/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!

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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

  • On Bayesian Deep Learning and Deep Bayesian Learning

    Yee Whye Teh

    Video

  • AlphaZero - Mastering Games without human knowledge

    David Silver

    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

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