Accepted Papers

  1. Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach by Maosen Zhang, Nan Jiang, Lei Li, and Yexiang Xue

  2. Improving Learning to Branch via Reinforcement Learning by Haoran Sun, Wenbo Chen, Hui Li, and Le Song

  3. Neural Algorithms for Graph Navigation by Aaron Zweig, Nesreen Ahmed, Theodore L. Willke, and Guixiang Ma

  4. Towards transferring algorithm configurations across problems by Alberto Franzin, and Thomas Stützle

  5. Trust, but verify: model-based exploration in sparse reward environments by Konrad Czechowski, Tomasz Odrzygóźdź, Michał Izworski, Marek Zbysiński, Łukasz Kuciński, and Piotr Miłoś

  6. Virtual Savant: learning for optimization by Renzo Massobrio, Sergio Nesmachnow, and Bernabé Dorronsoro

  7. Structure and randomness in planning and reinforcement learning by Piotr Kozakowski, Piotr Januszewski, Konrad Czechowski, Łukasz Kuciński, and Piotr Miłoś

  8. Neural-Driven Multi-criteria Tree Search for Paraphrase Generation by Betty Fabre, Tanguy Urvoy, Jonathan Chevelu, and Damien Lolive

  9. A step towards neural genome assembly by Lovro Vrček, Petar Veličković, and Mile Sikic

  10. Discrete Planning with Neuro-algorithmic Policies by Marin Vlastelica Pogančić, Michal Rolinek, and Georg Martius

  11. K-plex Cover Pooling for Graph Neural Networks by Davide Bacciu, Alessio Conte, Roberto Grossi, Francesco Landolfi, and Andrea Marino

  12. Neural Large Neighborhood Search by ravichandra addanki, Vinod Nair, and Mohammad Alizadeh

  13. Learning to Select Nodes in Bounded Suboptimal Conflict-Based Search for Multi-Agent Path Finding by Taoan Huang, Bistra Dilkina, and Sven Koenig

  14. Learning Lower Bounds for Graph Exploration With Reinforcement Learning by Jorel Elmiger, Lukas Faber, Pankaj Khanchandani, Oliver Paul Richter, and Roger Wattenhofer

  15. Fit The Right NP-Hard Problem: End-to-end Learning of Integer Programming Constraints by Anselm Paulus, Michal Rolinek, Vít Musil, Brandon Amos, and Georg Martius

  16. Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization by Michal Lisicki, Arash Afkanpour, and Graham W Taylor

  17. Reinforcement Learning with Efficient Active Feature Acquisition by Haiyan Yin, Yingzhen Li, Sinno Pan, Cheng Zhang, and Sebastian Tschiatschek

  18. Continuous Latent Search for Combinatorial Optimization by Sergey Bartunov, Vinod Nair, Peter Battaglia, and Timothy P Lillicrap

  19. Differentiable Programming for Piecewise Polynomial Functions by Minsu Cho, Ameya Joshi, Xian Yeow Lee, Aditya Balu, Adarsh Krishnamurthy, Baskar Ganapathysubramanian, Soumik Sarkar, and Chinmay Hegde

  20. A Seq2Seq approach to Symbolic Regression by Luca Biggio, Tommaso Bendinelli, and Giambattista Parascandolo

  21. Fragment Relation Networks for Geometric Shape Assembly by Jinhwi Lee, Jungtaek Kim, Hyunsoo Chung, Jaesik Park, and Minsu Cho

  22. Wasserstein Learning of Determinantal Point Processes by Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy, Ugo Tanielian, and Clément Calauzènes

  23. Learning for Integer-Constrained Optimization through Neural Networks with Limited Training by Zhou Zhou, Shashank Jere, Lizhong Zheng, and Lingjia Liu

  24. Investment vs. reward in a competitive knapsack problem by Oren Neumann, and Claudius Gros

  25. GalaxyTSP: A New Billion-Node Benchmark for TSP by Iddo Drori, Brandon J Kates, William R. Sickinger, Anant Girish Kharkar, Brenda Dietrich, Avi Shporer, and Madeleine Udell

  26. Matching through Embedding in Dense Graphs by Nitish K Panigrahy, Prithwish Basu, and Don Towsley

  27. A Framework For Differentiable Discovery Of Graph Algorithms by Hanjun Dai, Xinshi Chen, Yu Li, Xin Gao, and Le Song

  28. Dreaming with ARC by Andrzej Banburski, Anshula Gandhi, Simon Alford, Sylee Dandekar, Sang Chin, and tomaso a poggio

  29. CoCo: Learning Strategies for Online Mixed-Integer Control by Abhishek Cauligi, Preston Culbertson, Bartolomeo Stellato, Mac Schwager, and Marco Pavone

  30. Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers by Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, and Andrea Lodi

  31. Differentiable Top-k with Optimal Transport by Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, and Tomas Pfister

  32. XLVIN: eXecuted Latent Value Iteration Nets by Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, and Mladen Nikolić

  33. Learning Elimination Ordering for Tree Decomposition Problem by Taras Khakhulin, Roman Schutski, and Ivan Oseledets