Timezone: »
Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task. But an initialization contains relatively little information about the source task, and does not reflect the belief that our knowledge of the source task should affect the locations and shape of optima on the downstream task.Instead, we show that we can learn highly informative posteriors from the source task, through supervised or self-supervised approaches, which then serve as the basis for priors that modify the whole loss surface on the downstream task. This simple modular approach enables significant performance gains and more data-efficient learning on a variety of downstream classification and segmentation tasks, serving as a drop-in replacement for standard pre-training strategies. These highly informative priors also can be saved for future use, similar to pre-trained weights, and stand in contrast to the zero-mean isotropic uninformative priors that are typically used in Bayesian deep learning.
Author Information
Ravid Shwartz-Ziv (Hebrew University of Jerusalem)
Micah Goldblum (University of Maryland)
Hossein Souri (Johns Hopkins University)
Sanyam Kapoor (New York University)
Chen Zhu (Google Brain)
Yann LeCun (Facebook)
Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University. He received the Electrical Engineer Diploma from ESIEE, Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits for computer perception.
Andrew Wilson (New York University)

I am a professor of machine learning at New York University.
More from the Same Authors
-
2020 : An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process »
David Tran · Alex Valtchanov · Keshav R Ganapathy · Raymond Feng · Eric Slud · Micah Goldblum · Tom Goldstein -
2021 : Robust Reinforcement Learning for Shifting Dynamics During Deployment »
Samuel Stanton · Rasool Fakoor · Jonas Mueller · Andrew Gordon Wilson · Alexander Smola -
2021 : A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs »
Mucong Ding · Kezhi Kong · Jiuhai Chen · John Kirchenbauer · Micah Goldblum · David P Wipf · Furong Huang · Tom Goldstein -
2021 : Diurnal or Nocturnal? Federated Learning from Periodically Shifting Distributions »
Chen Zhu · Zheng Xu · Mingqing Chen · Jakub Konečný · Andrew S Hard · Tom Goldstein -
2022 : Investigating Reproducibility from the Decision Boundary Perspective. »
Gowthami Somepalli · Arpit Bansal · Liam Fowl · Ping-yeh Chiang · Yehuda Dar · Richard Baraniuk · Micah Goldblum · Tom Goldstein -
2022 : A Deep Dive into Dataset Imbalance and Bias in Face Identification »
Valeriia Cherepanova · Steven Reich · Samuel Dooley · Hossein Souri · John Dickerson · Micah Goldblum · Tom Goldstein -
2022 : SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training »
Gowthami Somepalli · Avi Schwarzschild · Micah Goldblum · C. Bayan Bruss · Tom Goldstein -
2022 : Transfer Learning with Deep Tabular Models »
Roman Levin · Valeriia Cherepanova · Avi Schwarzschild · Arpit Bansal · C. Bayan Bruss · Tom Goldstein · Andrew Wilson · Micah Goldblum -
2022 : A Deep Dive into Dataset Imbalance and Bias in Face Identification »
Valeriia Cherepanova · Steven Reich · Samuel Dooley · Hossein Souri · John Dickerson · Micah Goldblum · Tom Goldstein -
2022 : On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition »
Samuel Dooley · Rhea Sukthanker · John Dickerson · Colin White · Frank Hutter · Micah Goldblum -
2022 : On the Importance of Architectures and Hyperparameters for Fairness in Face Recognition »
Samuel Dooley · Rhea Sukthanker · John Dickerson · Colin White · Frank Hutter · Micah Goldblum -
2022 : A Deep Dive into Dataset Imbalance and Bias in Face Identification »
Valeriia Cherepanova · Steven Reich · Samuel Dooley · Hossein Souri · John Dickerson · Micah Goldblum · Tom Goldstein -
2022 : Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries »
Yuxin Wen · Arpit Bansal · Hamid Kazemi · Eitan Borgnia · Micah Goldblum · Jonas Geiping · Tom Goldstein -
2022 : On Representation Learning Under Class Imbalance »
Ravid Shwartz-Ziv · Micah Goldblum · Yucen Li · C. Bayan Bruss · Andrew Gordon Wilson -
2023 Poster: Understanding the detrimental class-level effects of data augmentation »
Polina Kirichenko · Mark Ibrahim · Randall Balestriero · Diane Bouchacourt · Shanmukha Ramakrishna Vedantam · Hamed Firooz · Andrew Wilson -
2023 Poster: What Can We Learn from Unlearnable Datasets? »
Pedro Sandoval-Segura · Vasu Singla · Jonas Geiping · Micah Goldblum · Tom Goldstein -
2023 Poster: Large Language Models Are Zero Shot Time Series Forecasters »
Marc Finzi · Nate Gruver · Shikai Qiu · Andrew Wilson -
2023 Poster: Simplifying Neural Network Training Under Class Imbalance »
Ravid Shwartz-Ziv · Micah Goldblum · Yucen Li · C. Bayan Bruss · Andrew Wilson -
2023 Poster: Self-Supervised Learning with Lie Symmetries for Partial Differential Equations »
Grégoire Mialon · Quentin Garrido · Hannah Lawrence · Danyal Rehman · Bobak Kiani · Yann LeCun -
2023 Poster: Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise »
Arpit Bansal · Eitan Borgnia · Hong-Min Chu · Jie Li · Hamid Kazemi · Furong Huang · Micah Goldblum · Jonas Geiping · Tom Goldstein -
2023 Poster: Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery »
Yuxin Wen · Neel Jain · John Kirchenbauer · Micah Goldblum · Jonas Geiping · Tom Goldstein -
2023 Poster: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra »
Andres Potapczynski · Marc Finzi · Geoff Pleiss · Andrew Wilson -
2023 Poster: Reverse Engineering Self-Supervised Learning »
Ido Ben-Shaul · Ravid Shwartz-Ziv · Tomer Galanti · Shai Dekel · Yann LeCun -
2023 Poster: Protein Design with Guided Discrete Diffusion »
Nate Gruver · Samuel Stanton · Nathan Frey · Tim G. J. Rudner · Isidro Hotzel · Julien Lafrance-Vanasse · Arvind Rajpal · Kyunghyun Cho · Andrew Wilson -
2023 Poster: An Information Theory Perspective on Variance-Invariance-Covariance Regularization »
Ravid Shwartz-Ziv · Randall Balestriero · Kenji Kawaguchi · Tim G. J. Rudner · Yann LeCun -
2023 Poster: Why Diffusion Models Memorize and How to Mitigate Copying »
Gowthami Somepalli · Vasu Singla · Micah Goldblum · Jonas Geiping · Tom Goldstein -
2023 Poster: Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution »
Tim G. J. Rudner · Ying Wang · Andrew Wilson -
2023 Poster: Should We Learn Most Likely Functions or Parameters? »
Tim G. J. Rudner · Sanyam Kapoor · Shikai Qiu · Andrew Wilson -
2023 Poster: Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition »
Samuel Dooley · Rhea Sukthanker · John Dickerson · Colin White · Frank Hutter · Micah Goldblum -
2023 Workshop: Backdoors in Deep Learning: The Good, the Bad, and the Ugly »
Khoa D Doan · Aniruddha Saha · Anh Tran · Yingjie Lao · Kok-Seng Wong · Ang Li · HARIPRIYA HARIKUMAR · Eugene Bagdasaryan · Micah Goldblum · Tom Goldstein -
2022 : Andrew Gordon Wilson: When Bayesian Orthodoxy Can Go Wrong: Model Selection and Out-of-Distribution Generalization »
Andrew Gordon Wilson -
2022 : Andrew Gordon Wilson: When Bayesian Orthodoxy Can Go Wrong: Model Selection and Out-of-Distribution Generalization »
Andrew Gordon Wilson -
2022 : Transfer Learning with Deep Tabular Models »
Roman Levin · Valeriia Cherepanova · Avi Schwarzschild · Arpit Bansal · C. Bayan Bruss · Tom Goldstein · Andrew Wilson · Micah Goldblum -
2022 Poster: The Effects of Regularization and Data Augmentation are Class Dependent »
Randall Balestriero · Leon Bottou · Yann LeCun -
2022 Poster: Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability »
Roman Levin · Manli Shu · Eitan Borgnia · Furong Huang · Micah Goldblum · Tom Goldstein -
2022 Poster: VICRegL: Self-Supervised Learning of Local Visual Features »
Adrien Bardes · Jean Ponce · Yann LeCun -
2022 Poster: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone »
Zi-Yi Dou · Aishwarya Kamath · Zhe Gan · Pengchuan Zhang · Jianfeng Wang · Linjie Li · Zicheng Liu · Ce Liu · Yann LeCun · Nanyun Peng · Jianfeng Gao · Lijuan Wang -
2022 Poster: Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers »
Wanqian Yang · Polina Kirichenko · Micah Goldblum · Andrew Wilson -
2022 Poster: On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification »
Sanyam Kapoor · Wesley Maddox · Pavel Izmailov · Andrew Wilson -
2022 Poster: Autoregressive Perturbations for Data Poisoning »
Pedro Sandoval-Segura · Vasu Singla · Jonas Geiping · Micah Goldblum · Tom Goldstein · David Jacobs -
2022 Poster: A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training »
Randall Balestriero · Ishan Misra · Yann LeCun -
2022 Poster: projUNN: efficient method for training deep networks with unitary matrices »
Bobak Kiani · Randall Balestriero · Yann LeCun · Seth Lloyd -
2022 Poster: Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch »
Hossein Souri · Liam Fowl · Rama Chellappa · Micah Goldblum · Tom Goldstein -
2022 Poster: On Feature Learning in the Presence of Spurious Correlations »
Pavel Izmailov · Polina Kirichenko · Nate Gruver · Andrew Wilson -
2022 Poster: Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods »
Randall Balestriero · Yann LeCun -
2022 Poster: PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization »
Sanae Lotfi · Marc Finzi · Sanyam Kapoor · Andres Potapczynski · Micah Goldblum · Andrew Wilson -
2022 Poster: End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking »
Arpit Bansal · Avi Schwarzschild · Eitan Borgnia · Zeyad Emam · Furong Huang · Micah Goldblum · Tom Goldstein -
2021 Workshop: Bayesian Deep Learning »
Yarin Gal · Yingzhen Li · Sebastian Farquhar · Christos Louizos · Eric Nalisnick · Andrew Gordon Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2021 : A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs »
Mucong Ding · Kezhi Kong · Jiuhai Chen · John Kirchenbauer · Micah Goldblum · David P Wipf · Furong Huang · Tom Goldstein -
2021 Poster: Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks »
Avi Schwarzschild · Eitan Borgnia · Arjun Gupta · Furong Huang · Uzi Vishkin · Micah Goldblum · Tom Goldstein -
2021 Poster: VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization »
Mucong Ding · Kezhi Kong · Jingling Li · Chen Zhu · John Dickerson · Furong Huang · Tom Goldstein -
2021 : Evaluating Approximate Inference in Bayesian Deep Learning + Q&A »
Andrew Gordon Wilson · Pavel Izmailov · Matthew Hoffman · Yarin Gal · Yingzhen Li · Melanie F. Pradier · Sharad Vikram · Andrew Foong · Sanae Lotfi · Sebastian Farquhar -
2021 Poster: GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training »
Chen Zhu · Renkun Ni · Zheng Xu · Kezhi Kong · W. Ronny Huang · Tom Goldstein -
2021 Poster: Residual Pathway Priors for Soft Equivariance Constraints »
Marc Finzi · Gregory Benton · Andrew Wilson -
2021 Poster: Does Knowledge Distillation Really Work? »
Samuel Stanton · Pavel Izmailov · Polina Kirichenko · Alexander Alemi · Andrew Wilson -
2021 Poster: Dangers of Bayesian Model Averaging under Covariate Shift »
Pavel Izmailov · Patrick Nicholson · Sanae Lotfi · Andrew Wilson -
2021 Poster: Adversarial Examples Make Strong Poisons »
Liam Fowl · Micah Goldblum · Ping-yeh Chiang · Jonas Geiping · Wojciech Czaja · Tom Goldstein -
2021 Poster: Encoding Robustness to Image Style via Adversarial Feature Perturbations »
Manli Shu · Zuxuan Wu · Micah Goldblum · Tom Goldstein -
2021 Poster: Long-Short Transformer: Efficient Transformers for Language and Vision »
Chen Zhu · Wei Ping · Chaowei Xiao · Mohammad Shoeybi · Tom Goldstein · Anima Anandkumar · Bryan Catanzaro -
2021 Poster: Conditioning Sparse Variational Gaussian Processes for Online Decision-making »
Wesley Maddox · Samuel Stanton · Andrew Wilson -
2021 Poster: Bayesian Optimization with High-Dimensional Outputs »
Wesley Maddox · Maximilian Balandat · Andrew Wilson · Eytan Bakshy -
2020 : Panel Discussion & Closing »
Yejin Choi · Alexei Efros · Chelsea Finn · Kristen Grauman · Quoc V Le · Yann LeCun · Ruslan Salakhutdinov · Eric Xing -
2020 : QA: Yann LeCun »
Yann LeCun -
2020 : Invited Talk: Yann LeCun »
Yann LeCun -
2020 : The Intrinsic Dimension of Images and Its Impact on Learning »
Chen Zhu · Micah Goldblum · Ahmed Abdelkader · Tom Goldstein · Phillip Pope -
2020 Poster: Bayesian Deep Learning and a Probabilistic Perspective of Generalization »
Andrew Wilson · Pavel Izmailov -
2020 Poster: Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints »
Marc Finzi · Ke Alexander Wang · Andrew Wilson -
2020 Spotlight: Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints »
Marc Finzi · Ke Alexander Wang · Andrew Wilson -
2020 Poster: Adversarially Robust Few-Shot Learning: A Meta-Learning Approach »
Micah Goldblum · Liam Fowl · Tom Goldstein -
2020 Poster: BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization »
Maximilian Balandat · Brian Karrer · Daniel Jiang · Samuel Daulton · Ben Letham · Andrew Wilson · Eytan Bakshy -
2020 Poster: Learning Invariances in Neural Networks from Training Data »
Gregory Benton · Marc Finzi · Pavel Izmailov · Andrew Wilson -
2020 Poster: Improving GAN Training with Probability Ratio Clipping and Sample Reweighting »
Yue Wu · Pan Zhou · Andrew Wilson · Eric Xing · Zhiting Hu -
2020 Poster: Large-Scale Adversarial Training for Vision-and-Language Representation Learning »
Zhe Gan · Yen-Chun Chen · Linjie Li · Chen Zhu · Yu Cheng · Jingjing Liu -
2020 Spotlight: Large-Scale Adversarial Training for Vision-and-Language Representation Learning »
Zhe Gan · Yen-Chun Chen · Linjie Li · Chen Zhu · Yu Cheng · Jingjing Liu -
2020 Poster: Why Normalizing Flows Fail to Detect Out-of-Distribution Data »
Polina Kirichenko · Pavel Izmailov · Andrew Wilson -
2019 : Break / Poster Session 1 »
Antonia Marcu · Yao-Yuan Yang · Pascale Gourdeau · Chen Zhu · Thodoris Lykouris · Jianfeng Chi · Mark Kozdoba · Arjun Nitin Bhagoji · Xiaoxia Wu · Jay Nandy · Michael T Smith · Bingyang Wen · Yuege Xie · Konstantinos Pitas · Suprosanna Shit · Maksym Andriushchenko · Dingli Yu · Gaël Letarte · Misha Khodak · Hussein Mozannar · Chara Podimata · James Foulds · Yizhen Wang · Huishuai Zhang · Ondrej Kuzelka · Alexander Levine · Nan Lu · Zakaria Mhammedi · Paul Viallard · Diana Cai · Lovedeep Gondara · James Lucas · Yasaman Mahdaviyeh · Aristide Baratin · Rishi Bommasani · Alessandro Barp · Andrew Ilyas · Kaiwen Wu · Jens Behrmann · Omar Rivasplata · Amir Nazemi · Aditi Raghunathan · Will Stephenson · Sahil Singla · Akhil Gupta · YooJung Choi · Yannic Kilcher · Clare Lyle · Edoardo Manino · Andrew Bennett · Zhi Xu · Niladri Chatterji · Emre Barut · Flavien Prost · Rodrigo Toro Icarte · Arno Blaas · Chulhee Yun · Sahin Lale · YiDing Jiang · Tharun Kumar Reddy Medini · Ashkan Rezaei · Alexander Meinke · Stephen Mell · Gary Kazantsev · Shivam Garg · Aradhana Sinha · Vishnu Lokhande · Geovani Rizk · Han Zhao · Aditya Kumar Akash · Jikai Hou · Ali Ghodsi · Matthias Hein · Tyler Sypherd · Yichen Yang · Anastasia Pentina · Pierre Gillot · Antoine Ledent · Guy Gur-Ari · Noah MacAulay · Tianzong Zhang -
2019 Workshop: Learning with Rich Experience: Integration of Learning Paradigms »
Zhiting Hu · Andrew Wilson · Chelsea Finn · Lisa Lee · Taylor Berg-Kirkpatrick · Ruslan Salakhutdinov · Eric Xing -
2019 : TBD »
Yann LeCun -
2019 Poster: Exact Gaussian Processes on a Million Data Points »
Ke Alexander Wang · Geoff Pleiss · Jacob Gardner · Stephen Tyree · Kilian Weinberger · Andrew Gordon Wilson -
2019 Poster: Function-Space Distributions over Kernels »
Gregory Benton · Wesley Maddox · Jayson Salkey · Julio Albinati · Andrew Gordon Wilson -
2019 Poster: A Simple Baseline for Bayesian Uncertainty in Deep Learning »
Wesley Maddox · Pavel Izmailov · Timur Garipov · Dmitry Vetrov · Andrew Gordon Wilson -
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2018 Poster: Scaling Gaussian Process Regression with Derivatives »
David Eriksson · Kun Dong · Eric Lee · David Bindel · Andrew Wilson -
2018 Poster: GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration »
Jacob Gardner · Geoff Pleiss · Kilian Weinberger · David Bindel · Andrew Wilson -
2018 Spotlight: GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration »
Jacob Gardner · Geoff Pleiss · Kilian Weinberger · David Bindel · Andrew Wilson -
2018 Poster: Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs »
Timur Garipov · Pavel Izmailov · Dmitrii Podoprikhin · Dmitry Vetrov · Andrew Wilson -
2018 Spotlight: Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs »
Timur Garipov · Pavel Izmailov · Dmitrii Podoprikhin · Dmitry Vetrov · Andrew Wilson -
2017 : Panel Session »
Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther -
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Symposium: Interpretable Machine Learning »
Andrew Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands -
2017 Poster: Bayesian GAN »
Yunus Saatci · Andrew Wilson -
2017 Spotlight: Bayesian GANs »
Yunus Saatci · Andrew Wilson -
2017 Poster: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier -
2017 Poster: Scalable Log Determinants for Gaussian Process Kernel Learning »
Kun Dong · David Eriksson · Hannes Nickisch · David Bindel · Andrew Wilson -
2017 Oral: Bayesian Optimization with Gradients »
Jian Wu · Matthias Poloczek · Andrew Wilson · Peter Frazier -
2017 Poster: Scalable Levy Process Priors for Spectral Kernel Learning »
Phillip Jang · Andrew Loeb · Matthew Davidow · Andrew Wilson -
2017 Tutorial: Geometric Deep Learning on Graphs and Manifolds »
Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun -
2016 : Discussion panel »
Ian Goodfellow · Soumith Chintala · Arthur Gretton · Sebastian Nowozin · Aaron Courville · Yann LeCun · Emily Denton -
2016 : Energy-Based Adversarial Training and Video Prediction »
Yann LeCun -
2016 Workshop: Interpretable Machine Learning for Complex Systems »
Andrew Wilson · Been Kim · William Herlands -
2016 Symposium: Deep Learning Symposium »
Yoshua Bengio · Yann LeCun · Navdeep Jaitly · Roger Grosse -
2016 Poster: Stochastic Variational Deep Kernel Learning »
Andrew Wilson · Zhiting Hu · Russ Salakhutdinov · Eric Xing -
2015 Workshop: Nonparametric Methods for Large Scale Representation Learning »
Andrew G Wilson · Alexander Smola · Eric Xing -
2015 Poster: Learning to Linearize Under Uncertainty »
Ross Goroshin · Michael Mathieu · Yann LeCun -
2015 Poster: Character-level Convolutional Networks for Text Classification »
Xiang Zhang · Junbo (Jake) Zhao · Yann LeCun -
2015 Poster: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Poster: Deep learning with Elastic Averaging SGD »
Sixin Zhang · Anna Choromanska · Yann LeCun -
2015 Spotlight: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Spotlight: Deep learning with Elastic Averaging SGD »
Sixin Zhang · Anna Choromanska · Yann LeCun -
2015 Tutorial: Deep Learning »
Geoffrey E Hinton · Yoshua Bengio · Yann LeCun -
2014 Workshop: Modern Nonparametrics 3: Automating the Learning Pipeline »
Eric Xing · Mladen Kolar · Arthur Gretton · Samory Kpotufe · Han Liu · Zoltán Szabó · Alan Yuille · Andrew G Wilson · Ryan Tibshirani · Sasha Rakhlin · Damian Kozbur · Bharath Sriperumbudur · David Lopez-Paz · Kirthevasan Kandasamy · Francesco Orabona · Andreas Damianou · Wacha Bounliphone · Yanshuai Cao · Arijit Das · Yingzhen Yang · Giulia DeSalvo · Dmitry Storcheus · Roberto Valerio -
2014 Poster: Fast Kernel Learning for Multidimensional Pattern Extrapolation »
Andrew Wilson · Elad Gilboa · John P Cunningham · Arye Nehorai -
2010 Spotlight: Copula Processes »
Andrew Wilson · Zoubin Ghahramani -
2010 Poster: Copula Processes »
Andrew Wilson · Zoubin Ghahramani