{"id":13932866,"url":"https://github.com/hindupuravinash/nips2017","last_synced_at":"2026-01-26T11:30:17.376Z","repository":{"id":77291119,"uuid":"113078107","full_name":"hindupuravinash/nips2017","owner":"hindupuravinash","description":"A list of resources for all invited talks, tutorials, workshops and presentations at NIPS 2017","archived":false,"fork":false,"pushed_at":"2017-12-27T06:17:26.000Z","size":544,"stargazers_count":888,"open_issues_count":0,"forks_count":194,"subscribers_count":80,"default_branch":"master","last_synced_at":"2024-12-30T07:42:17.220Z","etag":null,"topics":["deep-learning","machine-learning","neural-networks","nips-2017"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hindupuravinash.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-12-04T18:16:26.000Z","updated_at":"2024-12-25T16:27:02.000Z","dependencies_parsed_at":null,"dependency_job_id":"d618c6b5-6250-42ec-87ba-174142d1ee52","html_url":"https://github.com/hindupuravinash/nips2017","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hindupuravinash%2Fnips2017","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hindupuravinash%2Fnips2017/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hindupuravinash%2Fnips2017/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hindupuravinash%2Fnips2017/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hindupuravinash","download_url":"https://codeload.github.com/hindupuravinash/nips2017/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239768923,"owners_count":19693764,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","machine-learning","neural-networks","nips-2017"],"created_at":"2024-08-07T21:01:17.253Z","updated_at":"2026-01-26T11:30:17.323Z","avatar_url":"https://github.com/hindupuravinash.png","language":null,"funding_links":[],"categories":["Others"],"sub_categories":[],"readme":"# NIPS 2017\n\n\u003cp align=\"center\"\u003e\u003cimg width=\"50%\" src=\"nips_2017.jpg\" /\u003e\u003c/p\u003e\n\nThis 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.\n\nContributions 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!\n\nCheck out [Deep Hunt](https://www.deephunt.in) - a curated monthly AI newsletter for this repo as a [blog post](https://deephunt.in/nips-2017-e580ebc9c7b2) and follow me on [Twitter](https://www.twitter.com/hindupuravinash).\n\n## Contents\n\n- [Invited Talks](#invited-talks)\n\n- [Tutorials](#tutorials)\n\n- [Workshops](#workshops)\n\n- [WiML](#wiml)\n\n\n## Invited Talks\n\n- **Powering the next 100 years**\n\n  John Platt\n\n  Slides · [Video](https://www.youtube.com/watch?v=HL60wgrT67k) · Code\n\n- **Why AI Will Make it Possible to Reprogram the Human Genome**\n\n  Brendan J Frey\n\n  [Video](https://www.youtube.com/watch?v=QJLQBSQJEus)\n\n- **The Trouble with Bias**\n\n  Kate Crawford\n\n  [Video](https://www.youtube.com/watch?v=fMym_BKWQzk)\n\n- **The Unreasonable Effectiveness of Structure**\n\n  Lise Getoor\n\n  Slides · [Video](https://www.youtube.com/watch?v=t4k5LKCpboc)\n\n- **Deep Learning for Robotics**\n\n  Pieter Abbeel\n\n  [Slides](https://www.dropbox.com/s/fdw7q8mx3x4wr0c/2017_12_xx_NIPS-keynote-final.pdf) · [Video](https://www.youtube.com/watch?v=po9z_tMuEwE) · Code\n\n- **Learning State Representations**\n\n  Yael Niv\n  \n  [Video](https://www.youtube.com/watch?v=FhOwFDGm0d4)\n\n- **On Bayesian Deep Learning and Deep Bayesian Learning**\n\n  Yee Whye Teh\n\n  [Video](https://www.youtube.com/watch?v=9saauSBgmcQ)\n\n## Tutorials\n\n- **Deep Learning: Practice and Trends**\n\n  Nando de Freitas · Scott Reed · Oriol Vinyals\n\n  [Slides](https://drive.google.com/file/d/1SuwiICLERd7SfYo3FiqNG0tCEBUjKcT7/view) · [Video](https://www.youtube.com/watch?v=YJnddoa8sHk) · Code\n\n- **Reinforcement Learning with People**\n\n  Emma Brunskill\n\n  Slides · [Video](https://www.youtube.com/watch?v=TqT9nIx27Eg) · Code\n\n- **A Primer on Optimal Transport**\n\n  Marco Cuturi · Justin M Solomon\n\n  [Slides](https://www.dropbox.com/s/55tb2cf3zipl6xu/aprimeronOT.pdf) · Video · Code\n\n- **Deep Probabilistic Modelling with Gaussian Processes**\n\n  Neil D Lawrence\n\n  [Slides](http://inverseprobability.com/talks/lawrence-nips17/deep-probabilistic-modelling-with-gaussian-processes.html) · [Video](https://www.youtube.com/watch?v=RAiPlfohjJo) · Code    \n\n- **Fairness in Machine Learning**\n\n  Solon Barocas · Moritz Hardt\n\n  [Slides](http://mrtz.org/nips17/#/) · Video · Code\n\n- **Statistical Relational Artificial Intelligence: Logic, Probability and Computation**\n\n  Luc De Raedt · David Poole · Kristian Kersting · Sriraam Natarajan\n\n  Slides · Video · Code\n\n- **Engineering and Reverse-Engineering Intelligence Using Probabilistic Programs, Program Induction, and Deep Learning**\n\n  Josh Tenenbaum · Vikash K Mansinghka\n\n  Slides · Video · Code\n\n- **Differentially Private Machine Learning: Theory, Algorithms and Applications**\n\n  Kamalika Chaudhuri · Anand D Sarwate\n\n  [Slides](http://www.ece.rutgers.edu/~asarwate/nips2017/NIPS17_DPML_Tutorial.pdf) · Video · Code\n\n- **Geometric Deep Learning on Graphs and Manifolds**\n\n  Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun\n\n  [Slides](http://geometricdeeplearning.com/slides/NIPS-GDL.pdf) · [Video](https://www.youtube.com/watch?v=LvmjbXZyoP0) · Code\n  ​            \n## Workshops\n\n- ### [ML Systems Workshop @ NIPS 2017](http://learningsys.org/nips17/index.html)\n\n  Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw\n\n  - A distributed execution engine for emerging AI applications\n\n    Ion Stoica\n\n  - The Case for Learning Database Indexes    \n\n  - [Federated Multi-Task Learning](http://learningsys.org/nips17/assets/slides/mocha-NIPS.pdf)\n\n    Virginia Smith\n\n  - [Accelerating Persistent Neural Networks at Datacenter Scale](http://learningsys.org/nips17/assets/slides/brainwave-nips17.pdf)\n\n    Daniel Lo\n\n  - [DLVM: A modern compiler framework for neural network DSLs](http://learningsys.org/nips17/assets/slides/dlvm-nips17.pdf)\n\n    Richard Wei · Lane Schwartz · Vikram Adve\n\n  - [Machine Learning for Systems and Systems for Machine Learning](http://learningsys.org/nips17/assets/slides/dean-nips17.pdf)\n\n    Jeff Dean\n\n  - [Creating an Open and Flexible ecosystem for AI models with ONNX](http://learningsys.org/nips17/assets/slides/ONNX-workshop.pdf)\n\n    Sarah Bird · Dmytro Dzhulgakov \n\n  - [NSML: A Machine Learning Platform That Enables You to Focus on Your Models](http://learningsys.org/nips17/assets/slides/nsml_slides.pdf)\n\n     Nako Sung\n\n  - [DAWNBench: An End-to-End Deep Learning Benchmark and Competition](http://learningsys.org/nips17/assets/slides/dawn-nips17.pptx)\n\n    Cody Coleman\n\n- ### [Bayesian Deep Learning](http://bayesiandeeplearning.org/)\n\n  Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew G Wilson · Diederik P. (Durk) Kingma · Zoubin Ghahramani · Kevin P Murphy · Max Welling\n\n  - [Why Aren't You Using Probabilistic Programming?](http://dustintran.com/talks/Tran_Probabilistic_Programming.pdf)\n\n    Dustin Tran\n\n  - Automatic Model Selection in BNNs with Horseshoe Priors  \n\n    Finale Doshi\n\n  - Deep Bayes for Distributed Learning, Uncertainty Quantification and Compression\n\n    Max Welling \n\n  - Stochastic Gradient Descent as Approximate Bayesian Inference  \n\n    Matt Hoffman\n\n  - [Recent Advances in Autoregressive Generative Models](https://drive.google.com/file/d/11CNWY5op_J5PvP02J9g8tciAom-MW9MZ/view)\n\n    Nal Kalchbrenner\n\n  - Deep Kernel Learning  \n\n    Russ Salakhutdinov\n\n  - Bayes by Backprop\n\n    Meire Fortunato\n\n  - How do the Deep Learning layers converge to the Information Bottleneck limit by Stochastic Gradient Descent?  \n\n    Naftali (Tali) Tishby \n\n- ### [Learning with Limited Labeled Data: Weak Supervision and Beyond](https://lld-workshop.github.io/)\n\n  Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré\n\n  - [Welcome Note](https://lld-workshop.github.io/slides/opening.pdf)\n\n  - [Tales from fMRI: Learning from limited labeled data](https://lld-workshop.github.io/slides/gael_varoquaux_lld.pdf)   \n\n    Gaël Varoquaux \n\n  - [Learning from Limited Labeled Data (But a Lot of Unlabeled Data)](https://lld-workshop.github.io/slides/tom_mitchell_lld.pdf)\n\n    Tom Mitchell\n\n  - [Light Supervision of Structured Prediction Energy Networks](https://lld-workshop.github.io/slides/andrew_mccallum_lld.pdf)\n\n    Andrew McCallum\n\n  - [Forcing Neural Link Predictors to Play by the Rules](https://lld-workshop.github.io/slides/sebastian_riedel_lld.pdf)\n\n    Sebastian Riedel\n\n  - [Panel: Limited Labeled Data in Medical Imaging](https://lld-workshop.github.io/slides/radiology_panel_lld.pdf)\n\n    Daniel Rubin · Matt Lungren · Ina Fiterau\n\n  - [Sample and Computationally Efficient Active Learning Algorithms](https://lld-workshop.github.io/slides/nina_balcan_lld.pdf)\n\n    Nina Balcan\n\n  - [That Doesn't Make Sense! A Case Study in Actively Annotating Model Explanations](https://lld-workshop.github.io/slides/sameer_singh_lld.pdf)\n\n     Sameer Singh\n\n  - [Overcoming Limited Data with GANs](http://www.iangoodfellow.com/slides/2017-12-09-label.pdf)\n\n    Ian Goodfellow\n\n  - [What’s so Hard About Natural Language Understanding?](https://lld-workshop.github.io/slides/alan_ritter_lld.pdf)\n\n    Alan Ritter\n\n  - [Closing Remarks](https://lld-workshop.github.io/slides/closing.pdf)\n\n- ### [Advances in Approximate Bayesian Inference](http://approximateinference.org/)\n\n  Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · Dustin Tran · Tamara Broderick · Michalis Titsias · David Blei · Max Welling\n\n  - [Learning priors, likelihoods, or posteriors](http://approximateinference.org/2017/schedule/Murray2017.pdf)\n\n    Iain Murray\n\n  - Learning Implicit Generative Models Using Differentiable Graph Tests\n\n    Josip Djolonga \n\n  - [Gradient Estimators for Implicit Models)](http://approximateinference.org/2017/schedule/Li2017.pdf)\n\n    Yingzhen Li\n\n  - Variational Autoencoders for Recommendation\n\n    Dawen Liang\n\n  - [Approximate Inference in Industry: Two Applications at Amazon](http://approximateinference.org/2017/schedule/Archambeau2017.pdf)\n\n    Cedric Archambeau\n\n  - [Variational Inference based on Robust Divergences](http://approximateinference.org/2017/schedule/Futami2017.pdf)\n\n    Futoshi Futami\n\n  - [Adversarial Sequential Monte Carlo](http://approximateinference.org/2017/schedule/Kempinska2017.pdf)\n\n    Kira Kempinska\n\n  - [Scalable Logit Gaussian Process Classification](http://approximateinference.org/2017/schedule/Wenzel2017.pdf)\n\n    Florian Wenzel\n\n  - [Variational inference in deep Gaussian processes](http://adamian.github.io/talks/Damianou_NIPS17.pdf)\n\n    Andreas Damianou\n\n  - [Taylor Residual Estimators via Automatic Differentiation](http://approximateinference.org/2017/schedule/Miller2017.pdf)\n\n    Andrew Miller\n\n  - [Differential privacy and Bayesian learning](http://approximateinference.org/2017/schedule/Honkela2017.pdf)\n    \n    Antti Honkela\n\n  - Frequentist Consistency of Variational Bayes\n    \n    Yixin Wang\n\n- ### [Deep Learning at Supercomputer Scale](https://supercomputersfordl2017.github.io/)\n\n  Erich Elsen · Danijar Hafner · Zak Stone · Brennan Saeta\n\n  - [Generalization Gap](https://supercomputersfordl2017.github.io/Presentations/NIPS2017_SharpMinima.pdf)\n\n    Nitish Keskar\n\n  - [Closing the Generalization Gap](https://supercomputersfordl2017.github.io/Presentations/TrainLongerPresentation.pdf)\n\n    Itay Hubara · Elad Hoffer \n\n  - [Don’t Decay the Learning Rate, Increase the Batchsize)](https://supercomputersfordl2017.github.io/Presentations/DLSC_talk.pdf)\n\n    Sam Smith\n\n  - [ImageNet in 1 Hour](https://supercomputersfordl2017.github.io/Presentations/NIPS-workshop-priya-final.pptx)\n\n    Priya Goyal\n\n  - [ImageNet is the new MNIST](https://supercomputersfordl2017.github.io/Presentations/ImageNetNewMNIST.pdf)\n  \n    Chris Ying  \n\n  - [KFAC and Natural Gradients](https://supercomputersfordl2017.github.io/Presentations/K-FAC.pdf)\n\n    Matthew Johnson \u0026 Daniel Duckworth\n\n  - [Neumann Optimizer](https://supercomputersfordl2017.github.io/Presentations/NeumannOptimizerFinal.pdf)\n\n    Shankar Krishnan\n\n  - [Evolutionary Strategies](https://supercomputersfordl2017.github.io/Presentations/Salimans_ES.pdf)\n\n    Tim Salimans\n\n  - [Learning Device Placement](https://supercomputersfordl2017.github.io/Presentations/DevicePlacementWithDeepRL.pdf)\n\n    Azalia Mirhoseini\n\n  - [Scaling and Sparsity](https://supercomputersfordl2017.github.io/Presentations/scaling-is-predictable.pdf)\n\n    Gregory Diamos\n\n  - [Small World Network Architectures](https://supercomputersfordl2017.github.io/Presentations/SmallWorldNetworkArchitectures.pdf)\n\n    Scott Gray\n\n  - [Scalable RL \u0026 AlphaGo](https://supercomputersfordl2017.github.io/Presentations/DeepReinforcementLearningatScale.pdf)\n    \n    Timothy Lillicrap\n\n  - [Scaling Deep Learning to 15 PetaFlops](https://supercomputersfordl2017.github.io/Presentations/ThorstenLargeScaleDeepLearning.pdf)\n    \n    Thorsten Kurth\n\n  - [Scalable Silicon Compute](https://supercomputersfordl2017.github.io/Presentations/SimonKnowlesGraphCore.pdf)\n\n    Simon Knowles\n\n  - [Practical Scaling Techniques](https://supercomputersfordl2017.github.io/Presentations/practical_scaling_techniques_v6.pdf)\n\n    Ujval Kapasi\n\n  - Designing for Supercompute-Scale Deep Learning\n\n    Michael James\n\n- ### [Machine Learning Challenges as a Research Tool](http://ciml.chalearn.org/ciml2017)\n\n  Isabelle Guyon · Evelyne Viegas · Sergio Escalera · Jacob D Abernethy\n\n  - [RAMP platform](https://drive.google.com/file/d/12CwwCtCLDkp92MurS1aEDXV8YcXJNGJH/view)\n\n    Balázs Kégl\n\n  - [Automatic evaluation of chatbots](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjU0YjZiMmM4ZDVhMTA1ZjA)\n\n    Varvara Logacheva (speaker) · Mikhail Burtsev\n\n  - [TrackML](https://drive.google.com/file/d/1ifBM6PCpIUFSnI_TBiBB5YeCx6Kguj1Q/view)\n\n    David Rousseau\n\n  - [Data science bowl](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjYxMzI1ZDY4ZWE4Yzc4NzQ)\n\n    Drew Farris\n\n  - [CrowdAI](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OmRlOWMwYmI5MGQ1NGNh)\n  \n    Mohanty Sharada\n\n  - Kaggle platform\n\n    Ben Hamner\n\n  - [Project Malmo, Minecraft](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OmZiYTAyYmY3NjhiOGQ0OA)\n\n    Katja Hofmann\n\n  - [Project Alloy](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjdiNzIwMWMwMWY5ZjlhMjY)\n\n    Laura Seaman\n\n  - [Education and public service](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjQ1NWM4ODNkNjQzMjgxNTQ)\n\n    Jonathan C. Stroud\n\n  - [AutoDL (Google challenge)](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjJiMTU0MTlmZjY5NGZiOGI)\n\n    Olivier Bousquet\n\n  - [Scoring rule markets](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjNlNWRlMDdjYTgwNDFkZTA)\n\n    Rafael Frongillo · Bo Waggoner\n\n  - [ENCODE-DREAM challenge](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjZjYzdiZGZkMWI1MjliNzk)\n    \n    Akshay Balsubramani\n\n  - [Codalab platform](https://docs.google.com/a/chalearn.org/viewer?a=v\u0026pid=sites\u0026srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjVmY2U0NTk3M2RhYTRlZGY)\n    \n    Evelyne Viegas · Sergio Escalera · Isabelle Guyon\n\n- ### [Bayesian optimization for science and engineering](https://bayesopt.github.io/index.html)\n\n  Ruben Martinez-Cantin · José Miguel Hernández-Lobato · Javier Gonzalez\n\n  - Towards Safe Bayesian Optimization\n\n    Andreas Krause \n\n  - Learning to learn without gradient descent by gradient descent\n\n    Yutian Chen\n\n  - [Scaling Bayesian Optimization in High Dimensions](https://bayesopt.github.io/slides/2017/bayesopt_2017_jegelka.pdf)\n\n    Stefanie Jegelka\n\n  - [Neuroadaptive Bayesian Optimization - Implications for Cognitive Sciences](https://bayesopt.github.io/slides/2017/Lorenz_NIPS_Workshop_2017.pdf)\n\n    Romy Lorenz\n\n  - [Knowledge Gradient Methods for Bayesian Optimization](https://bayesopt.github.io/slides/2017/BayesOptWorkshopFrazier.pdf)\n  \n    Peter Frazier \n\n  - [Quantifying and reducing uncertainties on sets under Gaussian Process priors](https://bayesopt.github.io/slides/2017/NIPS_BOws_Ginsbourger_09_12_2017.pdf)\n\n    David Ginsbourger\n\n- ### [(Almost) 50 shades of Bayesian Learning: PAC-Bayesian trends and insights](https://bguedj.github.io/nips2017/50shadesbayesian.html)\n\n  Benjamin Guedj · Pascal Germain · Francis Bach\n\n  - Dimension-free PAC-Bayesian Bounds - [Part 1](https://bguedj.github.io/nips2017/pdf/catoni_nips2017_1.pdf) [Part 2](https://bguedj.github.io/nips2017/pdf/catoni_nips2017_2.pdf)\n\n    Olivier Catoni\n\n  - [A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity](https://bguedj.github.io/nips2017/pdf/grunwald_nips2017.pdf)\n\n    Peter Grünwald\n\n  - [A Tutorial on PAC-Bayesian Theory](https://bguedj.github.io/nips2017/pdf/laviolette_nips2017.pdf)\n\n    François Laviolette\n\n  - [Some recent advances on Approximate Bayesian Computation techniques](https://bguedj.github.io/nips2017/pdf/marin_nips2017.pdf)\n\n    Jean-Michel Marin\n\n  - [A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks](https://bguedj.github.io/nips2017/pdf/neyshabur_nips2017.pdf)\n  \n    Behnam Neyshabur\n\n  - [Deep Neural Networks: From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes](https://bguedj.github.io/nips2017/pdf/roy_nips2017.pdf)\n\n    Dan Roy\n \n - [A Strongly Quasiconvex PAC-Bayesian Bound](https://bguedj.github.io/nips2017/pdf/seldin_nips2017.pdf)\n  \n    Yevgeny Seldin\n\n  - [Distribution Dependent Priors for Stable Learning](https://bguedj.github.io/nips2017/pdf/shawe-taylor_nips2017.pdf)\n\n    John Shawe-Taylor\n\n## Symposiums\n\n- ### [Interpretable Machine Learning](http://interpretable.ml/)\n    \n  Andrew G Wilson · Jason Yosinski · Patrice Simard · Rich Caruana · William Herlands\n\n  - The role of causality for interpretability.\n    \n    Bernhard Scholkopf \n\n    [Slides](http://s.interpretable.ml/nips_interpretable_ml_2017_Bernhard_Schoelkopf.pdf) · [Video](https://www.youtube.com/watch?v=9C3RvDs_hHw)\n\n  - Interpretable Discovery in Large Image Data Sets\n    \n    Kiri Wagstaff\n\n    [Slides](http://s.interpretable.ml/nips_interpretable_ml_2017_kiri_wagstaff.pdf) · [Video](https://www.youtube.com/watch?v=_K2wVfi_KDM)\n\n  - The (hidden) Cost of Calibration.\n    \n    Bernhard Scholkopf \n\n    [Slides](http://s.interpretable.ml/nips_interpretable_ml_2017_Kilian_Weinberger.pdf) · [Video](https://www.youtube.com/watch?v=fDtQQ9GlSJY)\n\n  - Panel Discussion\n    \n    Hanna Wallach, Kiri Wagstaff, Suchi Saria, Bolei Zhou, and Zack Lipton. Moderated by Rich Caruana.\n\n    [Video](https://www.youtube.com/watch?v=kruwzfvKt3w)\n\n  - Interpretability for AI safety\n    \n    Victoria Krakovna\n\n    [Slides](http://s.interpretable.ml/nips_interpretable_ml_2017_victoria_Krakovna.pdf) · [Video](https://www.youtube.com/watch?v=3HzIutdlpho)\n\n  - Manipulating and Measuring Model Interpretability.\n    \n    Jenn Wortman Vaughan\n\n    [Slides](http://s.interpretable.ml/nips_interpretable_ml_2017_jenn_wortman_vaughan.pdf) · [Video](https://www.youtube.com/watch?v=8ZoL-cKRf2o)\n\n  - Debugging the Machine Learning Pipeline.\n    \n    Jerry Zhu\n\n    [Slides](http://s.interpretable.ml/nips_interpretable_ml_2017_jerry_zhu.pdf) · [Video](https://www.youtube.com/watch?v=XO2281l_JVw)\n\n  - Panel Debate and Followup Discussion\n    \n    Yann LeCun, Kilian Weinberger, Patrice Simard, and Rich Caruana.\n\n    [Video](https://www.youtube.com/watch?v=2hW05ZfsUUo)\n\n- ### [Deep Reinforcement Learning](https://sites.google.com/view/deeprl-symposium-nips2017/home)\n    \n  Pieter Abbeel · Yan Duan · David Silver · Satinder Singh · Junhyuk Oh · Rein Houthooft\n\n  - Mastering Games with Deep Reinforcement Learning\n    \n    David Silver\n\n    [Video](https://www.youtube.com/watch?v=A3ekFcZ3KNw)\n\n  - Reproducibility in Deep Reinforcement Learning and Beyond\n    \n    Joelle Pineau\n\n    Slides · Video\n\n  - Neural Map: Structured Memory for Deep RL\n    \n    Ruslan Salakhutdinov\n\n    [Slides](http://www.cs.cmu.edu/~rsalakhu/NIPS2017_StructureMemoryForDeepRL.pdf)\n\n  - Deep Exploration Via Randomized Value Functions\n    \n    Ben Van Roy\n\n    Slides · Video\n  \n  - Artificial Intelligence Goes All-In\n    \n    Michael Bowling    \n\n- ### [Kinds of intelligence: types, tests and meeting the needs of society](http://www.kindsofintelligence.org/)\n    \n  José Hernández-Orallo · Zoubin Ghahramani · Tomaso A Poggio · Adrian Weller · Matthew Crosby\n\n  - Opening remarks\n    \n    [Slides](https://intelligence.webs.upv.es/slides/NIPS-symposium-opening.pdf)\n\n  - Why the mind evolved: the evolution of navigation in real landscapes\n    \n    Lucia Jacob\n\n    Slides · Video\n\n  - The distinctive intelligence of young children: Insights for AI from cognitive development\n    \n    Alison Gopnik\n\n    [Slides](https://intelligence.webs.upv.es/slides/Gopnik-NIPS.pptx)\n\n  - Learning from first principles\n    \n    Demis Hassabis\n\n    Slides · Video\n  \n  - Types of intelligence: why human-like AI is important\n    \n    Josh Tenenbaum   \n\n  - The road to artificial general intelligence\n    \n    Gary Marcus\n\n    [Slides](https://intelligence.webs.upv.es/slides/Gopnik-NIPS.pptx)\n\n  - Video games and the road to collaborative AI\n    \n    Katja Hofmann\n\n    [Slides](https://intelligence.webs.upv.es/slides/2017-12-07-Katja-Hofmann-symposium-kinds-of-intelligence.pdf) · Video\n  \n  - Fair questions\n    \n    Cynthia Dwork\n\n    [Slides](https://intelligence.webs.upv.es/slides/NIPS2017-Dwork.pdf)\n \n  - States, corporations, thinking machines: artificial agency and artificial intelligence\n    \n    David Runciman\n\n    Slides · Video\n  \n  - Closing remarks\n    \n    [Slides](https://intelligence.webs.upv.es/slides/NIPS-symposium-closing.pdf)  \n\n## WiML\n\n- **Bayesian machine learning: Quantifying uncertainty and robustness at scale**\n\n  Tamara​ ​Broderick​\n\n  Slides · Video · Code\n\n- **Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory**\n\n  Aishwarya​ ​Unnikrishnan\n\n  Slides · Video · Code\n\n- **Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics**\n\n  Peyton​ ​Greenside​\n\n  Slides · Video · Code\n\n- **Machine Learning for Social Science**\n\n  Hannah​ ​Wallach​\n\n  Slides · Video · Code\n\n- **Fairness Aware Recommendations**\n\n  Palak​ ​Agarwal​\n\n  Slides · Video · Code\n\n- **Reinforcement Learning with a Corrupted Reward Channel**\n\n  Victoria​ ​Krakivna​\n\n  Slides · Video · Code\n\n- **Improving health-care: challenges and opportunities for reinforcement learning**\n\n  Joelle​ ​Pineau​\n\n  Slides · Video · Code\n\n- **Harnessing Adversarial Attacks on Deep Reinforement Learning for Improving Robustness**\n\n  Zhenyi​ ​Tang​\n\n  Slides · Video · Code\n\n- **Time-Critical Machine Learning**\n\n  Nina​ ​Mishra​\n\n  Slides · Video · Code  \n\n- **A General Framework for Evaluating Callout Mechanisms in Repeated Auctions**\n\n  Hoda​ ​Heidari​\n\n  Slides · Video · Code\n\n- **Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science**\n\n  Sarah​ ​Bouchat​\n\n  Slides · Video · Code\n\n- **Representation Learning in Large Attributed Graphs**\n\n  Nesreen​ ​K​ ​Ahmed​\n\n  [Slides](https://www.slideshare.net/NesreenAhmed2/representation-learning-in-large-attributed-graphs) · Video · Code      \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhindupuravinash%2Fnips2017","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhindupuravinash%2Fnips2017","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhindupuravinash%2Fnips2017/lists"}