Schedule

All discussion classes center around a required reading. The schedule also includes relevant papers not cited by those papers (since the Archeologist’s job is to uncover relevant papers cited by the required reading, and we don’t want to make their job too easy!) as well as some relevant papers by myself that I’m always happy to discuss during office hours or after class.

Foundations lectures

9/23
Introduction to class
Lecture
Slides
9/25
Introduction to graph neural networks
Lecture
Notes
9/30
Introduction to mathematical optimization: Integer programming
Lecture
Notes
10/2
Introduction to mathematical optimization: Constraint satisfaction programming
Lecture
Notes

Algorithmic reasoning with transformers and LLMs

10/7
In-Context Learning of Regression Algorithms
Discussion
  • Required reading: Akyürek, Ekin, et al. “What learning algorithm is in-context learning? investigations with linear models.” ICLR’23. [link]
  • Additional resources:
    • Talk by Jacob Andreas
    • Sanford, Clayton, et al. “Understanding transformer reasoning capabilities via graph algorithms.” NeurIPS’24. [link]
10/9
How Transformers Select Algorithms In-Context
Discussion
  • Required reading: Bai, Yu, et al. “Transformers as statisticians: Provable in-context learning with in-context algorithm selection.” NeurIPS’23. [link]
  • Additional resources:
10/14
Algorithmic Understanding of GenAI
Discussion
  • Required reading: Eberle, Oliver, et al. “Position: We Need An Algorithmic Understanding of Generative AI.” ICML’25. [link]
10/16
Auto-Designing Heuristics with LLMs
Discussion
  • Required reading: Liu, Fei, et al. “Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model.” ICML’24. [link]
  • Additional resources:
    • Streeter, Matthew, et al. “Combining multiple heuristics online.” AAAI’07. [link]

Algorithmic reasoning with graph neural networks

10/21
GNNs as CSP Heuristics
Discussion
  • Required reading: Tönshoff, Jan, et al. “One model, any CSP: graph neural networks as fast global search heuristics for constraint satisfaction.” IJCAI’23. [link]
  • Additional resources:
    • Horvitz, Eric, et al. “A Bayesian approach to tackling hard computational problems.” AAAI’01. [link]
10/23
Neural Algorithmic Reasoning
Discussion
  • Required reading: Numeroso, Danilo, Davide Bacciu, and Petar Veličković. “Dual algorithmic reasoning.” ICLR’23. [link]
10/28
Introduction to diffusion models
Lecture
Notes
10/30
Graph Diffusion for Combinatorial Optimization
Discussion
  • Required reading: Sun, Zhiqing, and Yiming Yang. “Difusco: Graph-based diffusion solvers for combinatorial optimization.” NeurIPS’23. [link]
11/4
No class

Mathematical optimization

11/6
LLMs for Optimization Modeling
Discussion
  • Required reading: AhmadiTeshnizi, Ali, et al. “OptiMUS-0.3: Using large language models to model and solve optimization problems at scale.” arXiv’25. [link]
  • Additional resources:
    • Lawless, Connor, et al. “LLMs for cold-start cutting plane separator configuration.” CPAIOR’25. [link]
11/11
Predict-and-Search for Integer Programming
Discussion
  • Required reading: Huang, Taoan, et al. “Contrastive predict-and-search for mixed integer linear programs.” ICML’24. [link]
  • Additional resources:
    • Balcan, Maria-Florina, et al. “Learning to branch: Generalization guarantees and limits of data-independent discretization.” Journal of the ACM 71.2 (2024): 1-73. [link]
11/13
Differentiable Integer Programming
Discussion
  • Required reading: Geng, Zijie, et al. “Differentiable integer linear programming.” ICLR’25. [link]
  • Additional resources:
    • Wang, Po-Wei, et al. “SATnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver.” ICML’19. [link]

Theoretical guarantees

11/18
Policy Gradients for Solution Samplers
Discussion
  • Required reading: Caramanis, Constantine, et al. “Optimizing solution-samplers for combinatorial problems: The landscape of policy-gradient method.” NeurIPS’23. [link]
11/20
Approximation Algorithms with Predictions
Discussion
  • Required reading: Antoniadis, Antonios, et al. “Approximation algorithms for combinatorial optimization with predictions.” ICLR’25. [link]
  • Aditional resources:
    • Shen, Judy Hanwen, Ellen Vitercik, and Anders Wikum. “Algorithms with calibrated machine learning predictions.” ICML’25. [link]
11/25
No class
11/27
No class

Final project

12/2
Guest lecture
12/4
Final project presentations