/nips2017

A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2017

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

  • 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

  • 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

  • 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