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NIPS 2017

This year's Neural Information Processing Systems (NIPS) 2017 conference held at Long Beach Convention Center, Long Beach California has been the biggest ever! Here's a list of resources and slides of all invited talks, tutorials and workshops.

Contributions are welcome. You can add links via pull requests or create an issue to lemme know something I missed or to start a discussion. If you know the speakers, please ask them to upload slides online!

Check out Deep Hunt - a curated monthly AI newsletter for this repo as a blog post and follow me on Twitter.

Contents

Invited Talks

  • Powering the next 100 years

    John Platt

    Slides · Video · Code

  • Why AI Will Make it Possible to Reprogram the Human Genome

    Brendan J Frey

    Video

  • The Trouble with Bias

    Kate Crawford

    Video

  • The Unreasonable Effectiveness of Structure

    Lise Getoor

    Slides · Video

  • Deep Learning for Robotics

    Pieter Abbeel

    Slides · Video · Code

  • Learning State Representations

    Yael Niv

    Video

  • On Bayesian Deep Learning and Deep Bayesian Learning

    Yee Whye Teh

    Video

Tutorials

  • Deep Learning: Practice and Trends

    Nando de Freitas · Scott Reed · Oriol Vinyals

    Slides · Video · Code

  • Reinforcement Learning with People

    Emma Brunskill

    Slides · Video · Code

  • A Primer on Optimal Transport

    Marco Cuturi · Justin M Solomon

    Slides · Video · Code

  • Deep Probabilistic Modelling with Gaussian Processes

    Neil D Lawrence

    Slides · Video · Code

  • Fairness in Machine Learning

    Solon Barocas · Moritz Hardt

    Slides · Video · Code

  • Statistical Relational Artificial Intelligence: Logic, Probability and Computation

    Luc De Raedt · David Poole · Kristian Kersting · Sriraam Natarajan

    Slides · Video · Code

  • Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning

    Josh Tenenbaum · Vikash K Mansinghka

    Slides · Video · Code

  • Differentially Private Machine Learning: Theory, Algorithms and Applications

    Kamalika Chaudhuri · Anand D Sarwate

    Slides · Video · Code

  • Geometric Deep Learning on Graphs and Manifolds

    Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun

    Slides · Video · Code ​

Workshops

Symposiums

  • Interpretable Machine Learning

    Andrew G Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands

    • The role of causality for interpretability.

      Bernhard Scholkopf

      Slides · Video

    • Interpretable Discovery in Large Image Data Sets

      Kiri Wagstaff

      Slides · Video

    • The (hidden) Cost of Calibration.

      Bernhard Scholkopf

      Slides · Video

    • Panel Discussion

      Hanna Wallach, Kiri Wagstaff, Suchi Saria, Bolei Zhou, and Zack Lipton. Moderated by Rich Caruana.

      Video

    • Interpretability for AI safety

      Victoria Krakovna

      Slides · Video

    • Manipulating and Measuring Model Interpretability.

      Jenn Wortman Vaughan

      Slides · Video

    • Debugging the Machine Learning Pipeline.

      Jerry Zhu

      Slides · Video

    • Panel Debate and Followup Discussion

      Yann LeCun, Kilian Weinberger, Patrice Simard, and Rich Caruana.

      Video

  • Deep Reinforcement Learning

    Pieter Abbeel · Yan Duan · David Silver · Satinder Singh · Junhyuk Oh · Rein Houthooft

    • Mastering Games with Deep Reinforcement Learning

      David Silver

      Video

    • Reproducibility in Deep Reinforcement Learning and Beyond

      Joelle Pineau

      Slides · Video

    • Neural Map: Structured Memory for Deep RL

      Ruslan Salakhutdinov

      Slides

    • Deep Exploration Via Randomized Value Functions

      Ben Van Roy

      Slides · Video

    • Artificial Intelligence Goes All-In

      Michael Bowling

  • Kinds of intelligence: types, tests and meeting the needs of society

    José Hernández-Orallo · Zoubin Ghahramani · Tomaso A Poggio · Adrian Weller · Matthew Crosby

    • Opening remarks

      Slides

    • Why the mind evolved: the evolution of navigation in real landscapes

      Lucia Jacob

      Slides · Video

    • The distinctive intelligence of young children: Insights for AI from cognitive development

      Alison Gopnik

      Slides

    • Learning from first principles

      Demis Hassabis

      Slides · Video

    • Types of intelligence: why human-like AI is important

      Josh Tenenbaum

    • The road to artificial general intelligence

      Gary Marcus

      Slides

    • Video games and the road to collaborative AI

      Katja Hofmann

      Slides · Video

    • Fair questions

      Cynthia Dwork

      Slides

    • States, corporations, thinking machines: artificial agency and artificial intelligence

      David Runciman

      Slides · Video

    • Closing remarks

      Slides

WiML

  • Bayesian machine learning: Quantifying uncertainty and robustness at scale

    Tamara​ ​Broderick​

    Slides · Video · Code

  • Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory

    Aishwarya​ ​Unnikrishnan

    Slides · Video · Code

  • Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics

    Peyton​ ​Greenside​

    Slides · Video · Code

  • Machine Learning for Social Science

    Hannah​ ​Wallach​

    Slides · Video · Code

  • Fairness Aware Recommendations

    Palak​ ​Agarwal​

    Slides · Video · Code

  • Reinforcement Learning with a Corrupted Reward Channel

    Victoria​ ​Krakivna​

    Slides · Video · Code

  • Improving health-care: challenges and opportunities for reinforcement learning

    Joelle​ ​Pineau​

    Slides · Video · Code

  • Harnessing Adversarial Attacks on Deep Reinforement Learning for Improving Robustness

    Zhenyi​ ​Tang​

    Slides · Video · Code

  • Time-Critical Machine Learning

    Nina​ ​Mishra​

    Slides · Video · Code

  • A General Framework for Evaluating Callout Mechanisms in Repeated Auctions

    Hoda​ ​Heidari​

    Slides · Video · Code

  • Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science

    Sarah​ ​Bouchat​

    Slides · Video · Code

  • Representation Learning in Large Attributed Graphs

    Nesreen​ ​K​ ​Ahmed​

    Slides · Video · Code