Workshop
ML For Systems
Benoit Steiner · Jonathan Raiman · Martin Maas · Azade Nova · Mimee Xu · Anna Goldie
Mon 13 Dec, 8:55 a.m. PST
ML for Systems is an emerging research area that has shown promising results in the past few years. Recent work has shown that ML can be used to replace heuristics, solve complex optimization problems, and improve modeling and forecasting when applied in the context of computer systems.
As an emerging area, ML for Systems is still in the process of defining the common problems, frameworks and approaches to solving its problems, which requires venues that bring together researchers and practitioners from both the systems and machine learning communities. Past iterations of the workshops focused on providing such a venue and broke new ground on a broad range of emerging new directions in ML for Systems. We want to carry this momentum forward by encouraging the community to explore areas that have previously received less attention. Specifically, the workshop commits to highlighting works that also optimize for security and privacy, as opposed to metrics like speed and memory and use ML to optimize for energy usage and carbon impact. Additionally, this year we will encourage the development of shared methodology, tools, and frameworks.
For the first time since the inception of the workshop, we will organize a competition. This competition will showcase important systems problems, and challenges the ML community to test their methods and algorithms on these problems. Our competition tasks are designed to have a low barrier of entry that attracts newcomers as well as systems veterans.
This setup will allow attendees to meet with top researchers and domain experts, old and new, bridging cutting edge ML research with practical systems design. We hope that providing a prestigious venue for researchers from both fields to meet and interact will result in both fundamental ML research as well as real-world impact to computer systems design and implementation.
Schedule
Mon 8:55 a.m. - 6:00 p.m.
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Poster Session & Hallway Track ( gather.town ) > link | 🔗 |
Mon 9:00 a.m. - 9:25 a.m.
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Opening Remarks
(
Introduction
)
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SlidesLive Video |
Jonathan Raiman · Anna Goldie · Benoit Steiner · Azade Nova · Martin Maas · Mimee Xu 🔗 |
Mon 9:30 a.m. - 10:05 a.m.
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Towards instance-optimized data systems
(
Invited Talk
)
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SlidesLive Video |
Tim Kraska 🔗 |
Mon 10:10 a.m. - 10:40 a.m.
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Accelerating Systems and ML for Science
(
Invited Talk
)
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SlidesLive Video |
Anima Anandkumar 🔗 |
Mon 10:45 a.m. - 11:20 a.m.
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Learning Neurosymbolic Performance Models
(
Invited Talk
)
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SlidesLive Video |
Michael Carbin 🔗 |
Mon 11:30 a.m. - 1:00 p.m.
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Lunch Break & Poster Session ( gather.town ) > link | 🔗 |
Mon 1:00 p.m. - 1:11 p.m.
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Towards Intelligent Load Balancing in Data Centers
(
Spotlight
)
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SlidesLive Video |
Zhiyuan Yao · Thomas Heide Clausen 🔗 |
Mon 1:11 p.m. - 1:20 p.m.
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Learning to Combine Instructions in LLVM Compiler
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Spotlight
)
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SlidesLive Video |
sandya mannarswamy · Dibyendu Das 🔗 |
Mon 1:20 p.m. - 1:30 p.m.
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Generative Optimization Networks for Memory Efficient Data Generation
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Spotlight
)
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SlidesLive Video |
Shreshth Tuli · Shikhar Tuli · Giuliano Casale · Nicholas Jennings 🔗 |
Mon 1:30 p.m. - 1:38 p.m.
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DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software
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Spotlight
)
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SlidesLive Video |
Chuan-Yung Tsai · Graham Taylor 🔗 |
Mon 1:38 p.m. - 1:49 p.m.
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Interpretability of Machine Learning in Computer Systems: Analyzing a Caching Model
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Spotlight
)
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SlidesLive Video |
Leon Sixt · Evan Liu · Marie Pellat · James Wexler · Milad Hashemi · Been Kim · Martin Maas 🔗 |
Mon 1:49 p.m. - 2:00 p.m.
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Automap: Towards Ergonomic Automated Parallelism for ML Models
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Spotlight
)
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SlidesLive Video |
Michael Schaarschmidt · Adam Paszke 🔗 |
Mon 2:00 p.m. - 2:10 p.m.
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Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning
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Spotlight
)
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SlidesLive Video |
Zacharaya Shabka · Georgios Zervas 🔗 |
Mon 2:10 p.m. - 2:22 p.m.
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Reinforced Workload Distribution Fairness
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Spotlight
)
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SlidesLive Video |
Zhiyuan Yao · Zihan Ding · Thomas Heide Clausen 🔗 |
Mon 2:22 p.m. - 2:32 p.m.
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Community Infrastructure for Applying Reinforcement Learning to Compiler Optimizations
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Spotlight
)
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SlidesLive Video |
Chris Cummins · Bram Wasti · Brandon Cui · Olivier Teytaud · Benoit Steiner · Yuandong Tian · Hugh Leather 🔗 |
Mon 2:32 p.m. - 2:42 p.m.
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Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
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Spotlight
)
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SlidesLive Video |
Santiago Miret · Vui Seng Chua · Mattias Marder · Mariano Phielipp · Nilesh Jain · Somdeb Majumdar 🔗 |
Mon 2:42 p.m. - 2:51 p.m.
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Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
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Spotlight
)
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SlidesLive Video |
Jiawei Zhao · Steve Dai · Rangha Venkatesan · Brian Zimmer · Mustafa Ali · Ming-Yu Liu · Brucek Khailany · · Anima Anandkumar 🔗 |
Mon 2:51 p.m. - 3:00 p.m.
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Data-Driven Offline Optimization for Architecting Hardware Accelerators ( Spotlight ) > link | Aviral Kumar · Amir Yazdanbakhsh · Milad Hashemi · Kevin Swersky · Sergey Levine 🔗 |
Mon 3:00 p.m. - 3:07 p.m.
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Achieving Low Complexity Neural Decoders via Iterative Pruning
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Spotlight
)
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SlidesLive Video |
Vikrant Malik · Rohan Ghosh · Mehul Motani 🔗 |
Mon 3:10 p.m. - 3:50 p.m.
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Gather.town Q&A with Speakers of Contributed Talks ( gather.town ) > link | 🔗 |
Mon 3:50 p.m. - 4:25 p.m.
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Learned Compiler Optimizations
(
Invited Talk
)
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SlidesLive Video |
Luis Ceze 🔗 |
Mon 4:30 p.m. - 5:00 p.m.
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ML for Autotuning Production ML Compilers
(
Invited Talk
)
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SlidesLive Video |
Phitchaya Phothilimtha 🔗 |
Mon 5:10 p.m. - 5:40 p.m.
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ML-guided iterative refinement for system optimization
(
Invited Talk
)
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SlidesLive Video |
Yuandong Tian 🔗 |
Mon 5:40 p.m. - 5:55 p.m.
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Closing Remarks
(
Outro
)
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SlidesLive Video |
Jonathan Raiman · Mimee Xu · Martin Maas · Anna Goldie · Azade Nova · Benoit Steiner 🔗 |