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The ICML Workshop on Retrospectives in Machine Learning will build upon the success of the 2019 NeurIPS Retrospectives workshop to further encourage the publication of retrospectives. A retrospective of a paper or a set of papers, by its author, takes the form of an informal paper. It provides a venue for authors to reflect on their previous publications, to talk about how their thoughts have changed following publication, to identify shortcomings in their analysis or results, and to discuss resulting extensions. The overarching goal of MLRetrospectives is to improve the science, openness, and accessibility of the machine learning field, by widening what is publishable and helping to identify opportunities for improvement. Retrospectives also give researchers and practitioners unable to attend conferences access to the author’s updated understanding of their work, which would otherwise only be accessible to their immediate circle. The machine learning community would benefit from retrospectives on much of the research which shapes our field, and this workshop will present an opportunity for a few retrospectives to be presented.
Fri 5:20 a.m. - 5:30 a.m.
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Welcome
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Talk
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Ryan Lowe · Jessica Forde 🔗 |
Fri 5:30 a.m. - 5:50 a.m.
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Invited Talk: Bernhard Scholkopf
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Talk
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Bernhard Schölkopf 🔗 |
Fri 5:50 a.m. - 6:00 a.m.
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Q&A: Bernhard Scholkopf
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Q&A
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Bernhard Schölkopf · Mayoore Jaiswal 🔗 |
Fri 6:00 a.m. - 6:20 a.m.
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Invited Talk: Pascale Fung
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Talk
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Pascale FUNG 🔗 |
Fri 6:20 a.m. - 6:30 a.m.
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Q&A: Pascale Fung
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Q&A
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Pascale FUNG · Rosanne Liu 🔗 |
Fri 6:30 a.m. - 6:50 a.m.
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Invited Talk: Deborah Raji
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Talk
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Inioluwa Raji 🔗 |
Fri 6:50 a.m. - 7:00 a.m.
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Q&A: Deborah Raji
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Q&A
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Inioluwa Raji · Jessica Forde 🔗 |
Fri 7:00 a.m. - 7:10 a.m.
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Coffee Break
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🔗 |
Fri 7:10 a.m. - 7:20 a.m.
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Contributed Talk: Jonathan Frankle
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Talk
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SlidesLive Video » |
Jonathan Frankle 🔗 |
Fri 7:20 a.m. - 7:30 a.m.
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Q&A: Jonathan Frankle
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Q&A
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Jonathan Frankle · Mayoore Jaiswal 🔗 |
Fri 7:30 a.m. - 7:50 a.m.
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Invited Talk: Dani Yogatama
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Talk
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SlidesLive Video » |
Dani Yogatama 🔗 |
Fri 7:50 a.m. - 8:00 a.m.
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Q&A: Dani Yogatama
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Q&A
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Dani Yogatama · Jesse Dodge · Jessica Forde 🔗 |
Fri 8:00 a.m. - 9:00 a.m.
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Panel Session
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Panel
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Jessica Forde 🔗 |
Fri 9:00 a.m. - 9:20 a.m.
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Invited Talks: Anima Anandakumar
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Talk
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Animashree Anandkumar 🔗 |
Fri 9:20 a.m. - 9:30 a.m.
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Q&A: Anima Anandakumar
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Q&A
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Animashree Anandkumar · Jessica Forde 🔗 |
Fri 9:30 a.m. - 11:00 a.m.
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Lunch Break
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🔗 |
Fri 11:00 a.m. - 11:20 a.m.
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Invited Talk: Margaret Mitchell
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Talk
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Jesse Dodge 🔗 |
Fri 11:20 a.m. - 11:30 a.m.
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Q&A: Margaret Mitchell
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Q&A
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Jesse Dodge 🔗 |
Fri 11:30 a.m. - 11:50 a.m.
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Invited Talk: Chris Maddison
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Talk
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Chris Maddison 🔗 |
Fri 11:50 a.m. - 12:00 p.m.
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Q&A: Chris Maddison
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Q&A
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Chris Maddison · Jessica Forde · Jesse Dodge 🔗 |
Fri 12:00 p.m. - 12:10 p.m.
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Contributed Talk: Jason Hartford
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Talk
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Jason Hartford 🔗 |
Fri 12:10 p.m. - 12:20 p.m.
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Q&A: Jason Hartford
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Q&A
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Jason Hartford · Jesse Dodge 🔗 |
Fri 12:20 p.m. - 1:00 p.m.
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Coffee Break
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🔗 |
Fri 1:00 p.m. - 1:20 p.m.
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Invited Talk: Peter Henderson
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Talk
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SlidesLive Video » |
Peter Henderson 🔗 |
Fri 1:20 p.m. - 1:30 p.m.
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Q&A: Peter Henderson
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Q&A
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Peter Henderson · Mayoore Jaiswal · Ryan Lowe 🔗 |
Fri 1:30 p.m. - 2:30 p.m.
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Brainstorming & Closing
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Breakout session
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Mayoore Jaiswal · Ryan Lowe · Jesse Dodge · Jessica Forde · Rosanne Liu 🔗 |
Author Information
Jessica Forde (Brown University)
Jesse Dodge (University of Washington)
Mayoore Jaiswal (IBM)
Rosanne Liu (Uber AI Labs)
Ryan Lowe (OpenAI)
Rosanne Liu (ML Collective)
Joelle Pineau (McGill University / Facebook)
Yoshua Bengio (Montreal Institute for Learning Algorithms)
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2019 : AI Commons »
Yoshua Bengio -
2019 : Opening remarks »
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2019 : Panel Discussion »
Yoshua Bengio · Andrew Ng · Raia Hadsell · John Platt · Claire Monteleoni · Jennifer Chayes -
2019 : Poster discussion »
Roman Novak · Maxime Gabella · Frederic Dreyer · Siavash Golkar · Anh Tong · Irina Higgins · Mirco Milletari · Joe Antognini · Sebastian Goldt · Adín Ramírez Rivera · Roberto Bondesan · Ryo Karakida · Remi Tachet des Combes · Michael Mahoney · Nicholas Walker · Stanislav Fort · Samuel Smith · Rohan Ghosh · Aristide Baratin · Diego Granziol · Stephen Roberts · Dmitry Vetrov · Andrew Wilson · César Laurent · Valentin Thomas · Simon Lacoste-Julien · Dar Gilboa · Daniel Soudry · Anupam Gupta · Anirudh Goyal · Yoshua Bengio · Erich Elsen · Soham De · Stanislaw Jastrzebski · Charles H Martin · Samira Shabanian · Aaron Courville · Shorato Akaho · Lenka Zdeborova · Ethan Dyer · Maurice Weiler · Pim de Haan · Taco Cohen · Max Welling · Ping Luo · zhanglin peng · Nasim Rahaman · Loic Matthey · Danilo J. Rezende · Jaesik Choi · Kyle Cranmer · Lechao Xiao · Jaehoon Lee · Yasaman Bahri · Jeffrey Pennington · Greg Yang · Jiri Hron · Jascha Sohl-Dickstein · Guy Gur-Ari -
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Yoshua Bengio -
2019 : Networking Lunch (provided) + Poster Session »
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David Rolnick · Alexandre Lacoste · Tegan Maharaj · Jennifer Chayes · Yoshua Bengio -
2019 Workshop: Generative Modeling and Model-Based Reasoning for Robotics and AI »
Aravind Rajeswaran · Emanuel Todorov · Igor Mordatch · William Agnew · Amy Zhang · Joelle Pineau · Michael Chang · Dumitru Erhan · Sergey Levine · Kimberly Stachenfeld · Marvin Zhang -
2019 Poster: State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations »
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer -
2019 Poster: On the Spectral Bias of Neural Networks »
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville -
2019 Oral: On the Spectral Bias of Neural Networks »
Nasim Rahaman · Aristide Baratin · Devansh Arpit · Felix Draxler · Min Lin · Fred Hamprecht · Yoshua Bengio · Aaron Courville -
2019 Oral: State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations »
Alex Lamb · Jonathan Binas · Anirudh Goyal · Sandeep Subramanian · Ioannis Mitliagkas · Yoshua Bengio · Michael Mozer -
2019 Poster: Manifold Mixup: Better Representations by Interpolating Hidden States »
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio -
2019 Poster: GMNN: Graph Markov Neural Networks »
Meng Qu · Yoshua Bengio · Jian Tang -
2019 Poster: Separable value functions across time-scales »
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill -
2019 Oral: Separable value functions across time-scales »
Joshua Romoff · Peter Henderson · Ahmed Touati · Yann Ollivier · Joelle Pineau · Emma Brunskill -
2019 Oral: GMNN: Graph Markov Neural Networks »
Meng Qu · Yoshua Bengio · Jian Tang -
2019 Oral: Manifold Mixup: Better Representations by Interpolating Hidden States »
Vikas Verma · Alex Lamb · Christopher Beckham · Amir Najafi · Ioannis Mitliagkas · David Lopez-Paz · Yoshua Bengio -
2018 Poster: Mutual Information Neural Estimation »
Mohamed Belghazi · Aristide Baratin · Sai Rajeswar · Sherjil Ozair · Yoshua Bengio · R Devon Hjelm · Aaron Courville -
2018 Oral: Mutual Information Neural Estimation »
Mohamed Belghazi · Aristide Baratin · Sai Rajeswar · Sherjil Ozair · Yoshua Bengio · R Devon Hjelm · Aaron Courville -
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Rosemary Nan Ke · Konrad Zolna · Alessandro Sordoni · Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal -
2018 Oral: Focused Hierarchical RNNs for Conditional Sequence Processing »
Rosemary Nan Ke · Konrad Zolna · Alessandro Sordoni · Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal -
2018 Poster: An Inference-Based Policy Gradient Method for Learning Options »
Matthew Smith · Herke van Hoof · Joelle Pineau -
2018 Oral: An Inference-Based Policy Gradient Method for Learning Options »
Matthew Smith · Herke van Hoof · Joelle Pineau -
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Rosemary Nan Ke · Anirudh Goyal · Alex Lamb · Joelle Pineau · Samy Bengio · Yoshua Bengio -
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Sergey Levine · Joelle Pineau · Balaraman Ravindran · Andrei A Rusu -
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Joelle Pineau -
2017 Poster: Sharp Minima Can Generalize For Deep Nets »
Laurent Dinh · Razvan Pascanu · Samy Bengio · Yoshua Bengio -
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David Krueger · Yoshua Bengio · Stanislaw Jastrzebski · Maxinder S. Kanwal · Nicolas Ballas · Asja Fischer · Emmanuel Bengio · Devansh Arpit · Tegan Maharaj · Aaron Courville · Simon Lacoste-Julien -
2017 Talk: A Closer Look at Memorization in Deep Networks »
David Krueger · Yoshua Bengio · Stanislaw Jastrzebski · Maxinder S. Kanwal · Nicolas Ballas · Asja Fischer · Emmanuel Bengio · Devansh Arpit · Tegan Maharaj · Aaron Courville · Simon Lacoste-Julien -
2017 Talk: Sharp Minima Can Generalize For Deep Nets »
Laurent Dinh · Razvan Pascanu · Samy Bengio · Yoshua Bengio