README.md

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

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Contents

• Invited Talks

• Tutorials

• Symposia

• Workshops

• Posters

• WiML

Invited Talks

• Predictive Learning

  Yann LeCun

• Intelligent Biosphere

  Drew Purves

• Engineering Principles From Stable and Developing Brains

  Saket Navlakha

• Machine Learning and Likelihood-Free Inference in Particle Physics

  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

  Jennifer Wortman Vaughan

• Deep Reinforcement Learning Through Policy Optimization

  Pieter Abbeel · John Schulman

• Variational Inference: Foundations and Modern Methods

  David Blei · Shakir Mohamed · Rajesh Ranganath

• Theory and Algorithms for Forecasting Non-Stationary Time Series

  Vitaly Kuznetsov · Mehryar Mohri

• Nuts and Bolts of Building Applications using Deep Learning

  Andrew Y Ng

• Natural Language Processing for Computational Social Science

  Cristian Danescu-Niculescu-Mizil · Lillian Lee

• Generative Adversarial Networks

  Ian Goodfellow

• Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity - Part I & Part II

  Suvrit Sra · Francis Bach

• ML Foundations and Methods for Precision Medicine and Healthcare

  Suchi Saria · Peter Schulam

  WiML

• Designing Algorithms for Practical Machine Learning

  Maya Gupta, Google Research.

• On the Expressive Power of Deep Neural Networks

  Maithra Raghu, Cornell Univ / Google Brain.

• Ancestral Causal Inference

  Sara Magliacane, VU Univ Amsterdam.

• Towards a Reasoning Engine for Individualizing Healthcare

  Suchi Saria, John Hopkins Univ.

• Learning Representations from Time Series Data through Contextualized LSTMs

  Madalina Fiterau, Stanford Univ.

• Towards Conversational Recommender Systems

  Konstantina Christakopoulou, Univ Minnesota.

• Large-Scale Machine Learning through Spectral Methods: Theory & Practice

  Anima Anandkumar, Amazon / UC Irvine.

• Raffle and WiML Updates

  Tamara Broderick, MIT and Sinead Williamson, UT Austin

• Using Convolutional Neural Networks to Estimate Population Density from High Resolution Satellite Images

  Amy Zhang, Facebook.

• Graphons and Machine Learning: Estimation of Sparse Massive Networks

  Jennifer Chayes, Microsoft Research.

Workshops

• Adversarial Training

  David Lopez-Paz · Leon Bottou · Alec Radford

  • Introduction to Generative Adversarial Networks

    Ian Goodfellow

  • How to train a GAN?

    Soumith Chintala

  • Learning features to distinguish distributions

    Arthur Gretton

  • Training Generative Neural Samplers using Variational Divergence

    Sebastian Nowozin

  • Adversarially Learned Inference (ALI) and BiGANs

    Aaron Courville

  • Energy-Based Adversarial Training and Video Prediction

    Yann LeCun

• Deep Reinforcement Learning

  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

    John Schulman

  • Learning to navigate

    Raia Hadsell

  • Large-Scale Self-Supervised Robot Learning

    Chelsea Finn

  • Challenges for human-level learning in Deep RL

    Josh Tenenbaum

  • Task Generalization via Deep Reinforcement Learning

    Junhyuk Oh

• Neural Abstract Machines & Program Induction

  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?

    Stephen Muggleton

  • In Search of Strong Generalization: Building Structured Models in the Age of Neural Networks

    Daniel Tarlow

  • Learning Program Representation: Symbols to Semantics

    Charles Sutton

  • From temporal abstraction to programs

    Doina Precup

  • Learning to Compose by Delegation

    Rob Fergus

  • How Can We Write Large Programs without Thinking?

    Percy Liang

  • Program Synthesis and Machine Learning

    Martin Vechev

  • Limitations of RNNs: a computational perspective

    Ed Grefenstette

  • Learning how to Learn Learning Algorithms: Recursive Self-Improvement

    Jürgen Schmidhuber

  • Bayesian program learning: Prospects for building more human-like AI systems

    Joshua Tenenbaum & Kevin Ellis

  • Learning When to Halt With Adaptive Computation Time

    Alex Graves

• The Future of Gradient-Based Machine Learning Software

  (aka Autodiff Workshop aka Automatic Differentiation)

  Alex Wiltschko · Zach DeVito · Frédéric Bastien · Pascal Lamblin

  • Automatic Differentiation: History and Headroom

    Barak A. Pearlmutter

  • TensorFlow: Future Directions for Simplifying Large-Scale Machine Learning

    Jeff Dean

  • No more mini-languages: The power of autodiffing full-featured Python

    David Duvenaud

  • Credit assignment: beyond backpropagation

    Yoshua Bengio

  • Autodiff writes your exponential family inference code

    Matthew Johnson

  • The tension between convenience and performance in automatic differentiation

    Jeffrey M. Siskind

• Reliable Machine Learning in the Wild

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

  • Opening Remarks

    Jacob Steinhardt

  • Rules for Reliable Machine Learning

    Martin A Zinkevich

  • What's your ML Test Score? A rubric for ML production systems

    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

    Moses Charikar, Jacob Steinhardt, Gregory Valiant Doug Tygar

  • Adversarial Examples and Adversarial Training

    Ian Goodfellow

  • Summoning Demons: The Pursuit of Exploitable Bugs in Machine Learning

    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

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

• Machine Intelligence @ NIPS

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

  • A roadmap for communication-based AI

    Marco Baroni

  • The commAI-env environment for communication-based AI

    Allan Jabri

  • Human-like dialogue: Key challenges for AI

    Raquel Fernandez

  • Learning incrementally to become a general problem solver

    Jürgen Schmidhuber

  • From particular to general: A preliminary case study of transfer learning in reading comprehension

    Rudolf Kadlec, Ondrej Bajgar, Jan Kleindienst

  • Consolidating the search for general AI

    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

    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?

    Julian Togelius

  • Minimally naturalistic Artificial Intelligence

    Steven Hansen

  • Remarks on the CommAI-env

    Gemma Boleda

• Machine Learning for Education

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

  • BLAh: Boolean Logic Analysis for Graded Student Response Data

    Phil Grimaldi, OpenStax/Rice University

  • Eliminating testing through continuous assessment

    Steve Ritter, Carnegie Learning

  • Gradescope -- AI for Grading

    Pieter Abbeel, UC Berkeley

  • A Machine Learning Approach to Personalizing Education: Improving Individual Learning through Tracking and Course Recommendation

    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)

    Lise Getoor, UC Santa Cruz

  • Machine Learning Approaches for Learning Analytics: Collaborative Filtering Or Regression With Experts?

    Kangwook Lee, KAIST

  • Using Computational Methods to Improve Feedback for Learners

    Anna Rafferty, Carleton College

  • Estimating student proficiency: Deep learning is not the panacea

    Michael Mozer, CU Boulder

  • Modeling skill interactions with multilayer item response functions

    Yan Karklin, Knewton

  • On Crowdlearning: How do People Learn in the Wild?

    Utkarsh Upadhyay, MPI-SWS

  • Beyond Assessment Scores: How Behavior Can Give Insight into Knowledge Transfer

    Christopher Brinton, Zoomi

  • Using Old Data To Yield Better Personalized Tutoring Systems

    Emma Brunskill, CMU