Workshop
Learning Meets Combinatorial Algorithms
Marin Vlastelica · Jialin Song · Aaron Ferber · Brandon Amos · Georg Martius · Bistra Dilkina · Yisong Yue
Sat 12 Dec, 3 a.m. PST
We propose to organize a workshop on machine learning and combinatorial algorithms. The combination of methods from machine learning and classical AI is an emerging trend. Many researchers have argued that “future AI” methods somehow need to incorporate discrete structures and symbolic/algorithmic reasoning. Additionally, learning-augmented optimization algorithms can impact the broad range of difficult but impactful optimization settings. Coupled learning and combinatorial algorithms have the ability to impact real-world settings such as hardware & software architectural design, self-driving cars, ridesharing, organ matching, supply chain management, theorem proving, and program synthesis among many others. We aim to present diverse perspectives on the integration of machine learning and combinatorial algorithms.
This workshop aims to bring together academic and industrial researchers in order to describe recent advances and build lasting communication channels for the discussion of future research directions pertaining the integration of machine learning and combinatorial algorithms. The workshop will connect researchers with various relevant backgrounds, such as those working on hybrid methods, have particular expertise in combinatorial algorithms, work on problems whose solution likely requires new approaches, as well as everyone interested in learning something about this emerging field of research. We aim to highlight open problems in bridging the gap between machine learning and combinatorial optimization in order to facilitate new research directions.
The workshop will foster the collaboration between the communities by curating a list of problems and challenges to promote the research in the field.
Our technical topics of interest include (but are not limited to):
- Hybrid architectures with combinatorial building blocks
- Attacking hard combinatorial problems with learning
- Neural architectures mimicking combinatorial algorithms
Further information about speakers, paper submissions and schedule are available at the workshop website: https://sites.google.com/view/lmca2020/home .
Schedule
Sat 3:00 a.m. - 4:30 a.m.
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Poster Session A: 3:00 AM - 4:30 AM PST
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Poster Session
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link
SlidesLive Video |
16 presentersTaras Khakhulin · Ravichandra Addanki · Jinhwi Lee · Jungtaek Kim · Piotr Januszewski · Konrad Czechowski · Francesco Landolfi · Lovro Vrček · Oren Neumann · Claudius Gros · Betty Fabre · Lukas Faber · Lucas Anquetil · Alberto Franzin · Tommaso Bendinelli · Sergey Bartunov |
Sat 6:50 a.m. - 7:00 a.m.
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Opening
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Introduction
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Marin Vlastelica Pogančić · Georg Martius 🔗 |
Sat 7:00 a.m. - 7:25 a.m.
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Invited Talk (Ellen Vitercik)
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Talk
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SlidesLive Video |
Ellen Vitercik 🔗 |
Sat 7:25 a.m. - 7:50 a.m.
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Invited Talk (Petar Veličković)
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Talk
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SlidesLive Video |
Petar Veličković 🔗 |
Sat 7:50 a.m. - 8:10 a.m.
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Q&A for Session
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Q&A and Discussions
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Sat 8:10 a.m. - 8:18 a.m.
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Contributed Talk: A Framework For Differentiable Discovery Of Graph Algorithms
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Contributed Talk
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SlidesLive Video |
Hanjun Dai 🔗 |
Sat 8:18 a.m. - 8:26 a.m.
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Contributed Talk: Learning To Select Nodes In Bounded Suboptimal Conflict-Based Search For Multi-Agent Path Finding
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Contributed Talk
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SlidesLive Video |
Taoan Huang 🔗 |
Sat 8:26 a.m. - 8:35 a.m.
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Contributed Talk: Neural Algorithms For Graph Navigation
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Contributed Talk
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SlidesLive Video |
Aaron Zweig 🔗 |
Sat 8:35 a.m. - 8:44 a.m.
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Contributed Talk: Fit The Right Np-Hard Problem: End-To-End Learning Of Integer Programming Constraints
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Contributed Talk
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SlidesLive Video |
Anselm Paulus 🔗 |
Sat 8:44 a.m. - 8:52 a.m.
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Contributed Talk: Language Generation Via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
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Contributed Talk
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Nan Jiang 🔗 |
Sat 8:52 a.m. - 9:05 a.m.
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Q&A for Contributed Talks
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Q&A and Discussions
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Sat 9:05 a.m. - 9:10 a.m.
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Break
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Sat 9:10 a.m. - 10:40 a.m.
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Poster Session B
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Poster Session
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>
link
SlidesLive Video |
24 presentersRavichandra Addanki · Andreea-Ioana Deac · Yujia Xie · Francesco Landolfi · Antoine Prouvost · Claudius Gros · Renzo Massobrio · Abhishek Cauligi · Simon Alford · Hanjun Dai · Alberto Franzin · Nitish Kumar Panigrahy · Brandon Kates · Iddo Drori · Taoan Huang · Zhou Zhou · Marin Vlastelica · Anselm Paulus · Aaron Zweig · Minsu Cho · Haiyan Yin · Michal Lisicki · Nan Jiang · Haoran Sun |
Sat 10:40 a.m. - 11:10 a.m.
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Break
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Sat 11:10 a.m. - 11:35 a.m.
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Invited Talk (Zico Kolter)
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Talk
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SlidesLive Video |
J. Zico Kolter 🔗 |
Sat 11:35 a.m. - 12:00 p.m.
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Invited Talk (Katherine Bouman)
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Talk
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Katherine Bouman 🔗 |
Sat 12:00 p.m. - 12:25 p.m.
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Invited Talk (Michal Rolinek)
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Talk
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SlidesLive Video |
Michal Rolinek 🔗 |
Sat 12:25 p.m. - 12:55 p.m.
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Q&A for Session 2
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Q&A and Discussions
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Sat 12:55 p.m. - 1:25 p.m.
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Break
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Sat 1:25 p.m. - 1:50 p.m.
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Invited Talk (Armando Solar-Lezama)
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Talk
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SlidesLive Video |
Armando Solar-Lezama 🔗 |
Sat 1:50 p.m. - 2:15 p.m.
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Invited Talk (Kevin Ellis)
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Talk
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SlidesLive Video |
Kevin Ellis 🔗 |
Sat 2:15 p.m. - 2:40 p.m.
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Invited Talk (Yuandong Tian)
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Talk
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SlidesLive Video |
Yuandong Tian 🔗 |
Sat 2:40 p.m. - 3:10 p.m.
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Q&A for Session 3
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Q&A and Discussions
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Sat 3:10 p.m. - 4:00 p.m.
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Guided Discussion and Closing
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Discussion
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Session A, Poster 2: Neural Large Neighborhood Search
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Poster
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SlidesLive Video |
Ravichandra Addanki 🔗 |
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Session B, Poster 3: Xlvin: Executed Latent Value Iteration Nets
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Poster
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SlidesLive Video |
Andreea-Ioana Deac 🔗 |
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Session B, Poster 4: Differentiable Top-k With Optimal Transport
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Poster
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SlidesLive Video |
Yujia Xie 🔗 |
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Session A, Poster 8: K-Plex Cover Pooling For Graph Neural Networks
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Poster
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SlidesLive Video |
Francesco Landolfi 🔗 |
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Session B, Poster 10: Ecole: A Gym-Like Library For Machine Learning In Combinatorial Optimization Solvers
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Poster
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SlidesLive Video |
Antoine Prouvost 🔗 |
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Session A, Poster 11: Investment Vs. Reward In A Competitive Knapsack Problem
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Poster
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SlidesLive Video |
Claudius Gros 🔗 |
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Session B, Poster 12: Virtual Savant: Learning For Optimization
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Poster
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SlidesLive Video |
Renzo Massobrio 🔗 |
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Session B, Poster 15: CoCo: Learning Strategies For Online Mixed-Integer Control
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Poster
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SlidesLive Video |
Abhishek Cauligi 🔗 |
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Session B, Poster 19: Dreaming With ARC
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Poster
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SlidesLive Video |
Simon Alford 🔗 |
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Session A, Poster 1: Learning Elimination Ordering For Tree Decomposition Problem
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Poster
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SlidesLive Video |
Taras Khakhulin 🔗 |
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Session B, Poster 2: Neural Large Neighborhood Search
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Poster
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SlidesLive Video |
Ravichandra Addanki 🔗 |
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Session B, Poster 20: A Framework For Differentiable Discovery Of Graph Algorithms
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Poster
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SlidesLive Video |
Hanjun Dai 🔗 |
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Session A, Poster 5: Fragment Relation Networks For Geometric Shape Assembly
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Poster
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SlidesLive Video |
Jinhwi Lee 🔗 |
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Session A, Poster 5: Fragment Relation Networks For Geometric Shape Assembly
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Poster
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Jungtaek Kim 🔗 |
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Session A, Poster 6: Structure And Randomness In Planning And Reinforcement Learning
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Poster
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SlidesLive Video |
Piotr Januszewski 🔗 |
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Session A, Poster 7: Trust, But Verify: Model-Based Exploration In Sparse Reward Environments
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Poster
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SlidesLive Video |
Konrad Czechowski 🔗 |
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Session A, Poster 21: Towards Transferring Algorithm Configurations Across Problems
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Poster
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SlidesLive Video |
Alberto Franzin 🔗 |
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Session B, Poster 8: K-Plex Cover Pooling For Graph Neural Networks
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Poster
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SlidesLive Video |
Francesco Landolfi 🔗 |
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Session A, Poster 9: A Step Towards Neural Genome Assembly
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Poster
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SlidesLive Video |
Lovro Vrček 🔗 |
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Session A, Poster 11: Investment Vs. Reward In A Competitive Knapsack Problem
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Poster
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Oren Neumann 🔗 |
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Session B, Poster 22: Matching Through Embedding In Dense Graphs
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Poster
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SlidesLive Video |
Nitish Kumar Panigrahy 🔗 |
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Session B, Poster 11: Investment Vs. Reward In A Competitive Knapsack Problem
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Poster
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SlidesLive Video |
Claudius Gros 🔗 |
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Session A, Poster 13: Neural-Driven Multi-Criteria Tree Search For Paraphrase Generation
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Poster
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SlidesLive Video |
Betty Fabre 🔗 |
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Session B, Poster 23: Galaxytsp: A New Billion-Node Benchmark For Tsp ( Poster ) > link | Brandon Kates 🔗 |
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Session A, Poster 16: Learning Lower Bounds For Graph Exploration With Reinforcement Learning
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Poster
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SlidesLive Video |
Lukas Faber 🔗 |
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Session A, Poster 17: Wasserstein Learning Of Determinantal Point Processes
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Poster
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SlidesLive Video |
Lucas Anquetil 🔗 |
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Session B, Poster 21: Towards Transferring Algorithm Configurations Across Problems
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Poster
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SlidesLive Video |
Alberto Franzin 🔗 |
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Session A, Poster 27: A Seq2Seq Approach To Symbolic Regression
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Poster
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SlidesLive Video |
Tommaso Bendinelli 🔗 |
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Session B, Poster 23: Galaxytsp: A New Billion-Node Benchmark For TSP
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Poster
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SlidesLive Video |
Iddo Drori 🔗 |
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Session A, Poster 31: Continuous Latent Search For Combinatorial Optimization
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Poster
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Sergey Bartunov 🔗 |
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Session B, Poster 24: Learning To Select Nodes In Bounded Suboptimal Conflict-Based Search For Multi-Agent Path Finding
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Poster
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Taoan Huang 🔗 |
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Session B, Poster 25: Learning For Integer-Constrained Optimization Through Neural Networks With Limited Training
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Poster
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SlidesLive Video |
Zhou Zhou 🔗 |
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Session B, Poster 26: Discrete Planning With Neuro-Algorithmic Policies
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Poster
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SlidesLive Video |
Marin Vlastelica 🔗 |
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Session B, Poster 28: Fit The Right Np-Hard Problem: End-To-End Learning Of Integer Programming Constraints
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Poster
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SlidesLive Video |
Anselm Paulus 🔗 |
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Session B, Poster 29: Neural Algorithms For Graph Navigation
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Poster
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SlidesLive Video |
Aaron Zweig 🔗 |
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Session B, Poster 30: Differentiable Programming For Piecewise Polynomial Functions
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Poster
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SlidesLive Video |
Minsu Cho 🔗 |
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Session B, Poster 32: Reinforcement Learning With Efficient Active Feature Acquisition
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Poster
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SlidesLive Video |
Haiyan Yin 🔗 |
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Session B, Poster 33: Evaluating Curriculum Learning Strategies In Neural Combinatorial Optimization
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Poster
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SlidesLive Video |
Michal Lisicki 🔗 |
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Session B, Poster 34: Language Generation Via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
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Poster
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SlidesLive Video |
Nan Jiang 🔗 |
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Session B, Poster 18: Improving Learning To Branch Via Reinforcement Learning
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Poster
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SlidesLive Video |
Haoran Sun 🔗 |