Tutorials 2016


Deep Reinforcement Learning through Policy Optimization

Pieter Abbeel (OpenAI, UC Berkeley) and John Schulman (OpenAI)

Large-scale Optimization: Beyond Stochastic Gradient Descent and Convexity

Francis Bach (INRIA, ENS) and Suvrit Sra (MIT)

Variational Inference: Foundations and Modern Methods

David Blei (Columbia), Shakir Mohamed (Google Deepmind) and Rajesh Ranganath (Princeton)

Natural Language Processing for Computational Social Science

Cristian Danescu-Niculescu-Mizil (Cornell) and Lillian Lee (Cornell)

Generative Adversarial Networks

Ian Goodfellow (OpenAI)

Theory and Algorithms for Forecasting Non-Stationary Time Series

Vitaly Kuznetsov (Google) and Mehryar Mohri (Courant Institute, Google Research)

Deep Learning for building AI systems

Andrew Ng (Baidu, Stanford University)

Ml Foundations and Methods for Precision Medicine and Healthcare

Suchi Saria (Johns Hopkins) and Peter Schulam (Johns Hopkins)

Crowdsourcing: Beyond Label Generation

Jenn Wortman Vaughan (Microsoft Research)