MLconf SF 2016 Speaker Resources

We recently asked the speakers of MLconf San Francisco 2016 to share their favorite articles, books & papers with the MLconf audience. We hope you find this list interesting and educational!

Daria Sorokina, Applied Scientist, A9(Amazon)

Amazon Search: The Joy of Ranking Products

Stephanie deWet, Software Engineer, Pinterest

Yunsong Guo. Pinnability: Machine Learning in the Pinterest Home Feed.

Deepak Agarwal, Bee-Chung Chen, Rupesh Gupta, Joshua Hartman, Qi He, Anand Iyer, Sumanth Kolar, Yiming Ma, Pannagadatta Shivaswamy, Ajit Singh, and Liang Zhang. 2014. Activity ranking in LinkedIn feed. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ’14). ACM, New York, NY, USA, 1603-1612. DOI:

Hao Ma, Xueqing Liu, and Zhihong Shen. 2016. User Fatigue in Online News Recommendation. In Proceedings of the 25th International Conference on World Wide Web (WWW ’16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1363-1372. DOI:

Ewa Dominowska. Generating a Billion Personal News Feeds. MLConf SEA 2016. live talk.

Virginia Smith, Researcher, UC Berkeley

CoCoA: A General Framework for Communication-Efficient Distributed Optimization. V. Smith, S. Forte, C. Ma, M. Takac, M. I. Jordan, M. Jaggi. Preprint, 2016.

Adding vs. Averaging in Distributed Primal-Dual Optimization. C. Ma, V. Smith, M. Jaggi, M. I. Jordan, P. Richtarik, M. Takac. International Conference on Machine Learning (ICML ’15).

Communication-Efficient Distributed Dual Coordinate Ascent. M. Jaggi, V. Smith, M. Takac, J. Terhorst, S. Krishnan, T. Hofmann, M. I. Jordan. Neural Information Processing Systems (NIPS ’14).

Guy Lebanon, Director of Machine Learning & Data Science, Netflix

Blog Post: Selecting the best artwork for videos through A/B testing

Alex Dimakis, Associate Professor, Dept. of Electrical and Computer Engineering, University of Texas at Austin

Software and datasets:

Tuebingen Benchmark:

Tetrad project:

Entropic Causality:

Video Tutorials:
CCD Summer Short Course 2016
CMU Center for Causal Discovery short course on Causality and Tetrad.
Tutorial: All of Causal Discovery (by Frederick Eberhardt)

Books and Papers:

P. Spirtes, C. Glymour and R. Scheines, Causation, Prediction, and Search. Bradford Books, 2001.

Causality by J. Pearl
Cambridge University Press, 2009.

Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction,
G. Imbens and D. Rubin

Jonas Peters, Peter Buehlmann and Nicolai Meinshausen (2016)
Causal inference using invariant prediction: identification and confidence intervals
Journal of the Royal Statistical Society, Series B

Frederich Eberhardt, Clark Glymour, and Richard Scheines.
On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables.

Alain Hauser and Peter Buhlmann. Two optimal strategies for active learning of causal models from interventional data.
International Journal of Approximate Reasoning, 55(4):926–939, 2014.

Learning Causal Graphs with Small Interventions
K. Shanmugam, M. Kocaoglu, A.G. Dimakis, S. Vishwanath (NIPS 2015)

Nonlinear causal discovery with additive noise models,
Patrik O Hoyer, Dominik Janzing, Joris M Mooij, Jonas Peters, Bernhard Scholkopf (NIPS 2008)[0].pdf

Daniel Shank, Data Scientist, Talla




Graves et al. 2016 – Hybrid computing using a neural network with dynamic external memory
Graves et al. 2014 – Neural Turing Machines
Yu et al. 2015 – Empirical Study on Deep Learning Models for Question Answering
Rae et al. 2016 – Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes

Harm van Seijen, Research Scientist, Maluuba

Further Reading:

“Introduction to Reinforcement Learning” by Richard S. Sutton & Andrew G. Barto

“Algorithms for Reinforcement Learning” by Csaba Szepesvari

“Policy Networks with Two-Stage Training for Dialogue Systems” by Mehdi Fatemi, Layla El Asri, Hannes Schulz, Jing He, Kaheer Suleman

Code Examples:

Simple DQN Example In Python:

Tool For Testing/Developing RL Algorithms: