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ORCIDAndrew Gordon Wilson
Person information
- affiliation: New York University, New York, NY, USA
- affiliation (former): Cornell University, Ithaca, NY, USA
- affiliation (former): Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
- affiliation (former): University of Cambridge, Department of Engineering, UK
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2020 – today
- 2024
[c96]Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe:
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. AISTATS 2024: 127-135- [i107]Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson:
Understanding the Detrimental Class-level Effects of Data Augmentation. CoRR abs/2401.01764 (2024) - [i106]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:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i105]Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary W. Ulissi:
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. CoRR abs/2402.04379 (2024) - [i104]Hoang Phan, Andrew Gordon Wilson, Qi Lei:
Controllable Prompt Tuning For Balancing Group Distributional Robustness. CoRR abs/2403.02695 (2024) - [i103]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:
Chronos: Learning the Language of Time Series. CoRR abs/2403.07815 (2024) - [i102]Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe:
Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors. CoRR abs/2403.09869 (2024) - [i101]Hossein Souri, Arpit Bansal, Hamid Kazemi, Liam Fowl, Aniruddha Saha, Jonas Geiping, Andrew Gordon Wilson, Rama Chellappa, Tom Goldstein, Micah Goldblum:
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. CoRR abs/2403.16365 (2024) - 2023
- [c95]Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson:
Bayesian Optimization with Conformal Prediction Sets. AISTATS 2023: 959-986 - [c94]Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson:
Automated Few-Shot Classification with Instruction-Finetuned Language Models. EMNLP (Findings) 2023: 2414-2432 - [c93]Marc Anton Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson:
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. ICLR 2023 - [c92]Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson:
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. ICLR 2023 - [c91]Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson:
The Lie Derivative for Measuring Learned Equivariance. ICLR 2023 - [c90]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. ICLR 2023 - [c89]Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum:
Transfer Learning with Deep Tabular Models. ICLR 2023 - [c88]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. ICLR 2023 - [c87]Marc Anton Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. ICML 2023: 10136-10152 - [c86]Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson:
Simple and Fast Group Robustness by Automatic Feature Reweighting. ICML 2023: 28448-28467 - [c85]Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson:
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. ICML 2023: 29275-29290 - [c84]Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum:
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. NeurIPS 2023 - [c83]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:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. NeurIPS 2023 - [c82]Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson:
Large Language Models Are Zero-Shot Time Series Forecasters. NeurIPS 2023 - [c81]Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson:
Protein Design with Guided Discrete Diffusion. NeurIPS 2023 - [c80]Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson:
Understanding the detrimental class-level effects of data augmentation. NeurIPS 2023 - [c79]Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson:
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. NeurIPS 2023 - [c78]Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson:
Should We Learn Most Likely Functions or Parameters? NeurIPS 2023 - [c77]Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson:
Simplifying Neural Network Training Under Class Imbalance. NeurIPS 2023 - [c76]Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson:
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. NeurIPS 2023 - [i100]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR abs/2302.04019 (2023) - [i99]Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson:
The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning. CoRR abs/2304.05366 (2023) - [i98]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:
A Cookbook of Self-Supervised Learning. CoRR abs/2304.12210 (2023) - [i97]Marc Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson:
A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks. CoRR abs/2304.14994 (2023) - [i96]Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson:
Automated Few-shot Classification with Instruction-Finetuned Language Models. CoRR abs/2305.12576 (2023) - [i95]Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson:
Protein Design with Guided Discrete Diffusion. CoRR abs/2305.20009 (2023) - [i94]Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson:
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization. CoRR abs/2305.20028 (2023) - [i93]Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez:
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems. CoRR abs/2306.07526 (2023) - [i92]Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson:
Simple and Fast Group Robustness by Automatic Feature Reweighting. CoRR abs/2306.11074 (2023) - [i91]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) - [i90]Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson:
Large Language Models Are Zero-Shot Time Series Forecasters. CoRR abs/2310.07820 (2023) - [i89]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:
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks. CoRR abs/2310.19909 (2023) - [i88]Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum:
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning. CoRR abs/2311.05877 (2023) - [i87]Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson:
Should We Learn Most Likely Functions or Parameters? CoRR abs/2311.15990 (2023) - [i86]Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson:
Simplifying Neural Network Training Under Class Imbalance. CoRR abs/2312.02517 (2023) - [i85]Yanjun Liu, Milena Jovanovic, Krishnanand Mallayya, Wesley J. Maddox, Andrew Gordon Wilson, Sebastian Klemenz, Leslie M. Schoop, Eun-Ah Kim
:
Materials Expert-Artificial Intelligence for Materials Discovery. CoRR abs/2312.02796 (2023) - [i84]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (2023) - [i83]Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson:
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. CoRR abs/2312.17162 (2023) - [i82]Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson:
Non-Vacuous Generalization Bounds for Large Language Models. CoRR abs/2312.17173 (2023) - [i81]Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson:
Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution. CoRR abs/2312.17174 (2023) - 2022
- [c75]Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson:
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. ICLR 2022 - [c74]Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson:
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. ICML 2022: 1798-1816 - [c73]Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson:
Bayesian Model Selection, the Marginal Likelihood, and Generalization. ICML 2022: 14223-14247 - [c72]Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson:
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. ICML 2022: 20459-20478 - [c71]Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa:
Low-Precision Stochastic Gradient Langevin Dynamics. ICML 2022: 26624-26644 - [c70]Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson:
On Feature Learning in the Presence of Spurious Correlations. NeurIPS 2022 - [c69]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. NeurIPS 2022 - [c68]Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson:
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. NeurIPS 2022 - [c67]Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson:
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS 2022 - [c66]Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson:
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. NeurIPS 2022 - [c65]Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson:
Low-precision arithmetic for fast Gaussian processes. UAI 2022: 1306-1316 - [i80]Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson:
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. CoRR abs/2202.04836 (2022) - [i79]Sanae Lotfi, Pavel Izmailov, Gregory W. Benton, Micah Goldblum, Andrew Gordon Wilson:
Bayesian Model Selection, the Marginal Likelihood, and Generalization. CoRR abs/2202.11678 (2022) - [i78]Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson:
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. CoRR abs/2203.12742 (2022) - [i77]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. CoRR abs/2203.16481 (2022) - [i76]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations. CoRR abs/2204.02937 (2022) - [i75]Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson:
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR abs/2205.10279 (2022) - [i74]Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa:
Low-Precision Stochastic Gradient Langevin Dynamics. CoRR abs/2206.09909 (2022) - [i73]Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum:
Transfer Learning with Deep Tabular Models. CoRR abs/2206.15306 (2022) - [i72]Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson:
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. CoRR abs/2207.06544 (2022) - [i71]Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson:
Low-Precision Arithmetic for Fast Gaussian Processes. CoRR abs/2207.06856 (2022) - [i70]Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson:
The Lie Derivative for Measuring Learned Equivariance. CoRR abs/2210.02984 (2022) - [i69]Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson:
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization. CoRR abs/2210.06441 (2022) - [i68]Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson:
On Feature Learning in the Presence of Spurious Correlations. CoRR abs/2210.11369 (2022) - [i67]Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson:
Bayesian Optimization with Conformal Coverage Guarantees. CoRR abs/2210.12496 (2022) - [i66]Renkun Ni, Ping-yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
K-SAM: Sharpness-Aware Minimization at the Speed of SGD. CoRR abs/2210.12864 (2022) - [i65]Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson:
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. CoRR abs/2211.13609 (2022) - [i64]Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson:
Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. CoRR abs/2211.15231 (2022) - [i63]Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein:
What do Vision Transformers Learn? A Visual Exploration. CoRR abs/2212.06727 (2022) - [i62]Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson:
Learning Multimodal Data Augmentation in Feature Space. CoRR abs/2212.14453 (2022) - 2021
- [c64]Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou:
Fast Adaptation with Linearized Neural Networks. AISTATS 2021: 2737-2745 - [c63]Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson:
Kernel Interpolation for Scalable Online Gaussian Processes. AISTATS 2021: 3133-3141 - [c62]Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson:
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. ICML 2021: 769-779 - [c61]Marc Finzi, Max Welling, Andrew Gordon Wilson:
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. ICML 2021: 3318-3328 - [c60]Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson:
What Are Bayesian Neural Network Posteriors Really Like? ICML 2021: 4629-4640 - [c59]Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson:
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. ICML 2021: 5279-5289 - [c58]Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse:
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. ICML 2021: 9955-9965 - [c57]Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson:
On the model-based stochastic value gradient for continuous reinforcement learning. L4DC 2021: 6-20 - [c56]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:
Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) 2021: 113-124 - [c55]Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson:
Dangers of Bayesian Model Averaging under Covariate Shift. NeurIPS 2021: 3309-3322 - [c54]Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson:
Conditioning Sparse Variational Gaussian Processes for Online Decision-making. NeurIPS 2021: 6365-6379 - [c53]Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson:
Does Knowledge Distillation Really Work? NeurIPS 2021: 6906-6919 - [c52]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. NeurIPS 2021: 19274-19287 - [c51]Marc Finzi, Greg Benton, Andrew Gordon Wilson:
Residual Pathway Priors for Soft Equivariance Constraints. NeurIPS 2021: 30037-30049 - [i61]Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson:
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. CoRR abs/2102.13042 (2021) - [i60]Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou:
Fast Adaptation with Linearized Neural Networks. CoRR abs/2103.01439 (2021) - [i59]Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson:
Kernel Interpolation for Scalable Online Gaussian Processes. CoRR abs/2103.01454 (2021) - [i58]Marc Finzi, Max Welling, Andrew Gordon Wilson:
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups. CoRR abs/2104.09459 (2021) - [i57]Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson:
What Are Bayesian Neural Network Posteriors Really Like? CoRR abs/2104.14421 (2021) - [i56]Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson:
Does Knowledge Distillation Really Work? CoRR abs/2106.05945 (2021) - [i55]Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger B. Grosse:
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition. CoRR abs/2106.05992 (2021) - [i54]Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson:
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. CoRR abs/2106.06695 (2021) - [i53]Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson:
Dangers of Bayesian Model Averaging under Covariate Shift. CoRR abs/2106.11905 (2021) - [i52]Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu:
Task-agnostic Continual Learning with Hybrid Probabilistic Models. CoRR abs/2106.12772 (2021) - [i51]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. CoRR abs/2106.12997 (2021) - [i50]Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson:
Conditioning Sparse Variational Gaussian Processes for Online Decision-making. CoRR abs/2110.15172 (2021) - [i49]Marc Finzi, Gregory W. Benton, Andrew Gordon Wilson:
Residual Pathway Priors for Soft Equivariance Constraints. CoRR abs/2112.01388 (2021) - [i48]Wesley J. Maddox, Sanyam Kapoor, Andrew Gordon Wilson:
When are Iterative Gaussian Processes Reliably Accurate? CoRR abs/2112.15246 (2021) - 2020
- [c50]Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson:
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. ICLR 2020 - [c49]Ian A. Delbridge, David Bindel, Andrew Gordon Wilson:
Randomly Projected Additive Gaussian Processes for Regression. ICML 2020: 2453-2463 - [c48]Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson:
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. ICML 2020: 3165-3176 - [c47]Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson:
Semi-Supervised Learning with Normalizing Flows. ICML 2020: 4615-4630 - [c46]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS 2020 - [c45]Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
Learning Invariances in Neural Networks from Training Data. NeurIPS 2020 - [c44]Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson:
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. NeurIPS 2020 - [c43]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Why Normalizing Flows Fail to Detect Out-of-Distribution Data. NeurIPS 2020 - [c42]Andrew Gordon Wilson, Pavel Izmailov:
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. NeurIPS 2020 - [c41]Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu:
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. NeurIPS 2020 - [i47]Andrew Gordon Wilson:
The Case for Bayesian Deep Learning. CoRR abs/2001.10995 (2020) - [i46]Andrew Gordon Wilson, Pavel Izmailov:
Bayesian Deep Learning and a Probabilistic Perspective of Generalization. CoRR abs/2002.08791 (2020) - [i45]Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson:
Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data. CoRR abs/2002.12880 (2020) - [i44]Wesley J. Maddox, Gregory W. Benton, Andrew Gordon Wilson:
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited. CoRR abs/2003.02139 (2020) - [i43]Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu:
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. CoRR abs/2006.06900 (2020) - [i42]Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson:
Why Normalizing Flows Fail to Detect Out-of-Distribution Data. CoRR abs/2006.08545 (2020) - [i41]Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson:
On the model-based stochastic value gradient for continuous reinforcement learning. CoRR abs/2008.12775 (2020) - [i40]Gregory W. Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
Learning Invariances in Neural Networks. CoRR abs/2010.11882 (2020) - [i39]Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson:
Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints. CoRR abs/2010.13581 (2020)
2010 – 2019
- 2019
- [j2]William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson:
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. J. Mach. Learn. Res. 20: 99:1-99:51 (2019) - [c40]Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average. ICLR (Poster) 2019 - [c39]Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. ICML 2019: 2484-2493 - [c38]Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa:
SWALP : Stochastic Weight Averaging in Low Precision Training. ICML 2019: 7015-7024 - [c37]Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. NeurIPS 2019: 13132-13143 - [c36]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 - [c35]Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson:
Function-Space Distributions over Kernels. NeurIPS 2019: 14939-14950 - [c34]Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson:
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning. UAI 2019: 788-798 - [c33]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. UAI 2019: 1169-1179 - [i38]Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. CoRR abs/1902.02476 (2019) - [i37]Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson:
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning. CoRR abs/1902.03932 (2019) - [i36]Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson:
Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning. CoRR abs/1903.04703 (2019) - [i35]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) - [i34]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 G. 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 R. 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:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i33]Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa:
SWALP : Stochastic Weight Averaging in Low-Precision Training. CoRR abs/1904.11943 (2019) - [i32]Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. CoRR abs/1905.07121 (2019) - [i31]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. CoRR abs/1907.07504 (2019) - [i30]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR abs/1910.06403 (2019) - [i29]Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson:
Function-Space Distributions over Kernels. CoRR abs/1910.13565 (2019) - [i28]Ian A. Delbridge, David S. Bindel, Andrew Gordon Wilson:
Randomly Projected Additive Gaussian Processes for Regression. CoRR abs/1912.12834 (2019) - [i27]Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson:
Semi-Supervised Learning with Normalizing Flows. CoRR abs/1912.13025 (2019) - 2018
- [c32]Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar:
Probabilistic FastText for Multi-Sense Word Embeddings. ACL (1) 2018: 1-11 - [c31]William Herlands, Edward McFowland, Andrew Gordon Wilson, Daniel B. Neill:
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. AISTATS 2018: 425-434 - [c30]Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson:
Product Kernel Interpolation for Scalable Gaussian Processes. AISTATS 2018: 1407-1416 - [c29]Ben Athiwaratkun, Andrew Gordon Wilson:
Hierarchical Density Order Embeddings. ICLR (Poster) 2018 - [c28]Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson:
Constant-Time Predictive Distributions for Gaussian Processes. ICML 2018: 4111-4120 - [c27]William Herlands, Edward McFowland III, Andrew Gordon Wilson, Daniel B. Neill:
Automated Local Regression Discontinuity Design Discovery. KDD 2018: 1512-1520 - [c26]David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson:
Scaling Gaussian Process Regression with Derivatives. NeurIPS 2018: 6868-6878 - [c25]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 - [c24]Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. NeurIPS 2018: 8803-8812 - [c23]Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Averaging Weights Leads to Wider Optima and Better Generalization. UAI 2018: 876-885 - [i26]Phillip A. Jang, Andrew E. Loeb, Matthew B. Davidow, Andrew Gordon Wilson:
Scalable Lévy Process Priors for Spectral Kernel Learning. CoRR abs/1802.00530 (2018) - [i25]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) - [i24]Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry P. Vetrov, Andrew Gordon Wilson:
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. CoRR abs/1802.10026 (2018) - [i23]Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Averaging Weights Leads to Wider Optima and Better Generalization. CoRR abs/1803.05407 (2018) - [i22]Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson:
Constant-Time Predictive Distributions for Gaussian Processes. CoRR abs/1803.06058 (2018) - [i21]William Herlands, Edward McFowland III, Andrew Gordon Wilson, Daniel B. Neill:
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data. CoRR abs/1804.01466 (2018) - [i20]Ben Athiwaratkun, Andrew Gordon Wilson:
Hierarchical Density Order Embeddings. CoRR abs/1804.09843 (2018) - [i19]Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar:
Probabilistic FastText for Multi-Sense Word Embeddings. CoRR abs/1806.02901 (2018) - [i18]Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson:
Improving Consistency-Based Semi-Supervised Learning with Weight Averaging. CoRR abs/1806.05594 (2018) - [i17]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) - [i16]William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson:
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction. CoRR abs/1810.11861 (2018) - [i15]David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson:
Scaling Gaussian Process Regression with Derivatives. CoRR abs/1810.12283 (2018) - 2017
- [j1]Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing:
Learning Scalable Deep Kernels with Recurrent Structure. J. Mach. Learn. Res. 18: 82:1-82:37 (2017) - [c22]Ben Athiwaratkun, Andrew Gordon Wilson:
Multimodal Word Distributions. ACL (1) 2017: 1645-1656 - [c21]Julio Albinati, Wagner Meira Jr., Gisele L. Pappa, Andrew Gordon Wilson:
Efficient Gaussian Process-Based Inference for Modelling Spatio-Temporal Dengue Fever. BRACIS 2017: 61-66 - [c20]Yunus Saatci, Andrew Gordon Wilson:
Bayesian GAN. NIPS 2017: 3622-3631 - [c19]Phillip A. Jang, Andrew E. Loeb, Matthew B. Davidow, Andrew Gordon Wilson:
Scalable Levy Process Priors for Spectral Kernel Learning. NIPS 2017: 3940-3949 - [c18]Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier:
Bayesian Optimization with Gradients. NIPS 2017: 5267-5278 - [c17]Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson:
Scalable Log Determinants for Gaussian Process Kernel Learning. NIPS 2017: 6327-6337 - [i14]Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier:
Bayesian Optimization with Gradients. CoRR abs/1703.04389 (2017) - [i13]Ben Athiwaratkun, Andrew Gordon Wilson:
Multimodal Word Distributions. CoRR abs/1704.08424 (2017) - [i12]Yunus Saatchi, Andrew Gordon Wilson:
Bayesian GAN. CoRR abs/1705.09558 (2017) - [i11]Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson:
Scalable Log Determinants for Gaussian Process Kernel Learning. CoRR abs/1711.03481 (2017) - 2016
- [c16]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Deep Kernel Learning. AISTATS 2016: 370-378 - [c15]William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing:
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. AISTATS 2016: 1013-1021 - [c14]Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Bayesian Nonparametric Kernel-Learning. AISTATS 2016: 1078-1086 - [c13]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Stochastic Variational Deep Kernel Learning. NIPS 2016: 2586-2594 - [i10]Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing:
Learning Scalable Deep Kernels with Recurrent Structure. CoRR abs/1610.08936 (2016) - [i9]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Stochastic Variational Deep Kernel Learning. CoRR abs/1611.00336 (2016) - 2015
- [c12]Zichao Yang, Andrew Gordon Wilson, Alexander J. Smola, Le Song:
A la Carte - Learning Fast Kernels. AISTATS 2015 - [c11]Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola:
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. ICML 2015: 607-616 - [c10]Andrew Gordon Wilson, Hannes Nickisch:
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). ICML 2015: 1775-1784 - [c9]Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing:
The Human Kernel. NIPS 2015: 2854-2862 - [i8]Andrew Gordon Wilson, Hannes Nickisch:
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP). CoRR abs/1503.01057 (2015) - [i7]Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing:
The Human Kernel. CoRR abs/1510.07389 (2015) - [i6]Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch:
Thoughts on Massively Scalable Gaussian Processes. CoRR abs/1511.01870 (2015) - [i5]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Deep Kernel Learning. CoRR abs/1511.02222 (2015) - 2014
- [c8]Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani:
Student-t Processes as Alternatives to Gaussian Processes. AISTATS 2014: 877-885 - [c7]Yuting Wu, Daniel J. Holland, Mick D. Mantle, Andrew Gordon Wilson, Sebastian Nowozin, Andrew Blake, Lynn F. Gladden:
A Bayesian method to quantifying chemical composition using NMR: Application to porous media systems. EUSIPCO 2014: 2515-2519 - [c6]Andrew Gordon Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai:
Fast Kernel Learning for Multidimensional Pattern Extrapolation. NIPS 2014: 3626-3634 - [i4]Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani:
Student-t Processes as Alternatives to Gaussian Processes. CoRR abs/1402.4306 (2014) - [i3]Zichao Yang, Alexander J. Smola, Le Song, Andrew Gordon Wilson:
A la Carte - Learning Fast Kernels. CoRR abs/1412.6493 (2014) - 2013
- [c5]Andrew Gordon Wilson, Ryan Prescott Adams:
Gaussian Process Kernels for Pattern Discovery and Extrapolation. ICML (3) 2013: 1067-1075 - [i2]Andrew Gordon Wilson, Ryan Prescott Adams:
Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation. CoRR abs/1302.4245 (2013) - [i1]Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham:
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes. CoRR abs/1310.5288 (2013) - 2012
- [c4]Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani:
Gaussian Process Regression Networks. ICML 2012 - [c3]Andrew Gordon Wilson, Zoubin Ghahramani:
Modelling Input Varying Correlations between Multiple Responses. ECML/PKDD (2) 2012: 858-861 - 2011
- [c2]Andrew Gordon Wilson, Zoubin Ghahramani:
Generalised Wishart Processes. UAI 2011: 736-744 - 2010
- [c1]Andrew Gordon Wilson, Zoubin Ghahramani:
Copula Processes. NIPS 2010: 2460-2468
Coauthor Index
Greg Benton
aka: Gregory W. Benton
aka: Gregory W. Benton
Marc Finzi
aka: Marc Anton Finzi
aka: Marc Anton Finzi
Samuel Stanton
aka: Samuel Don Stanton
aka: Samuel Don Stanton
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