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Just a quick introduction to the first NIPS workshop on Adversarial Training.
Author Information
David Lopez-Paz (Meta AI)
Alec Radford (OpenAI)
Leon Bottou (Facebook AI Research)
Léon Bottou received a Diplôme from l'Ecole Polytechnique, Paris in 1987, a Magistère en Mathématiques Fondamentales et Appliquées et Informatiques from Ecole Normale Supérieure, Paris in 1988, and a PhD in Computer Science from Université de Paris-Sud in 1991. He joined AT&T Bell Labs from 1991 to 1992 and AT&T Labs from 1995 to 2002. Between 1992 and 1995 he was chairman of Neuristique in Paris, a small company pioneering machine learning for data mining applications. He has been with NEC Labs America in Princeton since 2002. Léon's primary research interest is machine learning. His contributions to this field address theory, algorithms and large scale applications. Léon's secondary research interest is data compression and coding. His best known contribution in this field is the DjVu document compression technology (http://www.djvu.org.) Léon published over 70 papers and is serving on the boards of JMLR and IEEE TPAMI. He also serves on the scientific advisory board of Kxen Inc .
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2021 : On the Relation between Distributionally Robust Optimization and Data Curation »
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2021 : On the Relation between Distributionally Robust Optimization and Data Curation »
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2021 : Poster: Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation »
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2022 : Pre-train, fine-tune, interpolate: a three-stage strategy for domain generalization »
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2022 Workshop: INTERPOLATE — First Workshop on Interpolation Regularizers and Beyond »
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2022 Poster: The Effects of Regularization and Data Augmentation are Class Dependent »
Randall Balestriero · Leon Bottou · Yann LeCun -
2021 : Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation »
Agnieszka Słowik · Leon Bottou -
2021 Poster: An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers »
Ramakrishna Vedantam · David Lopez-Paz · David Schwab -
2020 Poster: Learning to summarize with human feedback »
Nisan Stiennon · Long Ouyang · Jeffrey Wu · Daniel Ziegler · Ryan Lowe · Chelsea Voss · Alec Radford · Dario Amodei · Paul Christiano -
2020 Poster: Language Models are Few-Shot Learners »
Tom B Brown · Benjamin Mann · Nick Ryder · Melanie Subbiah · Jared Kaplan · Prafulla Dhariwal · Arvind Neelakantan · Pranav Shyam · Girish Sastry · Amanda Askell · Sandhini Agarwal · Ariel Herbert-Voss · Gretchen M Krueger · Tom Henighan · Rewon Child · Aditya Ramesh · Daniel Ziegler · Jeffrey Wu · Clemens Winter · Chris Hesse · Mark Chen · Eric Sigler · Mateusz Litwin · Scott Gray · Benjamin Chess · Jack Clark · Christopher Berner · Sam McCandlish · Alec Radford · Ilya Sutskever · Dario Amodei -
2020 Oral: Language Models are Few-Shot Learners »
Tom B Brown · Benjamin Mann · Nick Ryder · Melanie Subbiah · Jared Kaplan · Prafulla Dhariwal · Arvind Neelakantan · Pranav Shyam · Girish Sastry · Amanda Askell · Sandhini Agarwal · Ariel Herbert-Voss · Gretchen M Krueger · Tom Henighan · Rewon Child · Aditya Ramesh · Daniel Ziegler · Jeffrey Wu · Clemens Winter · Chris Hesse · Mark Chen · Eric Sigler · Mateusz Litwin · Scott Gray · Benjamin Chess · Jack Clark · Christopher Berner · Sam McCandlish · Alec Radford · Ilya Sutskever · Dario Amodei -
2019 : Transfer Learning for Text Generation »
Alec Radford -
2019 Poster: Learning about an exponential amount of conditional distributions »
Mohamed Ishmael Belghazi · Maxime Oquab · David Lopez-Paz -
2019 Poster: Cold Case: The Lost MNIST Digits »
Chhavi Yadav · Leon Bottou -
2019 Spotlight: Cold Case: The Lost MNIST Digits »
Chhavi Yadav · Leon Bottou -
2019 Poster: Single-Model Uncertainties for Deep Learning »
Nataša Tagasovska · David Lopez-Paz -
2018 : Opening Remarks »
David Lopez-Paz -
2018 Workshop: Causal Learning »
Martin Arjovsky · Christina Heinze-Deml · Anna Klimovskaia · Maxime Oquab · Leon Bottou · David Lopez-Paz -
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
2018 Poster: SING: Symbol-to-Instrument Neural Generator »
Alexandre Defossez · Neil Zeghidour · Nicolas Usunier · Leon Bottou · Francis Bach -
2017 : Geometrical Insights for Unsupervised Learning »
Leon Bottou -
2017 : Looking for a Missing Signal »
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2017 Poster: Gradient Episodic Memory for Continual Learning »
David Lopez-Paz · Marc'Aurelio Ranzato -
2016 Workshop: Adversarial Training »
David Lopez-Paz · Leon Bottou · Alec Radford -
2016 Poster: Improved Techniques for Training GANs »
Tim Salimans · Ian Goodfellow · Wojciech Zaremba · Vicki Cheung · Alec Radford · Peter Chen · Xi Chen -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
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 Workshop: Learning Semantics »
Cedric Archambeau · Antoine Bordes · Leon Bottou · Chris J Burges · David Grangier -
2014 Workshop: Deep Learning and Representation Learning »
Andrew Y Ng · Yoshua Bengio · Adam Coates · Roland Memisevic · Sharanyan Chetlur · Geoffrey E Hinton · Shamim Nemati · Bryan Catanzaro · Surya Ganguli · Herbert Jaeger · Phil Blunsom · Leon Bottou · Volodymyr Mnih · Chen-Yu Lee · Rich M Schwartz -
2013 Workshop: Randomized Methods for Machine Learning »
David Lopez-Paz · Quoc V Le · Alexander Smola -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2013 Poster: The Randomized Dependence Coefficient »
David Lopez-Paz · Philipp Hennig · Bernhard Schölkopf -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2011 Workshop: Learning Semantics »
Antoine Bordes · Jason E Weston · Ronan Collobert · Leon Bottou -
2007 Tutorial: Learning Using Many Examples »
Leon Bottou · Andrew W Moore -
2007 Poster: The Tradeoffs of Large Scale Learning »
Leon Bottou · Olivier Bousquet