Deep Learning and Combinatorial Optimization (Schedule)

Deep Learning and Combinatorial Optimization

February 22 - 25, 2021

Schedule

All times in this Schedule are Pacific Time (PT)

Monday, February 22, 2021
8:00 - 8:25	Oktay Gunluk (Cornell University) Fair and Interpretable Decision Rules for Binary Classification
8:35 - 9:00	Ron Kimmel (Technion - Israel Institute of Technology) Learning Geometry
9:25 - 9:50	Sanjeeb Dash (IBM Watson Research Center) A method to learn compact logical rules for knowledge graph reasoning
10:00 - 10:25	Xavier Bresson (Nanyang Technological University, Singapore) The Transformer Network for the Traveling Salesman Problem
10:50 - 11:15	Kyle Cranmer (New York University) Quarks, hierarchical clustering, and combinatorial optimization
11:25 - 11:50	Azalia Mirhoseini (Google AI) Learning to Solve Combinatorial Optimization Problems in Systems and Chip Design
Tuesday, February 23, 2021
8:00 - 8:25	Sebastian Pokutta (Konrad-Zuse-Zentrum für Informationstechnik (ZIB)) Structured ML Training via Conditional Gradients
8:35 - 9:00	Thiago Serra (Bucknell University) Scaling Up Exact Neural Network Compression by ReLU Stability
9:25 - 9:50	Yunhao Tang (Columbia University) Reinforcement Learning for Integer Programming: Learning to Cut
10:00 - 10:25	Joseph Huchette (Rice University) Neural network verification as piecewise linear optimization
10:50 - 11:15	Emma Frejinger (University of Montreal) Can ML Help in Solving Cargo Capacity Management Booking Control Problems?
11:25 - 11:50	Le Song (Georgia Institute of Technology) A Framework For Differentiable Discovery Of Graph Algorithms
12:00 - 12:25	Petar Veličković (DeepMind Technologies) Reasoning on Natural Inputs
Wednesday, February 24, 2021
8:00 - 8:25	Vinod Nair (DeepMind Technologies) Solving Mixed Integer Programs Using Neural Networks
8:35 - 9:00	Joan Bruna (New York University) On Sparse Linear Programming and (simple) neural networks
9:25 - 9:50	Laurent Charlin (HEC Montréal) Exact Combinatorial Optimization with Graph Convolutional Neural Networks
10:00 - 10:25	Santanu Dey (Georgia Institute of Technology) Solving SDPs by using sparse PCA
10:50 - 11:15	Maxime Gasse (École Polytechnique de Montréal) Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
11:25 - 11:50	Wouter Kool (University of Amsterdam) Deep Learning for Combinatorial Optimization: count your flops and make your flops count!
12:00 - 12:25	Ellen Vitercik (Carnegie Mellon University) How much data is sufficient to learn high-performing algorithms?
Thursday, February 25, 2021
8:00 - 8:25	Zico Kolter (Carnegie Mellon University) Fast semidefinite programming for (differentiable) combinatorial optimization
8:35 - 9:00	Stefano Gualandi (Università di Pavia) Discrete Optimal Transport by Parallel Network Simplex
9:25 - 9:50	Bistra Dilkina (University of Southern California (USC)) Decision-focused learning: integrating downstream combinatorics in ML
10:00 - 10:25	Stefanie Jegelka (Massachusetts Institute of Technology) Task structure and generalization in graph neural networks
10:50 - 11:15	Louis-Martin Rousseau (École Polytechnique de Montréal) Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
11:25 - 11:50	Tina Eliassi-Rad (Northeastern University) Task-driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces
12:00 - 12:25	Sanjeev Arora (Princeton University) How to allow deep learning on your data without revealing your data

Deep Learning and Combinatorial Optimization

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Monday, February 22, 2021

Tuesday, February 23, 2021

Wednesday, February 24, 2021

Thursday, February 25, 2021

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Deep Learning and Combinatorial Optimization

Schedule

Monday, February 22, 2021

Tuesday, February 23, 2021

Wednesday, February 24, 2021

Thursday, February 25, 2021

Programs

News & Research

People

About IPAM