https://github.com/hindupuravinash/nips2016

A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2016
https://github.com/hindupuravinash/nips2016

conference machine-learning nips-2016

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A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2016

• Host: GitHub
• URL: https://github.com/hindupuravinash/nips2016
• Owner: hindupuravinash
• Created: 2016-12-11T12:20:59.000Z (over 8 years ago)
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• Last Pushed: 2017-01-07T19:02:35.000Z (over 8 years ago)
• Last Synced: 2024-12-30T07:42:16.476Z (6 months ago)
• Topics: conference, machine-learning, nips-2016
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  • Readme: README.md

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README

        
# NIPS 2016

A list of all invited talks, tutorials and presentations at Neural Information Processing Systems (NIPS) 2016 conference held at Barcelona and their resources

Contributions are welcome. Add links through pull requests or create an issue to start a discussion.

Check out my [AI newsletter](https://www.getrevue.co/profile/hindupuravinash) and follow me on [Twitter](https://www.twitter.com/hindupuravinash).

## Contents

- [Invited Talks](#invited-talks)

- [Tutorials](#tutorials)

- [Symposia](#symposia)

- [Workshops](#workshops)

- [Posters](#posters)

- [WiML](#wiml)

## Invited Talks

- [Predictive Learning](https://drive.google.com/open?id=0BxKBnD5y2M8NREZod0tVdW5FLTQ)

  

  Yann LeCun

- Intelligent Biosphere

  

  Drew Purves

- Engineering Principles From Stable and Developing Brains

  Saket Navlakha

- [Machine Learning and Likelihood-Free Inference in Particle Physics](https://figshare.com/articles/NIPS_2016_Keynote_Machine_Learning_Likelihood_Free_Inference_in_Particle_Physics/4291565)

  Kyle Cranmer

- Dynamic Legged Robots

  Marc Raibert

- Learning About the Brain: Neuroimaging and Beyond

  Irina Rish

- Reproducible Research: the Case of the Human Microbiome

  Susan Holmes

## Tutorials

- [Crowdsourcing: Beyond Label Generation](http://www.jennwv.com/projects/crowdtutorial.html)

  Jennifer Wortman Vaughan

- [Deep Reinforcement Learning Through Policy Optimization](http://people.eecs.berkeley.edu/~pabbeel/nips-tutorial-policy-optimization-Schulman-Abbeel.pdf)

  Pieter Abbeel · John Schulman

  

- [Variational Inference: Foundations and Modern Methods](http://www.cs.columbia.edu/~blei/talks/2016_NIPS_VI_tutorial.pdf)

  David Blei · Shakir Mohamed · Rajesh Ranganath

- [Theory and Algorithms for Forecasting Non-Stationary Time Series](http://www.cs.nyu.edu/~mohri/talks/NIPSTutorial2016.pdf)

  Vitaly Kuznetsov · Mehryar Mohri

- [Nuts and Bolts of Building Applications using Deep Learning](http://bit.ly/2g9Y09o)

  Andrew Y Ng

- [Natural Language Processing for Computational Social Science](http://www.cs.cornell.edu/~cristian/index_files/NIPS_NLP_for_CSS_tutorial.pdf)

  Cristian Danescu-Niculescu-Mizil · Lillian Lee

- [Generative Adversarial Networks](http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf)

  Ian Goodfellow

- [Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity - Part I](http://www.di.ens.fr/~fbach/fbach_tutorial_vr_nips_2016.pdf) & [Part II](http://www.di.ens.fr/~fbach/ssra_tutorial_vr_nips_2016.pdf)

  Suvrit Sra · Francis Bach

- ML Foundations and Methods for Precision Medicine and Healthcare

  Suchi Saria · Peter Schulam

  

  

  ## WiML

- [Designing Algorithms for Practical Machine Learning](https://www.periscope.tv/WiMLworkshop/1ypKdAZXVOyGW?)

  

  Maya Gupta, Google Research. 

  

- [On the Expressive Power of Deep Neural Networks](https://www.periscope.tv/WiMLworkshop/1DXxyoqrMXgGM?)

  

  Maithra Raghu, Cornell Univ / Google Brain. 

- [Ancestral Causal Inference](https://www.periscope.tv/WiMLworkshop/1DXxyoqryqWGM?)

  

  Sara Magliacane, VU Univ Amsterdam.

  

- [Towards a Reasoning Engine for Individualizing Healthcare](https://www.periscope.tv/WiMLworkshop/1vOxweXvPwgGB?) 

  

  Suchi Saria, John Hopkins Univ. 

  

- [Learning Representations from Time Series Data through Contextualized LSTMs](https://www.periscope.tv/WiMLworkshop/1vOxweXvqjEGB?)  

  

  Madalina Fiterau, Stanford Univ. 

  

- [Towards Conversational Recommender Systems](https://www.periscope.tv/WiMLworkshop/1vAGRXDbvbkxl?)  

  

  Konstantina Christakopoulou, Univ Minnesota. 

  

- [Large-Scale Machine Learning through Spectral Methods: Theory & Practice](https://www.periscope.tv/WiMLworkshop/1gqGvRjOeWOGB?)  

  

  Anima Anandkumar, Amazon / UC Irvine. 

  

- [Raffle and WiML Updates](https://www.periscope.tv/WiMLworkshop/1jMJgAkEVajKL?)  

  

  Tamara Broderick, MIT and Sinead Williamson, UT Austin

  

- [Using Convolutional Neural Networks to Estimate Population Density from High Resolution Satellite Images](https://www.periscope.tv/WiMLworkshop/1MnGnXwZmVMxO?)  

  

  Amy Zhang, Facebook. 

  

- [Graphons and Machine Learning: Estimation of Sparse Massive Networks](https://www.periscope.tv/WiMLworkshop/1dRKZRYgQwvKB?)

  

  Jennifer Chayes, Microsoft Research. 

  

## Workshops

- ### [Adversarial Training](https://sites.google.com/site/nips2016adversarial/)

  

  David Lopez-Paz · Leon Bottou · Alec Radford

  

  - [Introduction to Generative Adversarial Networks](http://www.iangoodfellow.com/slides/2016-12-9-gans.pdf)

    

    Ian Goodfellow

  

  - [How to train a GAN?](https://github.com/soumith/ganhacks)

    

    Soumith Chintala

  

  - [Learning features to distinguish distributions](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/testing_workshop.pdf)

    

    Arthur Gretton

  

  - [Training Generative Neural Samplers using Variational Divergence](http://www.nowozin.net/sebastian/blog/nips-2016-generative-adversarial-training-workshop-talk.html)

    

    Sebastian Nowozin

    

  - Adversarially Learned Inference (ALI) and BiGANs

    

    Aaron Courville

  

  - [Energy-Based Adversarial Training and Video Prediction](https://drive.google.com/file/d/0BxKBnD5y2M8NbzBUbXRwUDBZOVU/view)

    

    Yann LeCun

    

- ### [Deep Reinforcement Learning](https://sites.google.com/site/deeprlnips2016/)

  

  David Silver · Satinder Singh · Pieter Abbeel · Xi Chen  

  

  - Learning representations by stochastic gradient descent in cross-validation error

    

    Rich Sutton

    

  - [The Nuts and Bolts of Deep Reinforcement Learning Research](http://rll.berkeley.edu/deeprlcourse/docs/nuts-and-bolts.pdf)

    

    John Schulman

    

  - [Learning to navigate](https://drive.google.com/file/d/0B1PUpk7kwWu-dGFGaHU5dWJraVNHWWtZcGJXclUwQThaVHU0/view)

  

    Raia Hadsell

  

  - [Large-Scale Self-Supervised Robot Learning](https://drive.google.com/file/d/0B1PUpk7kwWu-SGhJTTJJOGt2bkpSUjZ3TGpKeFFSU0R0ZXRr/view)

  

    Chelsea Finn

    

  - Challenges for human-level learning in Deep RL

  

    Josh Tenenbaum

    

  - [Task Generalization via Deep Reinforcement Learning](https://drive.google.com/file/d/0B1PUpk7kwWu-dmM3NHc1c0hWV2N4YnRRSk5MUU9kODJpcGs4/view)

  

    Junhyuk Oh

- ### [Neural Abstract Machines & Program Induction](https://uclmr.github.io/nampi/)

  

  Matko Bošnjak · Nando de Freitas · Tejas D Kulkarni · Arvind Neelakantan · Scott E Reed · Sebastian Riedel · Tim Rocktäschel

  - [What use is Abstraction in Deep Program Induction?](https://uclmr.github.io/nampi/talk_slides/muggleton-nampi.pdf)

    

    Stephen Muggleton

  

  - [In Search of Strong Generalization: Building Structured Models in the Age of Neural Networks](https://uclmr.github.io/nampi/talk_slides/tarlow-nampi.pdf)

    

    Daniel Tarlow

  

  - [Learning Program Representation: Symbols to Semantics](http://homepages.inf.ed.ac.uk/csutton/talks/nampi2016-talk-sutton/)

    

    Charles Sutton

  - [From temporal abstraction to programs](https://uclmr.github.io/nampi/talk_slides/precup-nampi.pdf)

    Doina Precup

  - Learning to Compose by Delegation

  

    Rob Fergus

  - [How Can We Write Large Programs without Thinking?](https://uclmr.github.io/nampi/talk_slides/liang-nampi.pdf)

    

    Percy Liang

  - Program Synthesis and Machine Learning

    Martin Vechev

  - [Limitations of RNNs: a computational perspective](https://uclmr.github.io/nampi/talk_slides/grefenstette-nampi.pdf)

    Ed Grefenstette

  - [Learning how to Learn Learning Algorithms: Recursive Self-Improvement](https://uclmr.github.io/nampi/talk_slides/schmidhuber-nampi.pdf)

    Jürgen Schmidhuber

  - [Bayesian program learning: Prospects for building more human-like AI systems](https://uclmr.github.io/nampi/talk_slides/tenenbaum-ellis-nampi.pdf)

    Joshua Tenenbaum & Kevin Ellis

  - Learning When to Halt With Adaptive Computation Time

    Alex Graves

- ### [The Future of Gradient-Based Machine Learning Software](https://autodiff-workshop.github.io/)

  

  (aka *Autodiff Workshop* aka *Automatic Differentiation*)

  

  Alex Wiltschko · Zach DeVito · Frédéric Bastien · Pascal Lamblin

  

  - [Automatic Differentiation: History and Headroom](https://autodiff-workshop.github.io/slides/BarakPearlmutter.pdf)

    Barak A. Pearlmutter

    

  - [TensorFlow: Future Directions for Simplifying  Large-Scale Machine Learning](https://autodiff-workshop.github.io/slides/JeffDean.pdf)

    Jeff Dean

  

  - [No more mini-languages: The power of autodiffing full-featured Python](https://autodiff-workshop.github.io/slides/DavidDuvenaud.pdf)

    David Duvenaud

    

  - [Credit assignment: beyond backpropagation](http://www.iro.umontreal.ca/~bengioy/talks/NIPSAutoDiffWorkshop10dec2016.key.pdf)

    Yoshua Bengio

    

  - [Autodiff writes your exponential family inference code](https://autodiff-workshop.github.io/slides/MatthewJohnson.pdf)

    Matthew Johnson

    

  - [The tension between convenience and performance in automatic differentiation](https://autodiff-workshop.github.io/slides/JeffreyMarkSiskind.pdf)

    Jeffrey M. Siskind

    

- ### [Reliable Machine Learning in the Wild](https://sites.google.com/site/wildml2016nips/)

  Dylan Hadfield-Menell · Adrian Weller · David Duvenaud · Jacob Steinhardt · Percy S Liang

  

  - [Opening Remarks](https://0586f9b3-a-62cb3a1a-s-sites.googlegroups.com/site/wildml2016nips/OpeningRemarks.pdf)

  

    Jacob Steinhardt

  

  - [Rules for Reliable Machine Learning](http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf)

    

    Martin A Zinkevich

  

  - [What's your ML Test Score? A rubric for ML production systems](https://0586f9b3-a-62cb3a1a-s-sites.googlegroups.com/site/wildml2016nips/SculleySlides1.pdf)

    Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, D. Sculley

  - Robust Learning and Inference

    Yishay Mansour

  

  - Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

    

    Jennifer Hill

  

  - Robust Covariate Shift Classification Using Multiple Feature Views

    Anqi Liu, Hong Wang Brian D. Ziebart

  - [Learning from Untrusted Data](https://0586f9b3-a-62cb3a1a-s-sites.googlegroups.com/site/wildml2016nips/SteinhardtSlides.pdf)

    

    Moses Charikar, Jacob Steinhardt, Gregory Valiant Doug Tygar

  - [Adversarial Examples and Adversarial Training](https://0586f9b3-a-62cb3a1a-s-sites.googlegroups.com/site/wildml2016nips/GoodfellowSlides.pdf)

    

    Ian Goodfellow

  

  - [Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning](https://0586f9b3-a-62cb3a1a-s-sites.googlegroups.com/site/wildml2016nips/SuciuSlides.pdf)

    

    Rock Stevens, Octavian Suciu, Andrew Ruef, Sanghyun Hong, Michael Hicks, Tudor Dumitras

  - Learning Reliable Objectives

    

    Anca Dragan

  - Building and Validating the AI behind the Next-Generation Aircraft Collision Avoidance System

    Mykel J Kochenderfer

  

  - Online Prediction with Selfish Experts

    

    Okke Schrijvers

  

  - [TensorFlow Debugger: Debugging Dataflow Graphs for Machine Learning](https://0586f9b3-a-62cb3a1a-s-sites.googlegroups.com/site/wildml2016nips/SculleySlides2.pdf)

    

    Shanqing Cai, Eric Breck, Eric Nielsen, Michael Salib, D. Sculley

    

- ### [Machine Intelligence @ NIPS](https://mainatnips.github.io/)

  

  Tomas Mikolov · Baroni Marco · Armand Joulin · Germán Kruszewski · Angeliki Lazaridou · Klemen Simonic

  

  - [A roadmap for communication-based AI](https://mainatnips.github.io/mainatnips.github.io/slides/baroni-nursing-turing.pdf)

    

    Marco Baroni

  

  - [The commAI-env environment for communication-based AI](https://mainatnips.github.io/mainatnips.github.io/slides/allan-commai-env-workshop-talk.pdf)

    

    Allan Jabri

  

  - [Human-like dialogue: Key challenges for AI](https://mainatnips.github.io/mainatnips.github.io/slides/raquel-main-nips2016.pdf)

    

    Raquel Fernandez

  

  - [Learning incrementally to become a general problem solver](https://mainatnips.github.io/mainatnips.github.io/slides/schmidhuber-rsi2016white.pdf)

    

    Jürgen Schmidhuber

  - [From particular to general: A preliminary case study of transfer learning in reading comprehension](https://mainatnips.github.io/mainatnips.github.io/slides/Kadlec_Semisupervised_MAINatNIPS_v3_clean.pdf)

    

    Rudolf Kadlec, Ondrej Bajgar, Jan Kleindienst 

  - [Consolidating the search for general AI](https://mainatnips.github.io/mainatnips.github.io/slides/Rosa%20and%20Feyereisl%20-%20Machine%20Intelligence%20Workshop%20@%20NIPS%202016.pdf)

    

    Marek Rosa, Jan Feyereisl

  - Gaining insights from game theory about the emergence of communication

    

    Alex Peysakhovich

  - Socially constructed machine intelligence

    

    Tomo Lazovich, Matthew C. Graham, Troy M. Lau, Joshua C. Poore

  - [Virtual embodiment: A scalable long-term strategy for Artificial Intelligence research](https://mainatnips.github.io/mainatnips.github.io/slides/DouwePresentation.pdf)

    

    Douwe Kiela, Luana Bulat, Anita L. Vero, Stephen Clark

  - Building machines that learn and think like people

    

    Brenden Lake

  

  - Malmo: Flexible and scalable evaluation in Minecraft

    

    Fernando Diaz 

  

  - A paradigm for situated and goal-driven language learning

  

    Jon Gauthier, Igor Mordatch 

  - In praise of fake AI

  

    Arthur Szlam 

  - An evolutionary perspective on machine intelligence

  

    Emmanuel Dupoux 

  - [Are video games the perfect environments for developing artificial general intelligence? Which kind of general intelligence?](https://mainatnips.github.io/mainatnips.github.io/slides/togelius-2016-NIPSWS-Games4WhichAI.pdf)

    

    Julian Togelius

  

  - [Minimally naturalistic Artificial Intelligence](https://mainatnips.github.io/mainatnips.github.io/slides/HansenMinimally%20Naturalistic%20AI.pdf)

    

    Steven Hansen 

  

  - [Remarks on the CommAI-env](https://mainatnips.github.io/mainatnips.github.io/slides/gboleda-slides-main-at-nips.pdf)

    

    Gemma Boleda 

- ### [Machine Learning for Education](https://dsp.rice.edu/ml4ed_nips2016)

  Richard Baraniuk · Jiquan Ngiam · Christoph Studer · Phillip Grimaldi · Andrew Lan

  

  - [BLAh: Boolean Logic Analysis for Graded Student Response Data](https://dsp.rice.edu/sites/dsp.rice.edu/files/Lan%20-%2016NIPS_talk_BLAh.pptx)

    

    Phil Grimaldi, OpenStax/Rice University

  

  - [Eliminating testing through continuous assessment](https://dsp.rice.edu/sites/dsp.rice.edu/files/Ritter%20-%20assessment%20nips.pptx)

  

    Steve Ritter, Carnegie Learning

  

  - [Gradescope -- AI for Grading](https://dsp.rice.edu/sites/dsp.rice.edu/files/gradescope_nips_ed_workshop_2016.pdf)

    

    Pieter Abbeel, UC Berkeley

  - [A Machine Learning Approach to Personalizing Education: Improving Individual Learning through Tracking and Course Recommendation](https://dsp.rice.edu/sites/dsp.rice.edu/files/Mihaela.pdf)

  

    Mihaela van der Schaar, UCLA

  - Machine Learning Challenges and Opportunities in MOOCs

  

    Zhenghao Chen, Coursera

    

  - [Understanding Engagement and Sentiment in MOOCs using Probabilistic Soft Logic (PSL)](https://dsp.rice.edu/sites/dsp.rice.edu/files/Getoor-NIPS-ED+ML-WS-Dec10.pdf)

    

    Lise Getoor, UC Santa Cruz

  - [Machine Learning Approaches for Learning Analytics: Collaborative Filtering Or Regression With Experts?](https://dsp.rice.edu/sites/dsp.rice.edu/files/Kangwook_ML_for_LA.pdf)

  

    Kangwook Lee, KAIST

    

  - [Using Computational Methods to Improve Feedback for Learners](https://dsp.rice.edu/sites/dsp.rice.edu/files/MLForFeedback.pdf)

  

    Anna Rafferty, Carleton College

  - [Estimating student proficiency: Deep learning is not the panacea](https://dsp.rice.edu/sites/dsp.rice.edu/files/Mozer_NIPS2016_MLED.pptx)

    

    Michael Mozer, CU Boulder

  - [Modeling skill interactions with multilayer item response functions](https://dsp.rice.edu/sites/dsp.rice.edu/files/Karklin_NIPS2016-ML4Ed_Karklin.pdf)

    

    Yan Karklin, Knewton

    

  - [On Crowdlearning: How do People Learn in the Wild?](https://dsp.rice.edu/sites/dsp.rice.edu/files/Utkarsh.pdf)

    

    Utkarsh Upadhyay, MPI-SWS

  - [Beyond Assessment Scores: How Behavior Can Give Insight into Knowledge Transfer](https://dsp.rice.edu/sites/dsp.rice.edu/files/Brinton_16-12-10%20NIPS_Public.pptx)

  

    Christopher Brinton, Zoomi

  - Using Old Data To Yield Better Personalized Tutoring Systems

  

    Emma Brunskill, CMU