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 Cov