NIPS 2017

Accumulation of sources from NIPS 2017 in Long Beach, CA. Check out more about NIPS on https://nips.cc/

Currently collecting and feel free to pull requests, make issues or give feedbacks!

Table of Contents

• Tutorials
• Invited Talks
• Symposiums and Workshops
• Orals and Spotlights
• Blogs and Podcasts

Tutorials

• Deep Learning: Practice and Trends by Nando de Freitas, Scott Reed, Oriol Vinyals

  [Facebook_Video] · [Youtube] · [Slides]

• Reinforcement Learning with People by Emma Brunskill

  [Facebook_Video] · Youtube · Slides

• A Primer on Optimal Transport by Marco Cuturi, Justin M Solomon

  Facebook_Video · Youtube · Slides

• Deep Probabilistic Modelling with Gaussian Processes by Neil D Lawrence

  [Facebook_Video] · Youtube · [Slides]

• Fairness in Machine Learning by Solon Barocas, Moritz Hardt

  Facebook_Video · Youtube · [Slides]

• Statistical Relational Artificial Intelligence: Logic, Probability and Computation by Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan

  [Facebook_Video] · Youtube · Slides

• Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning by Josh Tenenbaum, Vikash K Mansinghka

  [Facebook_Video] · Youtube · Slides

• Differentially Private Machine Learning: Theory, Algorithms and Applications by Kamalika Chaudhuri, Anand D Sarwate

  Facebook_Video · Youtube · Slides

• Geometric Deep Learning on Graphs and Manifolds by Michael Bronstein, Joan Bruna, arthur szlam, Xavier Bresson, Yann LeCun

  Facebook_Video · [Youtube] · Slides

  This website is a treasure box for geometric deep learning. Check out http://geometricdeeplearning.com/

Invited Talks

• Opening Remarks / Powering the next 100 years by Terrence Sejnowki / John Platt

  [Facebook_Video] · Youtube · Slides

• Why AI Will Make it Possible to Reprogram the Human Genome by Brendan J Frey

  [Facebook_Video] · [Youtube] · Slides

• Random Features for Large-Scale Kernel Machines by Ali Rahimi, Benjamin Recht (Test of Time Award)

  Facebook_Video · [Youtube] · Slides · [Related Blog by inFERENCe]

• The Trouble with Bias by Kate Crawford

  Facebook_Video · [Youtube] · Slides

• The Unreasonable Effectiveness of Structure by Lise Getoor

  [Facebook_Video] · [Youtube] · Slides

• Deep Learning for Robotics by Pieter Abbeel

  [Facebook_Video] · Youtube · [Slides]

• Learning State Representations by Yael Niv

  [Facebook_Video] · Youtube · Slides

• On Bayesian Deep Learning and Deep Bayesian Learning by Yee Whye Teh

  [Facebook_Video] · [Youtube] · Slides

Symposiums and Workshops

• AlphaZero - Mastering Games without human knowledge by David Silver

  Facebook_Video · [Youtube] · Slides

• GANs for Creativity and Design by Ian Goodfellow

  Facebook_Video · Youtube · [Slides]

• GANs for Limited Labeled Data by Ian Goodfellow

  Facebook_Video · Youtube · [Slides]

• Machine Learning for Systems and Systems for Machine Learning by Jeff Dean

  Facebook_Video · Youtube · [Slides]

• NSML: A Machine Learning Platform That Enables You to Focus on Your Models by Nako Sung

  Facebook_Video · [Youtube] · Slides

• Teaching Artificial Intelligence to Run (NIPS 2017) by CrowdAI

  Facebook_Video · [Youtube] · Slides

Orals and Spotlights

• Algorithm (Tuesday 10:40~12:00)

  [Facebook_Video]

  (Diffusion Approximations for Online Principal Component Estimation and Global Convergence, Positive-Unlabeled Learning with Non- Negative Risk Estimator, An Applied Algorithmic Foundation for Hierarchical Clustering, Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results, QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding, Inhomogeneous Hypergraph Clustering with Applications, K-Medoids for K-Means Seeding, Online Learning with Transductive Regret, Matrix Norm Estimation from a Few Entries, Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding)

• Optimization (Tuesday 10:40~12:00)

  [Facebook_Video]

  (On the Optimization Landscape of Tensor Decompositions, Robust Optimization for Non-Convex Objectives, Bayesian Optimization with Gradients, Gradient Descent Can Take Exponential Time to Escape Saddle Points, Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization, Implicit Regularization in Matrix Factorization, Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls, Acceleration and Averaging in Stochastic Descent Dynamics, When Cyclic Coordinate Descent Beats Randomized Coordinate Descent)

• Theory (Tuesday 14:50~15:50)

  [Facebook_Video]

  (Safe and Nested Subgame Solving for Imperfect-Information Games, A graph-theoretic approach to multitasking, Information-theoretic analysis of generalization capability of learning algorithms, Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee, Clustering Billions of Reads for DNA Data Storage, On the Complexity of Learning Neural Networks, Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos, Estimating Mutual Information for Discrete-Continuous Mixtures)

• Algorithms, Optimization (Tuesday 14:50~15:50)

  [Facebook_Video]

  (Streaming Weak Submodularity: Interpreting Neural Networks on the Fly, A Unified Approach to Interpreting Model Predictions, Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays, Generalized Linear Model Regression under Distance-to-set Penalties, Decomposable Submodular Function Minimization: Discrete and Continuous, Unbiased estimates for linear regression via volume sampling, On Frank-Wolfe and Equilibrium Computation, On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models)

• Deep Learning, Applications (Tuesday 16:20~18:00)

  [Facebook_Video]

  (Unsupervised object learning from dense equivariant image labelling, Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts, Eigen-Distortions of Hierarchical Representations, Towards Accurate Binary Convolutional Neural Network, Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model, Poincaré Embeddings for Learning Hierarchical Representations, Deep Hyperspherical Learning, What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, One-Sided Unsupervised Domain Mapping, Deep Mean-Shift Priors for Image Restoration, Deep Voice 2: Multi-Speaker Neural Text-to-Speech, Graph Matching via Multiplicative Update Algorithm, Dynamic Routing Between Capsules, Modulating early visual processing by language)

• Algorithms (Tuesday 16:20~18:00)

  [Facebook_Video]

  (A Linear-Time Kernel Goodness-of-Fit Test, Generalization Properties of Learning with Random Features, Communication-Efficient Distributed Learning of Discrete Distributions, Optimistic posterior sampling for reinforcement learning: worst-case regret bounds, Regret Analysis for Continuous Dueling Bandit, Minimal Exploration in Structured Stochastic Bandits, Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe, Diving into the shallows: a computational perspective on large-scale shallow learning, Monte-Carlo Tree Search by Best Arm Identification, A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control, Parameter-Free Online Learning via Model Selection, Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction, Gaussian Quadrature for Kernel Features, Learning Linear Dynamical Systems via Spectral Filtering)

• Videos of papers recorded before the conference

  [Video]

Blogs and Podcasts

• NIPS 2017 — notes and thoughs by Olgalitech https://olgalitech.wordpress.com/2017/12/12/nips-2017-notes-and-thoughs/

• NIPS 2017 Notes by David Abel https://cs.brown.edu/~dabel/blog/posts/misc/nips_2017.pdf

• 데이터지능 팟캐스트 E6: Deep learning in NIPS2017 hosted by Jin Young Kim and Terry Um (in Korean) https://www.youtube.com/watch?v=Vm0gQ2eUtBs

• NIPS 2017 Summary! (unless an "official" one gets posted, and then remove this dreck) https://www.reddit.com/r/MachineLearning/comments/7j2v74/d_nips_2017_summary_unless_an_official_one_gets/