ML4CP

09:00 - 10:30

Invited talk

Welcome and Intro to Constraint Programming

Tias Guns

KU Leuven

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Tias Guns

KU Leuven

10/07/2023
VHI 00.10 Aula Gaston Eyckens

Welcome and Intro to Constraint Programming

Prof. Guns will welcome all to the summer school with a general motivation of why machine learning is increasingly used with constraint solving. This will be followed by an overview of the interactions between constraint solving and machine learning and how the program of the summer school highlights many of these interactions. The talk will then review the basic principles of constraint programming: from modeling problems using decision variables, constraints, and the use of global constraints, to different solver technologies and the translation to them. Real world examples will be shown of modeling, solving and visualising problems with the Python-based CPMpy modelling library.

10:30 - 11:00

Coffee break

11:00 - 12:30

Invited talk

Introduction to Machine Learning

Hendrik Blockeel

KU Leuven

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12:30 - 14:00

Lunch break

14:00 - 15:30

Lab Session

Perception-based solving with CPMpy

Tias Guns

KU Leuven

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Tias Guns

KU Leuven

10/07/2023
VHI 01.40 Collaborative Room

Perception-based solving with CPMpy

In this lab session you will get to play with the data from the visual Sudoku Assistant https://sudoku-assistant.cs.kuleuven.be. Given a (well-centered) image of a sudoku grid, you will train neural networks to predict the digits, you will develop a constraint model for sudoku solving, and you will link the prediction (probabilities) to the solver to get a CP-based joint inference method that can do better than simply solving for the predicted values. The lab session will be interactive, with open ended questions that require experimenting with the provided Jupyter Python Notebooks, as well as an open part at the end of things that can be further improved (different options requiring different skill levels).

15:30 - 16:00

Coffee break

16:00 - 17:30

Invited talk

End-to-end Constrained Optimization Learning

Ferdinando Fioretto

Syracuse University

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Ferdinando Fioretto

Syracuse University

10/07/2023
VHI 00.10 Aula Gaston Eyckens

End-to-end Constrained Optimization Learning

In recent years, the integration of Machine Learning (ML) with challenging scientific and engineering problems has experienced remarkable growth. In particular, deep learning has proven to be an effective solution for unconstrained problem settings, but it has struggled to perform as well in domains where hard constraints, domain knowledge, or physical principles need to be taken into account. In areas such as power systems, materials science, and fluid dynamics, the data follows well-established physical laws, and ignoring these laws can result in unreliable and ineffective solutions. In this talk, we will delve into the need for constraint-aware ML. We will present how to integrate key constrained optimization principles within the training process of deep learning models, endowing them with the capability of handling hard constraints and physical principles. The resulting models will bring a new level of accuracy and efficiency to hard decision tasks, which will be showcased on energy and scheduling problems. We will then introduce a powerful integration of constrained optimization as neural network layers, resulting in ML models that are able to enforce structure in the outputs of learned embeddings. This integration will provide ML models with enhanced expressiveness and modeling ability, which will be showcased through the certification fairness in learning to rank tasks and the assembly of high-quality ensemble models. Finally, we will discuss a number of grand challenges that I plan to address to develop a potentially transformative technology for both optimization and machine learning.

Machine Learning for Constraint Programming

ACP Summer School 2023

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on Machine Learning for Constraint Programming

Schedule

09:00 - 10:30

Welcome and Intro to Constraint Programming

Tias Guns

Show details

Tias Guns

Welcome and Intro to Constraint Programming

10:30 - 11:00

Coffee break

11:00 - 12:30

Introduction to Machine Learning

Hendrik Blockeel

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Hendrik Blockeel

Introduction to Machine Learning

12:30 - 14:00

Lunch break

14:00 - 15:30

Perception-based solving with CPMpy

Tias Guns

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Tias Guns

Perception-based solving with CPMpy

15:30 - 16:00

Coffee break

16:00 - 17:30

End-to-end Constrained Optimization Learning

Ferdinando Fioretto

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Ferdinando Fioretto

End-to-end Constrained Optimization Learning

09:00 - 10:30

Getting the Best out of your Constraint Solver: Portfolios and Automated Tuning

Lars Kotthoff

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Lars Kotthoff

Getting the Best out of your Constraint Solver: Portfolios and Automated Tuning

10:30 - 11:00

Coffee break

11:00 - 12:30

Lab session on Algorithm Portfolios and Automated Tuning

Lars Kotthoff

Details TBA

12:30 - 14:00

Lunch break

14:00 - 15:30

TBA

Elias Khalil

Details TBA

15:30 - 16:00

Coffee break

16:00 - 17:30

Lab session on decision-focussed learning

Elias Khalil

Details TBA

09:00 - 10:30

Deep Learning & Combinatorial Optimization

Wouter Kool

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Wouter Kool

Deep Learning & Combinatorial Optimization

10:30 - 11:00

Coffee break

11:00 - 12:30

Learning to Improve a Hybrid Genetic Search Algorithm

Wouter Kool

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Wouter Kool

Learning to Improve a Hybrid Genetic Search Algorithm

12:30 - 14:00

Lunch break

14:00 - 15:30

Rambling Away from Decision-Focused Learning

Michele Lombardi

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Michele Lombardi