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Self-supervised learning (SSL) is an unsupervised approach for representation learning without relying on human-provided labels. It creates auxiliary tasks on unlabeled input data and learns representations by solving these tasks. SSL has demonstrated great success on images (e.g., MoCo, PIRL, SimCLR) and texts (e.g., BERT) and has shown promising results in other data modalities, including graphs, time-series, audio, etc. On a wide variety of tasks, SSL without using human-provided labels achieves performance that is close to fully supervised approaches.
The existing SSL research mostly focuses on improving the empirical performance without a theoretical foundation. While the proposed SSL approaches are empirically effective, theoretically why they perform well is not clear. For example, why certain auxiliary tasks in SSL perform better than others? How many unlabeled data examples are needed by SSL to learn a good representation? How is the performance of SSL affected by neural architectures?
In this workshop, we aim to bridge this gap between theory and practice. We bring together SSL-interested researchers from various domains to discuss the theoretical foundations of empirically well-performing SSL approaches and how the theoretical insights can further improve SSL’s empirical performance. Different from previous SSL-related workshops which focus on empirical effectiveness of SSL approaches without considering their theoretical foundations, our workshop focuses on establishing the theoretical foundation of SSL and providing theoretical insights for developing new SSL approaches.
We invite submissions of both theoretical works and empirical works, and the intersection of the two. The topics include but are not limited to:
Theoretical foundations of SSL
Sample complexity of SSL methods
Theory-driven design of auxiliary tasks in SSL
Comparative analysis of different auxiliary tasks
Comparative analysis of SSL and supervised approaches
Information theory and SSL
SSL for computer vision, natural language processing, robotics, speech processing, time-series analysis, graph analytics, etc.
SSL for healthcare, social media, neuroscience, biology, social science, etc.
Cognitive foundations of SSL
In addition to invited talks by leading researchers from diverse backgrounds including CV, NLP, robotics, theoretical ML, etc., the workshop will feature poster sessions and panel discussion to share perspectives on establishing foundational understanding of existing SSL approaches and theoretically-principled ways of developing new SSL methods. We accept submissions of short papers (up to 4 pages excluding references in NeurIPS format), which will be peer-reviewed by at least two reviewers. The accepted papers are allowed to be submitted to other conference venues.
Sat 8:50 a.m. - 9:00 a.m.
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Opening remarks
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Sat 9:00 a.m. - 9:23 a.m.
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Invited Talk: Oriol Vinyals
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Invited Talk
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SlidesLive Video » |
Oriol Vinyals 🔗 |
Sat 9:23 a.m. - 9:25 a.m.
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QA: Oriol Vinyals
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QA
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Oriol Vinyals 🔗 |
Sat 9:25 a.m. - 9:48 a.m.
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Invited Talk: Ruslan Salakhutdinov
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Invited Talk
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Ruslan Salakhutdinov 🔗 |
Sat 9:48 a.m. - 9:50 a.m.
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QA: Ruslan Salakhutdinov
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QA
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Ruslan Salakhutdinov 🔗 |
Sat 9:50 a.m. - 10:13 a.m.
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Invited Talk: Yejin Choi
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Invited Talk
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SlidesLive Video » |
Yejin Choi 🔗 |
Sat 10:13 a.m. - 10:15 a.m.
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QA: Yejin Choi
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QA
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Yejin Choi 🔗 |
Sat 10:15 a.m. - 11:15 a.m.
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Poster Session I ( Poster Session ) link » | 🔗 |
Sat 11:15 a.m. - 11:38 a.m.
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Invited Talk: Jitendra Malik
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Invited Talk
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Jitendra Malik 🔗 |
Sat 11:38 a.m. - 11:40 a.m.
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QA: Jitendra Malik
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QA
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Jitendra Malik 🔗 |
Sat 11:40 a.m. - 12:03 p.m.
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Invited Talk: Jia Deng
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Invited Talk
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Jia Deng 🔗 |
Sat 12:03 p.m. - 12:05 p.m.
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QA: Jia Deng
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QA
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Jia Deng 🔗 |
Sat 12:05 p.m. - 12:28 p.m.
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Invited Talk: Alexei Efros
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Invited Talk
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Alexei Efros 🔗 |
Sat 12:28 p.m. - 12:30 p.m.
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QA: Alexei Efros
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QA
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Alexei Efros 🔗 |
Sat 12:30 p.m. - 1:30 p.m.
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Break
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Sat 1:30 p.m. - 1:53 p.m.
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Invited Talk: Yann LeCun
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Invited Talk
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Yann LeCun 🔗 |
Sat 1:53 p.m. - 1:55 p.m.
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QA: Yann LeCun
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QA
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Yann LeCun 🔗 |
Sat 1:55 p.m. - 2:18 p.m.
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Invited Talk: Kristen Grauman
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Invited Talk
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Kristen Grauman 🔗 |
Sat 2:18 p.m. - 2:20 p.m.
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QA: Kristen Grauman
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QA
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Kristen Grauman 🔗 |
Sat 2:20 p.m. - 2:43 p.m.
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Invited Talk: Katerina Fragkiadaki
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Invited Talk
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Katerina Fragkiadaki 🔗 |
Sat 2:43 p.m. - 2:45 p.m.
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QA: Katerina Fragkiadaki
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QA
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Katerina Fragkiadaki 🔗 |
Sat 2:45 p.m. - 3:08 p.m.
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Invited Talk: Abhinav Gupta
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Invited Talk
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Abhinav Gupta 🔗 |
Sat 3:08 p.m. - 3:10 p.m.
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QA: Abhinav Gupta
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QA
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Abhinav Gupta 🔗 |
Sat 3:10 p.m. - 4:10 p.m.
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Poster Session II ( Poster Session ) link » | 🔗 |
Sat 4:10 p.m. - 4:33 p.m.
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Invited Talk: Leonidas J. Guibas
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Invited Talk
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SlidesLive Video » |
Leonidas Guibas 🔗 |
Sat 4:33 p.m. - 4:35 p.m.
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QA: Leonidas J. Guibas
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QA
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Leonidas Guibas 🔗 |
Sat 4:35 p.m. - 4:58 p.m.
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Invited Talk: Quoc V. Le
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Invited Talk
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Quoc V. Le 🔗 |
Sat 4:58 p.m. - 5:00 p.m.
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QA: Quoc V. Le
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QA
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Quoc V. Le 🔗 |
Sat 5:00 p.m. - 5:23 p.m.
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Invited Talk: Chelsea Finn
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Invited Talk
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SlidesLive Video » |
Chelsea Finn 🔗 |
Sat 5:23 p.m. - 5:25 p.m.
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QA: Chelsea Finn
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QA
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Chelsea Finn 🔗 |
Sat 5:25 p.m. - 5:39 p.m.
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Contributed Talk: Yuandong Tian
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Contributed talk
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SlidesLive Video » |
Yuandong Tian 🔗 |
Sat 5:39 p.m. - 5:40 p.m.
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QA: Yuandong Tian
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QA
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Yuandong Tian 🔗 |
Sat 5:40 p.m. - 6:40 p.m.
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Panel Discussion & Closing
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Yejin Choi · Alexei Efros · Chelsea Finn · Kristen Grauman · Quoc V Le · Yann LeCun · Ruslan Salakhutdinov · Eric Xing 🔗 |
Author Information
Pengtao Xie (Carnegie Mellon University)
Shanghang Zhang (UC Berkeley)
Pulkit Agrawal (MIT)
Ishan Misra (Facebook AI Research)
Cynthia Rudin (Duke)
Abdelrahman Mohamed (Facebook AI Research (FAIR))
Wenzhen Yuan
Barret Zoph (Google Research, Brain Team)
Laurens van der Maaten (Facebook)
Xingyi Yang (University of California San Diego)
Eric Xing (Petuum Inc. / Carnegie Mellon University)
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Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing -
2016 Poster: Learning to Poke by Poking: Experiential Learning of Intuitive Physics »
Pulkit Agrawal · Ashvin Nair · Pieter Abbeel · Jitendra Malik · Sergey Levine -
2016 Oral: Learning to Poke by Poking: Experiential Learning of Intuitive Physics »
Pulkit Agrawal · Ashvin Nair · Pieter Abbeel · Jitendra Malik · Sergey Levine -
2016 Poster: Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices »
Kirthevasan Kandasamy · Maruan Al-Shedivat · Eric Xing -
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: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
2015 Spotlight: The Human Kernel »
Andrew Wilson · Christoph Dann · Chris Lucas · Eric Xing -
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: Modern Machine Learning and Natural Language Processing »
Ankur P Parikh · Avneesh Saluja · Chris Dyer · Eric Xing -
2014 Poster: On Model Parallelization and Scheduling Strategies for Distributed Machine Learning »
Seunghak Lee · Jin Kyu Kim · Xun Zheng · Qirong Ho · Garth Gibson · Eric Xing -
2014 Poster: Dependent nonparametric trees for dynamic hierarchical clustering »
Kumar Avinava Dubey · Qirong Ho · Sinead Williamson · Eric Xing -
2013 Poster: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Oral: More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server »
Qirong Ho · James Cipar · Henggang Cui · Seunghak Lee · Jin Kyu Kim · Phillip B. Gibbons · Garth Gibson · Greg Ganger · Eric Xing -
2013 Poster: Variance Reduction for Stochastic Gradient Optimization »
Chong Wang · Xi Chen · Alexander Smola · Eric Xing -
2013 Poster: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Spotlight: Restricting exchangeable nonparametric distributions »
Sinead Williamson · Steven MacEachern · Eric Xing -
2013 Poster: A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks »
Junming Yin · Qirong Ho · Eric Xing -
2012 Workshop: Spectral Algorithms for Latent Variable Models »
Ankur P Parikh · Le Song · Eric Xing -
2012 Poster: Monte Carlo Methods for Maximum Margin Supervised Topic Models »
Qixia Jiang · Jun Zhu · Maosong Sun · Eric Xing -
2012 Poster: On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks »
Qirong Ho · Junming Yin · Eric Xing -
2012 Poster: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2012 Spotlight: Symmetric Correspondence Topic Models for Multilingual Text Analysis »
Kosuke Fukumasu · Koji Eguchi · Eric Xing -
2011 Poster: Infinite Latent SVM for Classification and Multi-task Learning »
Jun Zhu · Ning Chen · Eric Xing -
2011 Poster: Kernel Embeddings of Latent Tree Graphical Models »
Le Song · Ankur P Parikh · Eric Xing -
2011 Poster: Large-Scale Category Structure Aware Image Categorization »
Bin Zhao · Li Fei-Fei · Eric Xing -
2010 Poster: Large Margin Learning of Upstream Scene Understanding Models »
Jun Zhu · Li-Jia Li · Li Fei-Fei · Eric Xing -
2010 Poster: Predictive Subspace Learning for Multi-view Data: a Large Margin Approach »
Ning Chen · Jun Zhu · Eric Xing -
2010 Poster: Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification »
Li-Jia Li · Hao Su · Eric Xing · Li Fei-Fei -
2010 Poster: Adaptive Multi-Task Lasso: with Application to eQTL Detection »
Seunghak Lee · Jun Zhu · Eric Xing -
2009 Poster: Heterogeneous multitask learning with joint sparsity constraints »
Xiaolin Yang · Seyoung Kim · Eric Xing -
2009 Poster: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Spotlight: Time-Varying Dynamic Bayesian Networks »
Le Song · Mladen Kolar · Eric Xing -
2009 Poster: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2009 Spotlight: Sparsistent Learning of Varying-coefficient Models with Structural Changes »
Mladen Kolar · Le Song · Eric Xing -
2008 Workshop: Analyzing Graphs: Theory and Applications »
Edo M Airoldi · David Blei · Jake M Hofman · Tony Jebara · Eric Xing -
2008 Poster: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Spotlight: Mixed Membership Stochastic Blockmodels »
Edo M Airoldi · David Blei · Stephen E Fienberg · Eric Xing -
2008 Poster: Partially Observed Maximum Entropy Discrimination Markov Networks »
Jun Zhu · Eric Xing · Bo Zhang -
2007 Workshop: Statistical Network Models »
Kevin Murphy · Lise Getoor · Eric Xing · Raphael Gottardo -
2007 Poster: HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation »
Bing Zhao · Eric Xing -
2006 Poster: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing -
2006 Talk: A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space »
KyungAh Sohn · Eric Xing