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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Powering the next 100 years
John Platt
Slides · Video · Code
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Why AI Will Make it Possible to Reprogram the Human Genome
Brendan J Frey
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The Trouble with Bias
Kate Crawford
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The Unreasonable Effectiveness of Structure
Lise Getoor
Slides · Video
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Deep Learning for Robotics
Pieter Abbeel
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Learning State Representations
Yael Niv
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On Bayesian Deep Learning and Deep Bayesian Learning
Yee Whye Teh
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Deep Learning: Practice and Trends
Nando de Freitas · Scott Reed · Oriol Vinyals
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Reinforcement Learning with People
Emma Brunskill
Slides · Video · Code
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A Primer on Optimal Transport
Marco Cuturi · Justin M Solomon
Slides · Video · Code
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Deep Probabilistic Modelling with Gaussian Processes
Neil D Lawrence
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Fairness in Machine Learning
Solon Barocas · Moritz Hardt
Slides · Video · Code
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Statistical Relational Artificial Intelligence: Logic, Probability and Computation
Luc De Raedt · David Poole · Kristian Kersting · Sriraam Natarajan
Slides · Video · Code
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Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning
Josh Tenenbaum · Vikash K Mansinghka
Slides · Video · Code
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Differentially Private Machine Learning: Theory, Algorithms and Applications
Kamalika Chaudhuri · Anand D Sarwate
Slides · Video · Code
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Geometric Deep Learning on Graphs and Manifolds
Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun
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Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw
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A distributed execution engine for emerging AI applications
Ion Stoica
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The Case for Learning Database Indexes
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Virginia Smith
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Accelerating Persistent Neural Networks at Datacenter Scale
Daniel Lo
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DLVM: A modern compiler framework for neural network DSLs
Richard Wei · Lane Schwartz · Vikram Adve
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Machine Learning for Systems and Systems for Machine Learning
Jeff Dean
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Creating an Open and Flexible ecosystem for AI models with ONNX
Sarah Bird · Dmytro Dzhulgakov
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NSML: A Machine Learning Platform That Enables You to Focus on Your Models
Nako Sung
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DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Cody Coleman
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Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew G Wilson · Diederik P. (Durk) Kingma · Zoubin Ghahramani · Kevin P Murphy · Max Welling
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Why Aren't You Using Probabilistic Programming?
Dustin Tran
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Automatic Model Selection in BNNs with Horseshoe Priors
Finale Doshi
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Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression
Max Welling
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Stochastic Gradient Descent as Approximate Bayesian Inference
Matt Hoffman
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Recent Advances in Autoregressive Generative Models
Nal Kalchbrenner
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Deep Kernel Learning
Russ Salakhutdinov
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Bayes by Backprop
Meire Fortunato
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How do the Deep Learning layers converge to the Information Bottleneck limit by Stochastic Gradient Descent?
Naftali (Tali) Tishby
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Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré
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Tales from fMRI: Learning from limited labeled data
Gaël Varoquaux
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Learning from Limited Labeled Data (But a Lot of Unlabeled Data)
Tom Mitchell
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Light Supervision of Structured Prediction Energy Networks
Andrew McCallum
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Forcing Neural Link Predictors to Play by the Rules
Sebastian Riedel
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Panel: Limited Labeled Data in Medical Imaging
Daniel Rubin · Matt Lungren · Ina Fiterau
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Sample and Computationally Efficient Active Learning Algorithms
Nina Balcan
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That Doesn't Make Sense! A Case Study in Actively Annotating Model Explanations
Sameer Singh
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Overcoming Limited Data with GANs
Ian Goodfellow
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What’s so Hard About Natural Language Understanding?
Alan Ritter
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Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · Dustin Tran · Tamara Broderick · Michalis Titsias · David Blei · Max Welling
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Learning priors, likelihoods, or posteriors
Iain Murray
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Learning Implicit Generative Models Using Differentiable Graph Tests
Josip Djolonga
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Gradient Estimators for Implicit Models)
Yingzhen Li
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Variational Autoencoders for Recommendation
Dawen Liang
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Approximate Inference in Industry: Two Applications at Amazon
Cedric Archambeau
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Variational Inference based on Robust Divergences
Futoshi Futami
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Adversarial Sequential Monte Carlo
Kira Kempinska
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Scalable Logit Gaussian Process Classification
Florian Wenzel
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Variational inference in deep Gaussian processes
Andreas Damianou
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Taylor Residual Estimators via Automatic Differentiation
Andrew Miller
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Differential privacy and Bayesian learning
Antti Honkela
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Frequentist Consistency of Variational Bayes
Yixin Wang
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Erich Elsen · Danijar Hafner · Zak Stone · Brennan Saeta
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Nitish Keskar
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Closing the Generalization Gap
Itay Hubara · Elad Hoffer
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Don’t Decay the Learning Rate, Increase the Batchsize)
Sam Smith
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Priya Goyal
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Chris Ying
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Matthew Johnson & Daniel Duckworth
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Shankar Krishnan
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Tim Salimans
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Azalia Mirhoseini
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Gregory Diamos
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Small World Network Architectures
Scott Gray
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Timothy Lillicrap
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Scaling Deep Learning to 15 PetaFlops
Thorsten Kurth
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Simon Knowles
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Ujval Kapasi
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Designing for Supercompute-Scale Deep Learning
Michael James
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Isabelle Guyon · Evelyne Viegas · Sergio Escalera · Jacob D Abernethy
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Balázs Kégl
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Automatic evaluation of chatbots
Varvara Logacheva (speaker) · Mikhail Burtsev
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David Rousseau
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Drew Farris
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Mohanty Sharada
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Kaggle platform
Ben Hamner
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Katja Hofmann
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Laura Seaman
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Jonathan C. Stroud
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Olivier Bousquet
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Rafael Frongillo · Bo Waggoner
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Akshay Balsubramani
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Evelyne Viegas · Sergio Escalera · Isabelle Guyon
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Ruben Martinez-Cantin · José Miguel Hernández-Lobato · Javier Gonzalez
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Towards Safe Bayesian Optimization
Andreas Krause
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Learning to learn without gradient descent by gradient descent
Yutian Chen
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Scaling Bayesian Optimization in High Dimensions
Stefanie Jegelka
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Neuroadaptive Bayesian Optimization - Implications for Cognitive Sciences
Romy Lorenz
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Knowledge Gradient Methods for Bayesian Optimization
Peter Frazier
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Quantifying and reducing uncertainties on sets under Gaussian Process priors
David Ginsbourger
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Benjamin Guedj · Pascal Germain · Francis Bach
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Dimension-free PAC-Bayesian Bounds - Part 1 Part 2
Olivier Catoni
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A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity
Peter Grünwald
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A Tutorial on PAC-Bayesian Theory
François Laviolette
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Some recent advances on Approximate Bayesian Computation techniques
Jean-Michel Marin
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A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Behnam Neyshabur
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Deep Neural Networks: From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes
Dan Roy
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A Strongly Quasiconvex PAC-Bayesian Bound
Yevgeny Seldin
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Distribution Dependent Priors for Stable Learning
John Shawe-Taylor
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Andrew G Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands
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The role of causality for interpretability.
Bernhard Scholkopf
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Interpretable Discovery in Large Image Data Sets
Kiri Wagstaff
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The (hidden) Cost of Calibration.
Bernhard Scholkopf
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Panel Discussion
Hanna Wallach, Kiri Wagstaff, Suchi Saria, Bolei Zhou, and Zack Lipton. Moderated by Rich Caruana.
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Interpretability for AI safety
Victoria Krakovna
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Manipulating and Measuring Model Interpretability.
Jenn Wortman Vaughan
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Debugging the Machine Learning Pipeline.
Jerry Zhu
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Panel Debate and Followup Discussion
Yann LeCun, Kilian Weinberger, Patrice Simard, and Rich Caruana.
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Pieter Abbeel · Yan Duan · David Silver · Satinder Singh · Junhyuk Oh · Rein Houthooft
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Mastering Games with Deep Reinforcement Learning
David Silver
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Reproducibility in Deep Reinforcement Learning and Beyond
Joelle Pineau
Slides · Video
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Neural Map: Structured Memory for Deep RL
Ruslan Salakhutdinov
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Deep Exploration Via Randomized Value Functions
Ben Van Roy
Slides · Video
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Artificial Intelligence Goes All-In
Michael Bowling
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José Hernández-Orallo · Zoubin Ghahramani · Tomaso A Poggio · Adrian Weller · Matthew Crosby
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Opening remarks
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Why the mind evolved: the evolution of navigation in real landscapes
Lucia Jacob
Slides · Video
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The distinctive intelligence of young children: Insights for AI from cognitive development
Alison Gopnik
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Learning from first principles
Demis Hassabis
Slides · Video
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Types of intelligence: why human-like AI is important
Josh Tenenbaum
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The road to artificial general intelligence
Gary Marcus
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Video games and the road to collaborative AI
Katja Hofmann
Slides · Video
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Fair questions
Cynthia Dwork
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States, corporations, thinking machines: artificial agency and artificial intelligence
David Runciman
Slides · Video
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Closing remarks
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Bayesian machine learning: Quantifying uncertainty and robustness at scale
Tamara Broderick
Slides · Video · Code
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Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory
Aishwarya Unnikrishnan
Slides · Video · Code
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Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics
Peyton Greenside
Slides · Video · Code
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Machine Learning for Social Science
Hannah Wallach
Slides · Video · Code
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Fairness Aware Recommendations
Palak Agarwal
Slides · Video · Code
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Reinforcement Learning with a Corrupted Reward Channel
Victoria Krakivna
Slides · Video · Code
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Improving health-care: challenges and opportunities for reinforcement learning
Joelle Pineau
Slides · Video · Code
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Harnessing Adversarial Attacks on Deep Reinforement Learning for Improving Robustness
Zhenyi Tang
Slides · Video · Code
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Time-Critical Machine Learning
Nina Mishra
Slides · Video · Code
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A General Framework for Evaluating Callout Mechanisms in Repeated Auctions
Hoda Heidari
Slides · Video · Code
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Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science
Sarah Bouchat
Slides · Video · Code
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Representation Learning in Large Attributed Graphs
Nesreen K Ahmed
Slides · Video · Code