Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement...

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Facebook at NeurIPS 2019

Transcript of Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement...

Page 1: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Facebook at NeurIPS 2019

Page 2: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Facebook research being presented at NeurIPS

Tuesday, December 10

Ø AM Posters: 10:45 am - 12:45 pm | East Exhibition Hall B + CChasing Ghosts: Instruction Following as Bayesian State TrackingPeter Anderson, Ayush Shrivastava, Devi Parikh, Dhruv Batra, and Stefan Lee

Learning to Perform Local Rewriting for Combinatorial OptimizationXinyun Chen and Yuandong Tian

Limiting Extrapolation in Linear Approximate Value IterationAndrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, and Emma Brunskill

One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizersAri Morcos, Haonan Yu, Michela Paganini, and Yuandong Tian

Monday, December 9

Ø Expo WorkshopsMulti-modal Research to Production with PyTorch and Facebook

Responsible and Reproducible AI with PyTorch and Facebook

Workshops & other activities at NeurIPS

Sunday, December 8

Ø 8:30 am – 10:30 am | West Hall C + B3Imitation Learning and its Application to Natural Language Generation (tutorial)Kyunghyun Cho

Ø 7:00 am – 8:00 pmWomen in Machine Learning WorkshopCo-organizer: Michela Paganini• CraftAssist: A Framework for Dialogue-enabled Interactive Agents by Kavya Srinet,

Jonathan Gray, Yacine Jernite, Haonan Yu, Zhuoyuan Chen, Demi Guo, Siddharth Goyal, Larry Zitnick, and Arthur Szlam

https://research.fb.com/publications/learning-to-perform-local-rewriting-for-combinatorial-optimization/
https://research.fb.com/publications/limiting-extrapolation-in-linear-approximate-value-iteration/
https://research.fb.com/publications/one-ticket-to-win-them-all-generalizing-lottery-ticket-initializations-across-datasets-and-optimizers/
https://nips.cc/ExpoConferences/2019/schedule%3Fworkshop_id=16
https://nips.cc/ExpoConferences/2019/schedule%3Fworkshop_id=14
https://nips.cc/Conferences/2019/Schedule%3FshowEvent=13209
Page 3: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Tuesday, December 10 cont’d

Ø PM Spotlight: 4:35 pm - 4:40 pm | West Ballrooms A + BA Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement LearningNicolas Carion, Nicolas Usunier, Gabriel Synnaeve, and Alessandro Lazaric

Ø PM Spotlight: 5:15 pm - 5:20 pm | West Ballrooms A + BTight Regret Bounds for Model-Based Reinforcement Learning with Greedy PoliciesYonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh, and Shie Mannor

Ø PM Posters: 5:30 pm - 7:30 pm | East Exhibition Hall B + CExploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPsJian Qian, Ronan Fruit, Matteo Pirotta, and Alessandro Lazaric

Hierarchical Decision Making by Generating and Following Natural Language InstructionsHengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, and Mike Lewis

Learning Temporal Pose Estimation from Sparsely Labeled VideosGedas Bertasius, Christoph Feichtenhofer, Du Tran, Jianbo Shi, and Lorenzo Torresani

On the Curved Geometry of Accelerated OptimizationAaron Defazio

Robust multi-agent counterfactual predictionAlexander Peysakhovich, Christian Kroer, and Adam Lerer

Ø AM Posters: 10:45 am - 12:45 pm | East Exhibition Hall B + CAnti-efficient encoding in emergent communicationRahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, and Marco Baroni

Hyper-Graph-Network Decoders for Block CodesEliya Nachmani and Lior Wolf

On the Ineffectiveness of Variance Reduced Optimization for Deep LearningAaron Defazio and Leon Bottou

Wednesday, December 11

Facebook research being presented at NeurIPS

https://research.fb.com/publications/a-structured-prediction-approach-for-generalization-in-cooperative-multi-agent-reinforcement-learning/
https://research.fb.com/publications/tight-regret-bounds-for-model-based-reinforcement-learning-with-greedy-policies/
https://research.fb.com/publications/exploration-bonus-for-regret-minimization-in-discrete-and-continuous-average-reward-mdps/
https://research.fb.com/publications/hierarchical-decision-making-by-generating-and-following-natural-language-instructions/
https://research.fb.com/publications/learning-temporal-pose-estimation-from-sparsely-labeled-videos/
https://research.fb.com/publications/on-the-curved-geometry-of-accelerated-optimization/
https://research.fb.com/publications/robust-multi-agent-counterfactual-prediction/
https://research.fb.com/publications/hyper-graph-network-decoders-for-block-codes/
https://research.fb.com/publications/on-the-ineffectiveness-of-variance-reduced-optimization-for-deep-learning/
Page 4: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Wednesday, December 11 cont’d

Ø AM Posters: 10:45 am - 12:45 pm | East Exhibition Hall B + CPerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor EnvironmentsDavid Novotny, Benjamin Graham, and Jeremy Reizenstein

Third-Person Visual Imitation Learning via Decoupled Hierarchical ControllerPratyusha Sharma, Deepak Pathak, and Abhinav Gupta

Unsupervised Object Segmentation By RedrawingMickaël Chen, Thierry Artieres, and Ludovic Denoyer

Ø PM Spotlight: 4:40 pm - 4:45 pm | West Exhibition Hall C + B3Cold Case: The Lost MNIST DigitsChhavi Yadav and Leon Bottou

Ø PM Spotlight: 4:55 pm - 5:00 pm | West Ballrooms A + BSuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding SystemsAlex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman

Ø PM Posters: 5:00 pm - 7:00 pm | East Exhibition Hall B + CCorrelated Uncertainty for Learning Dense Correspondences from Noisy LabelsNatalia Neverova, David Novotny, and Andrea Vedaldi

Differentiable Convex Optimization LayersAkshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J. Zico Kolter

Hyperbolic Graph Neural NetworksQi Liu, Maximilian Nickel, and Douwe Kiela

PHYRE: A New Benchmark for Physical ReasoningAnton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, and Ross Girshick

Ø PM Posters: 5:00 pm - 7:00 pm | East Exhibition Hall B + CRUBi: Reducing Unimodal Biases in Visual Question AnsweringRemi Cadene, Corentin Dancette, Hedi Ben younes, Matthieu Cord, and Devi Parikh

ViLBERT: Pretraining Task-Agnostic Representations for Vision and LanguageJiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee

Facebook research being presented at NeurIPS

https://research.fb.com/publications/third-person-visual-imitation-learning-via-decoupled-hierarchical-controller/
https://research.fb.com/publications/cold-case-the-lost-mnist-digits/
https://research.fb.com/publications/superglue-a-stickier-benchmark-for-general-purpose-language-understanding-systems/
https://research.fb.com/publications/correlated-uncertainty-for-learning-dense-correspondences-from-noisy-labels/
https://research.fb.com/publications/differentiable-convex-optimization-layers/
https://research.fb.com/publications/phyre-a-new-benchmark-for-physical-reasoning/
https://research.fb.com/publications/rubi-reducing-unimodal-biases-for-visual-question-answering/
https://research.fb.com/publications/vilbert-pretraining-task-agnostic-visiolinguistic-representations-for-vision-and-language-tasks/
Page 5: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Thursday, December 12

Ø AM Posters: 10:45 am - 12:45 pm | East Exhibition Hall B + CCompositional generalization through meta sequence-to-sequence learningBrenden Lake

Cross-channel Communication NetworksJianwei Yang, Zhile Ren, Chuang Gan, Hongyuan Zhu, and Devi Parikh

Finding the Needle in the Haystack with Convolutions: on the benefits of architectural biasStéphane d'Ascoli, Levent Sagun, Giulio Biroli, and Joan Bruna

Fixing the train-test resolution discrepancyHugo Touvron, Andrea Vedaldi, Matthijs Douze, and Hervé Jegou

Gossip-based Actor-Learner Architectures for Deep Reinforcement LearningMahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, and Mike Rabbat

Ø PM Posters: 5:00 pm - 7:00 pm |. East Exhibition Hall B + CCoda: An End-to-End Neural Program DecompilerCheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, and Jishen Zhao

Levenshtein TransformerJiatao Gu, Changhan Wang, Junbo Zhao

PyTorch: An Imperative Style, High-Performance Deep Learning LibraryAdam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, SasankChilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Facebook research being presented at NeurIPS

https://research.fb.com/publications/compositional-generalization-through-meta-sequence-to-sequence-learning/
https://research.fb.com/publications/cross-channel-communication-networks/
https://research.fb.com/publications/finding-the-needle-in-the-haystack-with-convolutions-on-the-benefits-of-architectural-bias/
https://research.fb.com/publications/levenshtein-transformer/
https://research.fb.com/publications/pytorch-an-imperative-style-high-performance-deep-learning-library/
Page 6: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Friday, December 13

EMC2: Energy Efficient Machine Learning and Cognitive ComputingGrand Keynote: Yann LeCun• Energy-Aware Neural Architecture Optimization with Splitting Steepest Descent by Dilin

Wang, Lemeng Wu, Meng Li, Vikas Chandra, and Qiang Liu• Improving Efficiency in Neural Network Accelerator using Operands Hamming Distance

Optimization by Meng Li, Yilei Li, Pierce Chuang, Liangzhen Lai, and Vikas Chandra

Meta-LearningCo-organizer: Roberto CalandraInvited speaker: Brenden Lake

Retrospectives: A Venue for Self-Reflection in ML ResearchCo-organizers: Jessica Forde, Michela Paganini, Joelle Pineau, Koustuv Sinha, and Shagun Sodhani

Safety and Robustness in Decision MakingCo-organizer: Mohammad Ghavamzadeh• Conservative Exploration in Finite Horizon Markov Decision Processes by Evrard

Garcelon, Mohammad Ghavamzadeh, Alessandro Lazaric, and Matteo Pirotta• Improved Algorithms for Conservative Exploration in Bandits by Evrard Garcelon,

Mohammad Ghavamzadeh, Alessandro Lazaric, and Matteo Pirotta• Robust Identifiability in Linear Structural Equation Models for Causal Inference by

Karthik Abinav Sankararaman, Anand Louis, and Navin Goyal• Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety

Constraints by Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, and Eytan Bakshy

Workshops at NeurIPS

Saturday, December 14

Bridging Game Theory and Deep Learning• A closer look at the optimization landscape of GANs by Hugo Bérard,

Gauthier Gidel, Amjad Almahairi, Pascal Vincent, and Simon Lacoste-Julien

Context and Compositionality in Biological and Artificial Neural SystemsCo-organizer: Kyunghyun Cho

Page 7: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Saturday, December 14 cont’d

Deep Reinforcement LearningCo-organizer: Joelle Pineau• Benchmarking Batch Deep Reinforcement Learning Algorithms by Scott Fujimoto,

Edoardo Conti, Mohammad Ghavamzadeh, and Joelle Pineau• Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement

Learning by Kevin Sebastian Luck, Heni Ben Amor, and Roberto Calandra• Modular Visual Navigation using Active Neural Mapping by Devendra Singh Chaplot,

Saurabh Gupta, Abhinav Gupta, and Ruslan Salakhutdinov• Objective Mismatch in Model-based Reinforcement Learning by Nathan Lambert,

Brandon Amos, Omry Yadan, and Roberto Calandra• Plan2Vec: Unsupervised Representation Learning by Latent Plans by Ge Yang, Amy

Zhang, Ari Morcos, Joelle Pineau, Pieter Abbeel, and Roberto Calandra• Search in Cooperative Partially Observable Games by Adam Lerer, Hengyuan Hu,

Jakob Foerster, and Noam Brown• SEERL: Sample Efficient Ensemble Reinforcement Learning by Rohan Saphal,

Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha, and Bharat Kaul

Emergent Communication: Towards Natural LanguageCo-organizers: Kyunghyun Cho, Douwe Kiela, and Cinjon Resnick

Machine Learning and the Physical SciencesCo-organizer: Michela Paganini

MLSys: Workshop on Systems for MLCo-organizer: Aparna Lakshmiratan• Mvfst-rl: An Asynchronous RL Framework for Congestion Control with Delayed Actions by

Viswanath Sivakumar, Tim Rocktäschel, Alexander H. Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, and Sebastian Riedel

• Post-Training 4-bit Quantization on Embedding Tables by Hui Guan, Andrey Malevich, JiyanYang, Jongsoo Park, and Hector Yuen

• Predictive Precompute with Recurrent Neural Networks by Hanson Wang, Zehui Wang, and Yuanyuan Ma

Workshop on ML for SystemsSpeaker: Eytan Bakshy

Workshops at NeurIPS

Page 8: Facebook at NeurIPS2019 - Facebook Research · Saturday, December 14 cont’d Deep Reinforcement Learning Co-organizer: Joelle Pineau • Benchmarking Batch Deep Reinforcement Learning

Saturday, December 14 cont’d

Robot Learning: Control and Interaction in the Real WorldCo-organizer: Roberto Calandra

Science meets Engineering of Deep LearningCo-organizers: Adriana Romero, Levent SagunSpeakers: Kyunghyun Cho, Natalia NeverovaPanelists: Nafissa Yakubova, Aparna LakshmiratanPanel Advisor: Michela Paganini• Non-Gaussian Processes and Neural Networks at Finite Widths by Sho Yaida• The Generalization-Stability Tradeoff in Neural Network Pruning by Brian R Bartoldson, Ari

Morcos, Adrian Barbu, and Gordon Erlebacher• Training Batchnorm and Only Batchnorm by Jonathan Frankle, David Schwab, and Ari

Morcos

The third Conversational AI workshopCo-chair: Alborz GeramifardInvited speaker: Y-Lan Boureau• Improving Robustness of Task Oriented Dialog Systems by Arash Einolghozati, Sonal Gupta,

Mrinal Mohit, and Rushin Shah

Workshops at NeurIPS


Reinforcement Learning

Reinforcement Learning

Robust Attribution Regularizationpages.cs.wisc.edu/~jiefeng/docs/neurips2019/poster.pdf · Title: Robust Attribution Regularization Author: Jiefeng Chen 1 Xi Wu 2 Vaibhav Rastogi

Robust Attribution Regularizationpages.cs.wisc.edu/~jiefeng/docs/neurips2019/poster.pdf · Title: Robust Attribution Regularization Author: Jiefeng Chen 1 Xi Wu 2 Vaibhav Rastogi

Reinforcement Learning and Deep Reinforcement Learningcse.ucdenver.edu/.../Class-22-Reinforcement-learning-DL.pdf · 2018. 11. 28. · Outlines 1 Principles of Reinforcement Learning

Reinforcement Learning and Deep Reinforcement Learningcse.ucdenver.edu/.../Class-22-Reinforcement-learning-DL.pdf · 2018. 11. 28. · Outlines 1 Principles of Reinforcement Learning

Reinforcement Accessories

Reinforcement Accessories

Generalization in Reinforcement Learning: Successful ...papers.nips.cc/paper/1109-generalization-in-reinforcement-learning... · Generalization in Reinforcement Learning: Successful

Generalization in Reinforcement Learning: Successful ...papers.nips.cc/paper/1109-generalization-in-reinforcement-learning... · Generalization in Reinforcement Learning: Successful

The AP Art History Dinner Party - bremertonschools.org€¦ · facebook facebook facebook facebook facebook facebook facebook faceb facebook facebook facebook fi facebook f; facebook

The AP Art History Dinner Party - bremertonschools.org€¦ · facebook facebook facebook facebook facebook facebook facebook faceb facebook facebook facebook fi facebook f; facebook

Reinforcement Learning - Multi-Agent Reinforcement ...

Reinforcement Learning - Multi-Agent Reinforcement ...

Inverse Reinforcement Learning - Peoplecbfinn/_files/bootcamp_inverserl.pdf · Apprenticeship Learning via Inverse Reinforcement Learning. Good introduction to inverse reinforcement

Inverse Reinforcement Learning - Peoplecbfinn/_files/bootcamp_inverserl.pdf · Apprenticeship Learning via Inverse Reinforcement Learning. Good introduction to inverse reinforcement

Abstract - University of California, Davisthakur.cs.ucdavis.edu/assets/pubs/NeurIPS2019.pdf · Abstract A linear restriction of a function is the same function with its domain restricted

Abstract - University of California, Davisthakur.cs.ucdavis.edu/assets/pubs/NeurIPS2019.pdf · Abstract A linear restriction of a function is the same function with its domain restricted

Reinforcement Sensitivity

Reinforcement Sensitivity

Reinforcement bar

Reinforcement bar

Continuous hoops for transverse reinforcement of ... · Continuous hoops for transverse reinforcement of ... transverse reinforcement details for the ... Fig. 1 Transverse reinforcement

Continuous hoops for transverse reinforcement of ... · Continuous hoops for transverse reinforcement of ... transverse reinforcement details for the ... Fig. 1 Transverse reinforcement

Reinforcement Learning or Active Inference?karl/Reinforcement Learning or Active... · Reinforcement Learning or Active Inference? ... From the point of view of reinforcement learning

Reinforcement Learning or Active Inference?karl/Reinforcement Learning or Active... · Reinforcement Learning or Active Inference? ... From the point of view of reinforcement learning

Greedy Sampling for Clustering in the Presence of Outliersbhaskara/files/neurips2019-pres1.pdf · 2019-10-28 · Experiments K-means experiments on real datasets wherein 2.5% of data

Greedy Sampling for Clustering in the Presence of Outliersbhaskara/files/neurips2019-pres1.pdf · 2019-10-28 · Experiments K-means experiments on real datasets wherein 2.5% of data

Schedules of reinforcement. Schedules of Reinforcement Continuous reinforcement refers to reinforcement being administered to each instance of a response.

Schedules of reinforcement. Schedules of Reinforcement Continuous reinforcement refers to reinforcement being administered to each instance of a response.

Eick: Reinforcement Learning. Reinforcement Learning Introduction Passive Reinforcement Learning Temporal Difference Learning Active Reinforcement Learning.

Eick: Reinforcement Learning. Reinforcement Learning Introduction Passive Reinforcement Learning Temporal Difference Learning Active Reinforcement Learning.

Summer Reinforcement

Summer Reinforcement

Bayesian Reinforcement Learning - mlg.eng.cam.ac.ukmlg.eng.cam.ac.uk/rowan/files/BayesianReinforcementLearning.pdf · Introduction Bayesian Reinforcement Learning Bayesian Reinforcement

Bayesian Reinforcement Learning - mlg.eng.cam.ac.ukmlg.eng.cam.ac.uk/rowan/files/BayesianReinforcementLearning.pdf · Introduction Bayesian Reinforcement Learning Bayesian Reinforcement

From Reinforcement Learning to Deep Reinforcement …fagostin/assets/files/...Keywords: Machine learning · Reinforcement learning Deep learning · Deep reinforcement learning 1 Introduction

From Reinforcement Learning to Deep Reinforcement …fagostin/assets/files/...Keywords: Machine learning · Reinforcement learning Deep learning · Deep reinforcement learning 1 Introduction

Skinner’s Behavioral Reinforcement Theory Positive Reinforcement Negative Reinforcement PunishmentExtinction Person repeats desired behaviors to gain a.

Skinner’s Behavioral Reinforcement Theory Positive Reinforcement Negative Reinforcement PunishmentExtinction Person repeats desired behaviors to gain a.