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Workshop on ML for Systems at NeurIPS 2019, December 14th, 9AM-6PM Vancouver Convention Centre, West Level 2 202-204

Compute requirements are growing at an exponential rate1, and optimizing these computer systems often involves complex high-dimensional combinatorial problems. Yet, current methods rely heavily on heuristics. Very recent work has outlined a broad scope where machine learning vastly outperforms these traditional heuristics, including scheduling2,12, data structure design3,9, microarchitecture4, compilers5,8, circuit design7,10, and the control of warehouse scale computing systems6. In order to continue to scale these computer systems, new learning approaches are needed. The goal of this workshop is to develop novel machine learning methods to optimize and accelerate software and hardware systems.

The main objective of this workshop is to expand upon this recent work and build a community focused on using machine learning in computer architecture and systems problems. We seek to improve the state of the art in the areas where learning has already proven to perform better than traditional heuristics, as well as expand to new areas throughout the system stack such as hardware/circuit design and operating/runtime systems.

We expect this year to improve the state of the art in areas where learning has already proven to outperform traditional heuristics. We also expect to expand into new areas throughout the systems stack, such as computer architecture and operating/runtime systems, and incorporate new ML topics like relational learning. Given that the community is larger than last year, for NeurIPS 2019, we intend to foster more discussion through breakout sessions. The interdisciplinary nature of this area makes NeurIPS an ideal venue for this workshop.

By forming a community of academic and industrial researchers who are excited about this area, we seek to build towards intelligent, self optimizing systems and answer questions such as: How do we generate and share high quality datasets that span the layers of the system stack? Which learned representations best represent code performance and runtime? Which simulators and simulation methodologies provide a tractable proving ground techniques like reinforcement learning?

To this end, the target audience for this workshop includes a wide variety of attendees from state-of-the-art researchers in machine learning to domain experts in computer systems design. We have invited a broad set of expert speakers to present the potential for impact of combining deep learning research with computer systems. We hope that by providing a formal venue for researchers from both fields to meet and interact, that the result will include both fundamental research in ML as well as real-world impact to computer systems design and implementation.

The workshop will host 6 speakers and we invite researchers to submit relevant papers through our call for papers. The speakers, and potentially other relevant stakeholders, are invited to participate in a panel discussion to end the workshop. See the schedule.

Speakers

Jeff Dean

Keynote Speaker

Senior Fellow, Google AI. Google Brain lead and co-founder. Co-designer and implementor of Tensorflow, MapReduce, BigTable, Spanner.

Eytan Bakshy

Senior scientist on the Facebook Core Data Science Team where he leads the Adaptive Experimentation group. He is particularly interested in developing scalable and robust methods for sequential experimentation and reinforcement learning for real-world applications. In his former life, he was interested in substantive questions around the role of peer effects in online and offline behaviors, including information diffusion and civic engagement.

Akanksha Jain

Research Associate at the Computer Science department of the University of Texas at Austin. She researches Computer Architecture, with a focus on memory system performance. Her research has introduced novel ways to improve hardware caching and prefetching. She is the inventor of the Hawkeye cache replacement policy, which won the 2017 Cache Replacement Championship. Her current research focus is to make machine learning a viable tool for computer architects.

Ion Stoica

Ion Stoica is a Professor in the EECS Department at University of California at Berkeley leading the RISELab. He does research on cloud computing and networked computer systems. Past work includes Apache Spark, Apache Mesos, Tachyon, Chord DHT, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards, including the SIGOPS Hall of Fame Award (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001). In 2013, he co-founded Databricks a startup to commercialize technologies for Big Data processing, and in 2006 he co-founded Conviva, a startup to commercialize technologies for large scale video distribution.

Other speakers TBD

Organizing Committee

Program Committee

  • Michael Carbin, MIT
  • Carl Case, NVIDIA
  • Erich Elsen, Google
  • Andrew Gibiansky, Voicery
  • Anna Goldie, Google Brain
  • Rama Govindaraju, Google
  • Milad Hashemi, Google
  • Sara Hooker, Google
  • Safeen Huda, Google
  • Arpith Jacob, Google
  • Joe Jiang, Google
  • Derek Lockhart, Google
  • Martin Maas, Google
  • Sadhika Malladi, Princeton
  • Azalia Mirhoseini, Google Brain
  • Ashish Naik, Google
  • Azade Nazi, Google Brain
  • Aurojit Panda, New York University Courant
  • Jonathan Raiman, OpenAI
  • Herman Schmit, Google
  • Siddhartha Sen, Microsoft Research
  • Shubho Sengupta, Facebook AI Research
  • Narges Shahidi, Google
  • Zhan Shi, University of Texas at Austin
  • Tatiana Shpeisman, Google
  • Ebrahim Songhori, Google
  • Suvinay Subramanian, Google
  • Kevin Swersky, Google Brain
  • Phillipe Tillet, Harvard
  • Minjie Wang, New York University
  • John Whaley , UnifyID Inc
  • Qiumin Xu, Google
  • Xinlei Xu, New York University
  • Amir Yazdanbakhsh, Google Research
  • Dan Zhang, Google X
  • Yanqi Zhou, Google

Contact Us

Contact us at mlforsystems@googlegroups.com.