The Neural Information Processing Conference, NeurIPS, formerly known as NIPS, is one of the leading venues for machine learning researchers.  Penn faculty, students (indicated by an * below), and alumni (indicated by a + below) were very well represented!  Aaron Roth and Shivani Agarwal served as Area Chairs, and we had no fewer than 7 papers:

  • Local Differential Privacy for Evolving Data, by Matthew Joseph*, Aaron Roth*, Bo Waggoner+, and Jonathan Ullman.  This Spotlight paper studies how to maintain a statistic with local differential privacy with privacy loss that degrades only with the number of times the statistic changes, not with the number of times you update it.
  • A Smoothed Analysis of the Greedy Algorithm for Linear Contextual Bandits, by Sampath Kannan*, Jamie Morganstern+, Aaron Roth*, Bo Waggoner+, and Steven Wu+.  This Spotlight paper explains why generically you don’t need exploration in linear contextual bandit problems: greedy is enough, so long as the contexts are slightly perturbed.
  • Online Learning with an Unknown Fairness Metric, by Stephen Gillen*, Christopher Jung*, Michael Kearns*, and Aaron Roth*.  This paper shows how to solve an online learning problem subject to an individual fairness metric constraint, when you don’t know the metric, but are friends with a moral philosopher.
  • Learning Loop Invariants for Program Verification, by Xujie Si*, Hanjun Dai, Mukund Raghothaman*, Mayur Naik*, and Le Song.  This Spotlight paper shows how to infer loop invariants, a fundamental problem in software verification, using deep reinforcement learning.
  • Verifiable Reinforcement Learning via Policy Extraction, by Osbert Bastani*, Yewen Pu, and Armando Solar-Lezama. We show how to learn control policies for robotics tasks that we can automatically prove are safe; e.g., they avoid obstacles, do not fall over, etc.
  • Contextual Pricing for Lipschitz Buyers, by Jieming Mao*, Renato Paes Leme, and Jon Schneider.  This paper shows how to maximize profit when many different kinds of goods to buyers with unknown valuations.
  • Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems, by Mrinmaya Sachan, Avinava Dubey, Tom Mitchell, Dan Roth*, and Eric P. Xing. This paper studies joint learning and inference for parsing (and answering) Newtonian Physics problems in textbooks.

Congratulations to all of the authors!

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