A list of resources for all invited talks, tutorials, workshops and presentations at 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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Powering the next 100 years
John Platt
Slides · Video · Code
Why AI Will Make it Possible to Reprogram the Human Genome
Brendan J Frey
The Trouble with Bias
Kate Crawford
The Unreasonable Effectiveness of Structure
Lise Getoor
Slides · Video
Deep Learning for Robotics
Pieter Abbeel
Learning State Representations
Yael Niv
On Bayesian Deep Learning and Deep Bayesian Learning
Yee Whye Teh
Deep Learning: Practice and Trends
Nando de Freitas · Scott Reed · Oriol Vinyals
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
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
Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw
A distributed execution engine for emerging AI applications
Ion Stoica
The Case for Learning Database Indexes
Virginia Smith
Accelerating Persistent Neural Networks at Datacenter Scale
Daniel Lo
DLVM: A modern compiler framework for neural network DSLs
Richard Wei · Lane Schwartz · Vikram Adve
Machine Learning for Systems and Systems for Machine Learning
Jeff Dean
Creating an Open and Flexible ecosystem for AI models with ONNX
Sarah Bird · Dmytro Dzhulgakov
NSML: A Machine Learning Platform That Enables You to Focus on Your Models
Nako Sung
DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Cody Coleman
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew G Wilson · Diederik P. (Durk) Kingma · Zoubin Ghahramani · Kevin P Murphy · Max Welling
Why Aren't You Using Probabilistic Programming?
Dustin Tran
Automatic Model Selection in BNNs with Horseshoe Priors
Finale Doshi
Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression
Max Welling
Stochastic Gradient Descent as Approximate Bayesian Inference
Matt Hoffman
Recent Advances in Autoregressive Generative Models
Nal Kalchbrenner
Deep Kernel Learning
Russ Salakhutdinov
Bayes by Backprop
Meire Fortunato
How do the Deep Learning layers converge to the Information Bottleneck limit by Stochastic Gradient Descent?
Naftali (Tali) Tishby
Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré
Tales from fMRI: Learning from limited labeled data
Gaël Varoquaux
Learning from Limited Labeled Data (But a Lot of Unlabeled Data)
Tom Mitchell
Light Supervision of Structured Prediction Energy Networks
Andrew McCallum
Forcing Neural Link Predictors to Play by the Rules
Sebastian Riedel
Panel: Limited Labeled Data in Medical Imaging
Daniel Rubin · Matt Lungren · Ina Fiterau
Sample and Computationally Efficient Active Learning Algorithms
Nina Balcan
That Doesn't Make Sense! A Case Study in Actively Annotating Model Explanations
Sameer Singh
Overcoming Limited Data with GANs
Ian Goodfellow
What’s so Hard About Natural Language Understanding?
Alan Ritter
Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · Dustin Tran · Tamara Broderick · Michalis Titsias · David Blei · Max Welling
Learning priors, likelihoods, or posteriors
Iain Murray
Learning Implicit Generative Models Using Differentiable Graph Tests
Josip Djolonga
Gradient Estimators for Implicit Models)
Yingzhen Li
Variational Autoencoders for Recommendation
Dawen Liang
Approximate Inference in Industry: Two Applications at Amazon
Cedric Archambeau
Variational Inference based on Robust Divergences
Futoshi Futami
Adversarial Sequential Monte Carlo
Kira Kempinska
Scalable Logit Gaussian Process Classification
Florian Wenzel
Variational inference in deep Gaussian processes
Andreas Damianou
Taylor Residual Estimators via Automatic Differentiation
Andrew Miller
Differential privacy and Bayesian learning
Antti Honkela
Frequentist Consistency of Variational Bayes
Yixin Wang
Erich Elsen · Danijar Hafner · Zak Stone · Brennan Saeta
Nitish Keskar
Closing the Generalization Gap
Itay Hubara · Elad Hoffer
Don’t Decay the Learning Rate, Increase the Batchsize)
Sam Smith
Priya Goyal
Chris Ying
Matthew Johnson & Daniel Duckworth
Shankar Krishnan
Tim Salimans
Azalia Mirhoseini
Gregory Diamos
Small World Network Architectures
Scott Gray
Timothy Lillicrap
Scaling Deep Learning to 15 PetaFlops
Thorsten Kurth
Simon Knowles
Ujval Kapasi
Designing for Supercompute-Scale Deep Learning
Michael James
Isabelle Guyon · Evelyne Viegas · Sergio Escalera · Jacob D Abernethy
Balázs Kégl
Automatic evaluation of chatbots
Varvara Logacheva (speaker) · Mikhail Burtsev
David Rousseau
Drew Farris
Mohanty Sharada
Kaggle platform
Ben Hamner
Katja Hofmann
Laura Seaman
Jonathan C. Stroud
Olivier Bousquet
Rafael Frongillo · Bo Waggoner
Akshay Balsubramani
Evelyne Viegas · Sergio Escalera · Isabelle Guyon
Ruben Martinez-Cantin · José Miguel Hernández-Lobato · Javier Gonzalez
Towards Safe Bayesian Optimization
Andreas Krause
Learning to learn without gradient descent by gradient descent
Yutian Chen
Scaling Bayesian Optimization in High Dimensions
Stefanie Jegelka
Neuroadaptive Bayesian Optimization - Implications for Cognitive Sciences
Romy Lorenz
Knowledge Gradient Methods for Bayesian Optimization
Peter Frazier
Quantifying and reducing uncertainties on sets under Gaussian Process priors
David Ginsbourger
Benjamin Guedj · Pascal Germain · Francis Bach
Dimension-free PAC-Bayesian Bounds - Part 1 Part 2
Olivier Catoni
A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity
Peter Grünwald
A Tutorial on PAC-Bayesian Theory
François Laviolette
Some recent advances on Approximate Bayesian Computation techniques
Jean-Michel Marin
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Behnam Neyshabur
Deep Neural Networks: From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes
Dan Roy
A Strongly Quasiconvex PAC-Bayesian Bound
Yevgeny Seldin
Distribution Dependent Priors for Stable Learning
John Shawe-Taylor
Andrew G Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands
The role of causality for interpretability.
Bernhard Scholkopf
Interpretable Discovery in Large Image Data Sets
Kiri Wagstaff
The (hidden) Cost of Calibration.
Bernhard Scholkopf
Panel Discussion
Hanna Wallach, Kiri Wagstaff, Suchi Saria, Bolei Zhou, and Zack Lipton. Moderated by Rich Caruana.
Interpretability for AI safety
Victoria Krakovna
Manipulating and Measuring Model Interpretability.
Jenn Wortman Vaughan
Debugging the Machine Learning Pipeline.
Jerry Zhu
Panel Debate and Followup Discussion
Yann LeCun, Kilian Weinberger, Patrice Simard, and Rich Caruana.
Pieter Abbeel · Yan Duan · David Silver · Satinder Singh · Junhyuk Oh · Rein Houthooft
Mastering Games with Deep Reinforcement Learning
David Silver
Reproducibility in Deep Reinforcement Learning and Beyond
Joelle Pineau
Slides · Video
Neural Map: Structured Memory for Deep RL
Ruslan Salakhutdinov
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
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
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
Video games and the road to collaborative AI
Katja Hofmann
Slides · Video
Fair questions
Cynthia Dwork
States, corporations, thinking machines: artificial agency and artificial intelligence
David Runciman
Slides · Video
Closing remarks
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