Schedule

7:30-8.45: Breakfast (Eclipse Dining Room - included)

8:45-9:00:     Opening Remarks

Session 1: Invited Talks

9:00 - 9:30     What are the Unique Challenges and Opportunities in Systems for ML?, Matei Zaharia, Stanford University and Databricks

9:30 - 10:00     A View of Programming Languages & Software Engineering for ML Software, Caroline Lemieux, UC Berkeley

Morning Poster Session

10:00 - 10:30     Poster Lightning Talks (1 minute each)

10:30 - 11:30     Break and Poster Session (Ballroom/Hallway Rotunda)

Session 2: Contributed Talks

11:30 - 11:45     Contributed Talk 1: Video Event Specification using Programmatic Composition

11:45 - 12:00     Contributed Talk 2: Adaptive Distributed Training of Deep Learning Models

12:00 - 12:15     Contributed Talk 3: Standardizing Evaluation of Neural Network Pruning

12:15 - 12:30     Contributed Talk 4: AliGraph: An Industrial Graph Neural Network Platform

12:30 - 2:00     Lunch (Waterhouse Ballroom)

Session 3: Invited Talks

2:00 - 2:30     Asynchrony and Quantization for Efficient and Scalable Learning, Christopher De Sa, Cornell University

2:30 - 3:00     Learning Based Coded-Computation: A Novel Approach for Resilient Computation in ML Inference Systems, Rashmi K. Vinayak, Carnegie Mellon University

Afternoon Poster Session

3:00 - 4:00     Break and Poster Session

Session 4: Invited Talks

4:00 - 4:30     Building Scalable Systems for Reinforcement Learning and Using Reinforcement Learning for Better Systems, Yuandong Tian, Facebook

4:30 - 5:00     Challenges and Progress in Scaling ML Fairness, Alex Beutel, Google Brain

5:00 - 5:15 Closing Remarks 6:00 - 8:00 Dinner (Peninsula Room - included)