MLconf Seattle 2017 Speaker Resources

Andrew Musselman, Committer & PMC Member, Apache Mahout


Byron Galbraith, Chief Data Scientist, Talla

Neural Information Retrieval Resources:

Mitra and Craswell (2017) Neural Models for Information Retrieval

Mitra and Craswell (2017) Neural Text Embeddings for IR

WSDM 2017 Tutorial

Zhang et al. (2016) Neural Information Retrieval: A Literature Review

Neu-IR Workshop at SIGIR


Hanie Sedghi, Research Scientist, Allen Institute for Artificial Intelligence

Janzamin, H. Sedghi and A. Anandkumar, Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods, 2015

Janzamin*, H. Sedghi* and A. Anandkumar, Score Function Features for Discriminative Learning: Matrix and Tensor Framework, 2014

H.Sedghi and A. Anandkumar, Provable Methods for Training Neural Networks with Sparse Connectivity, NIPS Deep Learning Workshop 2014, ICLR workshop 2015

Sedghi, M. Janzamin and A. Anandkumar, Provable Tensor Methods for Learning Mixtures of Generalized Linear Models, AISTATS, 2016

Sedghi and A. Anandkumar, Training Input-Output Recurrent Neural Networks through Spectral Methods, 2016


John Maxwell, Data Scientist, Nordstrom

The Offset Tree for Learning with Partial Labels:

Efficient Bandit Algorithms for Online Multiclass Prediction:

Doubly Robust Policy Evaluation and Learning:

A Contextual-Bandit Approach to Personalized News Article Recommendation:

Microsoft Decision Service White Paper:

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits:

John’s blog: some posts on contextual bandits



Luna Dong, Principal Scientist, Amazon


Margaret Mitchell, Senior Research Scientist, Google


Ryan Calo, Assistant Professor, University of Washington

[1] E.g., Harley Shaiken, A Robot is After Your Job; New technology isn’t a panacea, N.Y. Times, Sept. 3, 1980, at A19. The AI Winter refers to the period in the mid-1980s in which interest in AI began to drop as the field failed to yield significant practical gains. See Artificial Intelligence and Life in 2030, 51, Stanford University, Sept. 2016, online at Skynet is the name of the fictional, murderous artificial intelligence in the Terminator movie series.

[1] Id. at 9, 26–27.

[1] National Science and Technology Council, Exec. Office of the President, Preparing for the Future of Artificial Intelligence, 12 (2016).

[1] U.S. Senate Committee on Commerce, Science, & Transportation, The Dawn of Artificial Intelligence (Nov. 30, 2016), online at; U.S. Congress Joint Economic Committee, The Transformative Impact of Robots and Automation (May 5, 2016), online at  

[1] Ilina Lietzen, Robots: Legal Affairs Committee calls for EU-wide rules, European Parliament News (Jan. 12, 2017, 12:27 PM),;  Robotics Policy Office is to be Established in METI, Ministry of Economy, Trade and Industry (July 1, 2015),

[1] E.g., April Glaser, LinkedIn’s and eBay’s founders are donating $20 million to protect us from artificial intelligence, recode (Jan. 10, 2017, 4:35 PM), online at ($5 million from the Knight foundation and $1 million donation from the Hewlett Foundation to Harvard and MIT); Carnegie Mellon Receives $10 Million From K&L Gates to Study Ethical Issues Posed by Artificial Intelligence, Carnegie Mellon University

News (Nov. 2, 2016), online at ($10 million donation establishing the K&L Gates Endowment for Ethics and Computational Technologies); Max Tegmark, Elon Must donates $10M to keep AI beneficial, Future of Life Institute (Jan. 15, 2015), online at ($10 million donation from Elon Musk to Future of Life Institute).

[1] Harry Surden, The Variable Determinacy Thesis, 12 Colum. Sc. & Tech. L. Rev. 1 (2011). See also Harry Surden, Technological Opacity, Predictability, and Self-Driving Cars, 38 Cardozo L. Rev. 121, 162–63 (2016).

[1] Future of Life Institute, AI and Law, 3:05, YouTube (Feb. 20, 2017),

[1] See José de Sousa E Brito, Right, Duty, and Utility: from Bentham to Kant and from Mill to Aristotle, XVII/2 Revista Iberoamericana de Estudios Utilitaristas 91, 92 (2010).

[1] See, e.g., Nat’l Soc’y of Prof’l Eng’rs v. United States, 435 U.S. 679 (1978) (Department of Justice complaint against engineers for price and marketing practices). See also In the Matter of the American Medical Association, et al., 94 F.T.C. 701 (1979) (doctors); In the Matter of Connecticut Chiropractic Ass’n, 114 F.T.C. 708 (1991) (chiropractors); In the Matter of Nat’l Soc’y of Prof’l Eng’rs, 116 F.T.C. 787 (1993) (engineers).

[1] Romain Dillet, Apple Joins Amazon, Facebook, Google, IBM and Microsoft in AI Initiative, TechCrunch (Jan. 27, 2017), online at  

[1] Ryan Calo, Robotics and the Lessons of Cyberlaw, 103 Calif. L. Rev. 513, 542 (2015).

[1] See Kate Crawford & Ryan Calo, There is a blind spot in AI research, Nature (Oct. 13, 2016), online at (discussing different approaches to AI’s social impacts).

[1] E-mail from Solon Barocas to Ryan Calo (1:12 PM, Jan. 24, 2017) (on file with author).


Serena Yeung, PHD Student, Stanford

End-to-end Learning of Action Detection from Frame Glimpses in Videos. CVPR 2016.

Serena Yeung, Olga Russakovsky, Greg Mori, Li Fei-Fei.

Every Moment Counts: Dense Detailed Labeling of Actions in Videos. IJCV 2017.

Serena Yeung, Olga Russakovsky, Ning Jin, Mykhaylo Andriluka, Greg Mori, Li Fei-Fei.

Learning to Learn from Noisy Web Videos. CVPR 2017.

Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei.


Tianqi Chen, PHD Student, University of Washington