Monday, February 22, 2021

Morning Session

07:55-07:55

SESSION CHAIR: Louis-Martin Rousseau (École Polytechnique de Montréal)

07:55-08:00

Welcome & Opening Remarks: Dean Miguel García-Garibay (Dean of Physical Sciences, UCLA) and Dima Shlyakhtenko (Director, IPAM)

08:00-08:25

Oktay Gunluk (Cornell University)

Fair and Interpretable Decision Rules for Binary Classification

08:35-09:00

Ron Kimmel (Technion - Israel Institute of Technology)

Learning Geometry

[PDF]

09:10-09:25

Break

09:25-09:50

Sanjeeb Dash (IBM Watson Research Center)

A method to learn compact logical rules for knowledge graph reasoning

10:00-10:25

Xavier Bresson (National University of Singapore)

The Transformer Network for the Traveling Salesman Problem

[PDF]

10:35-10:50

Break

10:50-11:15

Kyle Cranmer (University of Wisconsin-Madison)

Quarks, hierarchical clustering, and combinatorial optimization

[PDF]

11:25-11:50

Azalia Mirhoseini (Google AI)

Learning to Solve Combinatorial Optimization Problems in Systems and Chip Design

Tuesday, February 23, 2021

Morning Session

08:00-08:00

SESSION CHAIR: Wouter Kool (University of Amsterdam)

08:00-08:25

Sebastian Pokutta (Konrad-Zuse-Zentrum für Informationstechnik (ZIB))

Structured ML Training via Conditional Gradients

[PDF]

08:35-09:00

Thiago Serra (Bucknell University)

Scaling Up Exact Neural Network Compression by ReLU Stability

[PDF]

09:10-09:25

Break

09:25-09:50

Yunhao Tang (Columbia University)

Reinforcement Learning for Integer Programming: Learning to Cut

[PPT]

10:00-10:25

Joseph Huchette (Rice University)

Neural network verification as piecewise linear optimization

[PDF]

10:35-10:50

Break

10:50-11:15

Emma Frejinger (University of Montreal)

Can ML Help in Solving Cargo Capacity Management Booking Control Problems?

[PDF]

11:25-11:50

Le Song (Georgia Institute of Technology)

A Framework For Differentiable Discovery Of Graph Algorithms

[PDF]

Afternoon Session

12:00-12:25

Petar Veličković (DeepMind Technologies)

Reasoning on Natural Inputs

[PDF]

Wednesday, February 24, 2021

Morning Session

08:00-08:00

SESSION CHAIR: Kyle Cranmer (New York University)

08:00-08:25

Vinod Nair (Google Brain )

Solving Mixed Integer Programs Using Neural Networks

[PDF]

08:35-09:00

Joan Bruna (New York University)

On Sparse Linear Programming and (simple) neural networks

[PDF]

09:10-09:25

Break

09:25-09:50

Laurent Charlin (HEC Montréal)

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

[PDF]

10:00-10:25

Santanu Dey (Georgia Institute of Technology)

Solving SDPs by using sparse PCA

[PDF]

10:35-10:50

Break

10:50-11:15

Maxime Gasse (École Polytechnique de Montréal)

Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers

[PDF]

11:25-11:50

Wouter Kool (University of Amsterdam)

Deep Learning for Combinatorial Optimization: count your flops and make your flops count!

[PDF]

Afternoon Session

12:00-12:25

Ellen Vitercik (Stanford University)

How much data is sufficient to learn high-performing algorithms?

[PDF]

Thursday, February 25, 2021

Morning Session

08:00-08:00

SESSION CHAIR: Petar Veličković (DeepMind Technologies)

08:00-08:25

Zico Kolter (Carnegie Mellon University)

Fast semidefinite programming for (differentiable) combinatorial optimization

08:35-09:00

Stefano Gualandi (Università di Pavia)

Discrete Optimal Transport by Parallel Network Simplex

09:10-09:25

Break

09:25-09:50

Bistra Dilkina (University of Southern California (USC))

Decision-focused learning: integrating downstream combinatorics in ML

[PDF]

10:00-10:25

Stefanie Jegelka (Massachusetts Institute of Technology)

Task structure and generalization in graph neural networks

[PDF]

10:35-10:50

Break

10:50-11:15

Louis-Martin Rousseau (École Polytechnique de Montréal)

Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization

[PDF]

11:25-11:50

Tina Eliassi-Rad (Northeastern University)

Task-driven Network Discovery via Deep Reinforcement Learning on Embedded Spaces

[PDF]

Afternoon Session

12:00-12:25

Sanjeev Arora (Princeton University)

How to allow deep learning on your data without revealing your data

[PDF]