2023	J	`jnl`	Simple and Fast Group Robustness by Automatic Feature Reweighting. CoRR Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson
2023	A*	`conf`	Simplifying Neural Network Training Under Class Imbalance. NeurIPS Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
2023	J	`jnl`	Simplifying Neural Network Training Under Class Imbalance. CoRR Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson
2023	A*	`conf`	The Lie Derivative for Measuring Learned Equivariance. ICLR Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
2023	J	`jnl`	The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. CoRR Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson
2023	A*	`conf`	Transfer Learning with Deep Tabular Models. ICLR Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
2023	A*	`conf`	Understanding the detrimental class-level effects of data augmentation. NeurIPS Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson
2023	A*	`conf`	User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. ICML Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez
2023	J	`jnl`	User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. CoRR Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez
2023	A*	`conf`	Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. NeurIPS Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
2023	J	`jnl`	Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. CoRR Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
2022	A*	`conf`	Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. ICML Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
2022	J	`jnl`	Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. CoRR Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
2022	A*	`conf`	Bayesian Model Selection, the Marginal Likelihood, and Generalization. ICML Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
2022	J	`jnl`	Bayesian Model Selection, the Marginal Likelihood, and Generalization. CoRR Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson
2022	J	`jnl`	Bayesian Optimization with Conformal Coverage Guarantees. CoRR Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson
2022	A*	`conf`	Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. NeurIPS Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
2022	J	`jnl`	Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. CoRR Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson
2022	A*	`conf`	Deconstructing the Inductive Biases of Hamiltonian Neural Networks. ICLR Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson
2022	J	`jnl`	Deconstructing the Inductive Biases of Hamiltonian Neural Networks. CoRR Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson
2022	J	`jnl`	How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson
2022	J	`jnl`	K-SAM: Sharpness-Aware Minimization at the Speed of SGD. CoRR Renkun Ni, Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein
2022	J	`jnl`	Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. CoRR Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
2022	J	`jnl`	Learning Multimodal Data Augmentation in Feature Space. CoRR Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson
2022	J	`jnl`	Low-Precision Arithmetic for Fast Gaussian Processes. CoRR Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson
2022	A*	`conf`	Low-Precision Stochastic Gradient Langevin Dynamics. ICML Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa
2022	J	`jnl`	Low-Precision Stochastic Gradient Langevin Dynamics. CoRR Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa
2022	A	`conf`	Low-precision arithmetic for fast Gaussian processes. UAI Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson
2022	A*	`conf`	On Feature Learning in the Presence of Spurious Correlations. NeurIPS Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
2022	J	`jnl`	On Feature Learning in the Presence of Spurious Correlations. CoRR Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
2022	A*	`conf`	On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. NeurIPS Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
2022	J	`jnl`	On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. CoRR Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson
2022	A*	`conf`	PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. NeurIPS Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
2022	J	`jnl`	PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. CoRR Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson
2022	A*	`conf`	Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
2022	J	`jnl`	Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson
2022	J	`jnl`	The Lie Derivative for Measuring Learned Equivariance. CoRR Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson
2022	J	`jnl`	Transfer Learning with Deep Tabular Models. CoRR Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
2022	A*	`conf`	Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. ICML Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson
2022	J	`jnl`	Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. CoRR Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson
2022	J	`jnl`	What do Vision Transformers Learn? A Visual Exploration. CoRR Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein
2021	A*	`conf`	A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. ICML Marc Finzi, Max Welling, Andrew Gordon Wilson
2021	J	`jnl`	A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. CoRR Marc Finzi, Max Welling, Andrew Gordon Wilson
2021	A*	`conf`	Bayesian Optimization with High-Dimensional Outputs. NeurIPS Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
2021	J	`jnl`	Bayesian Optimization with High-Dimensional Outputs. CoRR Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
2021	A*	`conf`	Conditioning Sparse Variational Gaussian Processes for Online Decision-making. NeurIPS Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
2021	J	`jnl`	Conditioning Sparse Variational Gaussian Processes for Online Decision-making. CoRR Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson
2021	A*	`conf`	Dangers of Bayesian Model Averaging under Covariate Shift. NeurIPS Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson
2021	J	`jnl`	Dangers of Bayesian Model Averaging under Covariate Shift. CoRR Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson
2021	A*	`conf`	Does Knowledge Distillation Really Work? NeurIPS Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
2021	J	`jnl`	Does Knowledge Distillation Really Work? CoRR Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson
2021	—	`conf`	Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) Andrew Gordon Wilson, Pavel Izmailov, Matthew D. Hoffman, Yarin Gal, Yingzhen Li, Melanie F. Pradier, Sharad Vikram, Andrew Y. K. Foong, Sanae Lotfi, Sebastian Farquhar
2021	A	`conf`	Fast Adaptation with Linearized Neural Networks. AISTATS Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou
2021	J	`jnl`	Fast Adaptation with Linearized Neural Networks. CoRR Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou
2021	A	`conf`	Kernel Interpolation for Scalable Online Gaussian Processes. AISTATS Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson
2021	J	`jnl`	Kernel Interpolation for Scalable Online Gaussian Processes. CoRR Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson
2021	A*	`conf`	Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. ICML Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson
2021	J	`jnl`	Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. CoRR Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson
2021	—	`conf`	On the model-based stochastic value gradient for continuous reinforcement learning. L4DC Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson
2021	A*	`conf`	Residual Pathway Priors for Soft Equivariance Constraints. NeurIPS Marc Finzi, Greg Benton, Andrew Gordon Wilson
2021	J	`jnl`	Residual Pathway Priors for Soft Equivariance Constraints. CoRR Marc Finzi, Gregory W. Benton, Andrew Gordon Wilson
2021	A*	`conf`	SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. ICML Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson
2021	J	`jnl`	SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. CoRR Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson
2021	A*	`conf`	Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. ICML Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse
2021	J	`jnl`	Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. CoRR Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse
2021	J	`jnl`	Task-agnostic Continual Learning with Hybrid Probabilistic Models. CoRR Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu
2021	A*	`conf`	What Are Bayesian Neural Network Posteriors Really Like? ICML Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
2021	J	`jnl`	What Are Bayesian Neural Network Posteriors Really Like? CoRR Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
2021	J	`jnl`	When are Iterative Gaussian Processes Reliably Accurate? CoRR Wesley J. Maddox, Sanyam Kapoor, Andrew Gordon Wilson
2020	A*	`conf`	Bayesian Deep Learning and a Probabilistic Perspective of Generalization. NeurIPS Andrew Gordon Wilson, Pavel Izmailov
2020	J	`jnl`	Bayesian Deep Learning and a Probabilistic Perspective of Generalization. CoRR Andrew Gordon Wilson, Pavel Izmailov
2020	A*	`conf`	BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
2020	A*	`conf`	Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. ICLR Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2020	A*	`conf`	Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. ICML Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
2020	J	`jnl`	Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. CoRR Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson
2020	A*	`conf`	Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. NeurIPS Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu
2020	J	`jnl`	Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. CoRR Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu
2020	A*	`conf`	Learning Invariances in Neural Networks from Training Data. NeurIPS Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
2020	J	`jnl`	Learning Invariances in Neural Networks. CoRR Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
2020	J	`jnl`	On the model-based stochastic value gradient for continuous reinforcement learning. CoRR Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson
2020	A*	`conf`	Randomly Projected Additive Gaussian Processes for Regression. ICML Ian A. Delbridge, David Bindel, Andrew Gordon Wilson
2020	J	`jnl`	Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited. CoRR Wesley J. Maddox, Gregory W. Benton, Andrew Gordon Wilson
2020	A*	`conf`	Semi-Supervised Learning with Normalizing Flows. ICML Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson
2020	A*	`conf`	Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. NeurIPS Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson
2020	J	`jnl`	Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. CoRR Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson
2020	J	`jnl`	The Case for Bayesian Deep Learning. CoRR Andrew Gordon Wilson
2020	A*	`conf`	Why Normalizing Flows Fail to Detect Out-of-Distribution Data. NeurIPS Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
2020	J	`jnl`	Why Normalizing Flows Fail to Detect Out-of-Distribution Data. CoRR Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson
2019	A*	`conf`	A Simple Baseline for Bayesian Uncertainty in Deep Learning. NeurIPS Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
2019	J	`jnl`	A Simple Baseline for Bayesian Uncertainty in Deep Learning. CoRR Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry P. Vetrov, Andrew Gordon Wilson
2019	J	`jnl`	BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
2019	J	`jnl`	Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. J. Mach. Learn. Res. William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson
2019	J	`jnl`	Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. CoRR Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
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	A*	`conf`	Function-Space Distributions over Kernels. NeurIPS Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson
2019	J	`jnl`	Function-Space Distributions over Kernels. CoRR Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson
2019	A	`conf`	Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning. UAI Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson
2019	J	`jnl`	Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning. CoRR Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson
2019	J	`jnl`	Randomly Projected Additive Gaussian Processes for Regression. CoRR Ian A. Delbridge, David S. Bindel, Andrew Gordon Wilson
2019	A*	`conf`	SWALP : Stochastic Weight Averaging in Low Precision Training. ICML Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa
2019	J	`jnl`	SWALP : Stochastic Weight Averaging in Low-Precision Training. CoRR Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa
2019	J	`jnl`	Semi-Supervised Learning with Normalizing Flows. CoRR Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson
2019	A*	`conf`	Simple Black-box Adversarial Attacks. ICML Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
2019	J	`jnl`	Simple Black-box Adversarial Attacks. CoRR Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
2019	A	`conf`	Subspace Inference for Bayesian Deep Learning. UAI Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
2019	J	`jnl`	Subspace Inference for Bayesian Deep Learning. CoRR Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
2019	—	`conf`	There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. ICLR (Poster) Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
2018	A*	`conf`	Automated Local Regression Discontinuity Design Discovery. KDD William Herlands, Edward McFowland III, Andrew Gordon Wilson, Daniel B. Neill
2018	A	`conf`	Averaging Weights Leads to Wider Optima and Better Generalization. UAI Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
2018	J	`jnl`	Averaging Weights Leads to Wider Optima and Better Generalization. CoRR Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
2018	J	`jnl`	Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. CoRR William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson
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`	Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. AISTATS William Herlands, Edward McFowland, Andrew Gordon Wilson, Daniel B. Neill
2018	J	`jnl`	Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. CoRR William Herlands, Edward McFowland III, Andrew Gordon Wilson, Daniel B. Neill
2018	—	`conf`	Hierarchical Density Order Embeddings. ICLR (Poster) Ben Athiwaratkun, Andrew Gordon Wilson
2018	J	`jnl`	Hierarchical Density Order Embeddings. CoRR Ben Athiwaratkun, Andrew Gordon Wilson
2018	J	`jnl`	Improving Consistency-Based Semi-Supervised Learning with Weight Averaging. CoRR Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson
2018	A*	`conf`	Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson
2018	J	`jnl`	Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. CoRR Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson
2018	—	`conf`	Probabilistic FastText for Multi-Sense Word Embeddings. ACL (1) Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar
2018	J	`jnl`	Probabilistic FastText for Multi-Sense Word Embeddings. CoRR Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar
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
2018	J	`jnl`	Scalable Lévy Process Priors for Spectral Kernel Learning. CoRR Phillip A. Jang, Andrew E. Loeb, Matthew B. Davidow, Andrew Gordon Wilson
2018	A*	`conf`	Scaling Gaussian Process Regression with Derivatives. NeurIPS David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson
2018	J	`jnl`	Scaling Gaussian Process Regression with Derivatives. CoRR David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson
2017	—	`conf`	Bayesian GAN. NIPS Yunus Saatci, Andrew Gordon Wilson
2017	J	`jnl`	Bayesian GAN. CoRR Yunus Saatchi, Andrew Gordon Wilson
2017	—	`conf`	Bayesian Optimization with Gradients. NIPS Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier
2017	J	`jnl`	Bayesian Optimization with Gradients. CoRR Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier
2017	—	`conf`	Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever. BRACIS Julio Albinati, Wagner Meira Jr., Gisele L. Pappa, Andrew Gordon Wilson
2017	J	`jnl`	Learning Scalable Deep Kernels with Recurrent Structure. J. Mach. Learn. Res. Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing
2017	—	`conf`	Multimodal Word Distributions. ACL (1) Ben Athiwaratkun, Andrew Gordon Wilson
2017	J	`jnl`	Multimodal Word Distributions. CoRR Ben Athiwaratkun, Andrew Gordon Wilson
2017	—	`conf`	Scalable Levy Process Priors for Spectral Kernel Learning. NIPS Phillip A. Jang, Andrew E. Loeb, Matthew B. Davidow, Andrew Gordon Wilson
2017	—	`conf`	Scalable Log Determinants for Gaussian Process Kernel Learning. NIPS Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson
2017	J	`jnl`	Scalable Log Determinants for Gaussian Process Kernel Learning. CoRR Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson
2016	A	`conf`	Bayesian Nonparametric Kernel-Learning. AISTATS Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing
2016	A	`conf`	Deep Kernel Learning. AISTATS Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing
2016	J	`jnl`	Learning Scalable Deep Kernels with Recurrent Structure. CoRR Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing
2016	A	`conf`	Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. AISTATS William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing
2016	—	`conf`	Stochastic Variational Deep Kernel Learning. NIPS Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing
2016	J	`jnl`	Stochastic Variational Deep Kernel Learning. CoRR Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing
2015	A	`conf`	A la Carte - Learning Fast Kernels. AISTATS Zichao Yang, Andrew Gordon Wilson, Alexander J. Smola, Le Song
2015	J	`jnl`	Deep Kernel Learning. CoRR Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing
2015	A*	`conf`	Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. ICML Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola
2015	A*	`conf`	Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). ICML Andrew Gordon Wilson, Hannes Nickisch
2015	J	`jnl`	Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). CoRR Andrew Gordon Wilson, Hannes Nickisch
2015	—	`conf`	The Human Kernel. NIPS Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing
2015	J	`jnl`	The Human Kernel. CoRR Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing
2015	J	`jnl`	Thoughts on Massively Scalable Gaussian Processes. CoRR Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch
2014	—	`conf`	A Bayesian method to quantifying chemical composition using NMR: Application to porous media systems. EUSIPCO Yuting Wu, Daniel J. Holland, Mick D. Mantle, Andrew Gordon Wilson, Sebastian Nowozin, Andrew Blake, Lynn F. Gladden
2014	J	`jnl`	A la Carte - Learning Fast Kernels. CoRR Zichao Yang, Alexander J. Smola, Le Song, Andrew Gordon Wilson
2014	—	`conf`	Fast Kernel Learning for Multidimensional Pattern Extrapolation. NIPS Andrew Gordon Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai
2014	A	`conf`	Student-t Processes as Alternatives to Gaussian Processes. AISTATS Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani
2014	J	`jnl`	Student-t Processes as Alternatives to Gaussian Processes. CoRR Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani
2013	J	`jnl`	GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes. CoRR Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham
2013	J	`jnl`	Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation CoRR Andrew Gordon Wilson, Ryan Prescott Adams
2013	—	`conf`	Gaussian Process Kernels for Pattern Discovery and Extrapolation. ICML (3) Andrew Gordon Wilson, Ryan Prescott Adams
2012	A*	`conf`	Gaussian Process Regression Networks. ICML Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani
2012	—	`conf`	Modelling Input Varying Correlations between Multiple Responses. ECML/PKDD (2) Andrew Gordon Wilson, Zoubin Ghahramani
2011	A	`conf`	Generalised Wishart Processes. UAI Andrew Gordon Wilson, Zoubin Ghahramani
2010	—	`conf`	Copula Processes. NIPS Andrew Gordon Wilson, Zoubin Ghahramani