1 code implementation • 18 Dec 2024 • Junyang Cai, Taoan Huang, Bistra Dilkina
The proposed framework provides MILP embeddings helpful in guiding MILP solving across solvers (e. g., Gurobi and SCIP) and across tasks (e. g., Branching and Solver configuration).
no code implementations • 18 Dec 2024 • Junyang Cai, Serdar Kadioglu, Bistra Dilkina
Mixed-Integer Programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems.
no code implementations • 17 Jun 2024 • Weizhe Chen, Sven Koenig, Bistra Dilkina
In this past year, large language models (LLMs) have had remarkable success in domains outside the traditional natural language processing, and people are starting to explore the usage of LLMs in more general and close to application domains like code generation, travel planning, and robot controls.
2 code implementations • 11 Jun 2024 • Weimin Huang, Taoan Huang, Aaron M Ferber, Bistra Dilkina
We curate MILP distributions from existing work in this area as well as real-world problems that have not been used, and classify them into different hardness levels.
no code implementations • 3 Apr 2024 • Weizhe Chen, Sven Koenig, Bistra Dilkina
Cooperative multi-agent reinforcement learning (MARL) has been an increasingly important research topic in the last half-decade because of its great potential for real-world applications.
no code implementations • 26 Mar 2024 • David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.
no code implementations • 19 Jan 2024 • Junyang Cai, Taoan Huang, Bistra Dilkina
Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method.
no code implementations • 8 Jan 2024 • Weizhe Chen, Sven Koenig, Bistra Dilkina
With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation models can be used to solve a large variety of tasks.
1 code implementation • 28 Dec 2023 • Thomy Phan, Taoan Huang, Bistra Dilkina, Sven Koenig
State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i. e., the neighborhood, of the solution, using randomized destroy heuristics and prioritized planning.
no code implementations • 22 Dec 2023 • Augustin Parjadis, Quentin Cappart, Bistra Dilkina, Aaron Ferber, Louis-Martin Rousseau
Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality of feasible solutions and the design of efficient propagators in constraint programming (such as the weighted circuit constraint).
no code implementations • 3 Oct 2023 • Aaron Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian
Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images.
1 code implementation • 27 Apr 2023 • Burak Bartan, Haoming Li, Harris Teague, Christopher Lott, Bistra Dilkina
The deployment and training of neural networks on edge computing devices pose many challenges.
no code implementations • 10 Apr 2023 • John Dickerson, Bistra Dilkina, Yu Ding, Swati Gupta, Pascal Van Hentenryck, Sven Koenig, Ramayya Krishnan, Radhika Kulkarni, Catherine Gill, Haley Griffin, Maddy Hunter, Ann Schwartz
This workshop Report Out focuses on the foundational elements of trustworthy AI and OR technology, and how to ensure all AI and OR systems implement these elements in their system designs.
no code implementations • 3 Feb 2023 • Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems.
no code implementations • 15 Dec 2022 • Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
LNS relies on heuristics to select neighborhoods to search in.
no code implementations • 22 Oct 2022 • Aaron Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts.
no code implementations • 16 Oct 2021 • Elias B. Khalil, Pashootan Vaezipoor, Bistra Dilkina
In Mixed Integer Linear Programming (MIP), a (strong) backdoor is a "small" subset of an instance's integer variables with the following property: in a branch-and-bound procedure, the instance can be solved to global optimality by branching only on the variables in the backdoor.
no code implementations • 7 Jul 2021 • Kai Wang, Bryan Wilder, Sze-chuan Suen, Bistra Dilkina, Milind Tambe
We introduce a novel decomposed GP regression to incorporate the subgroup decomposed feedback.
no code implementations • 9 Jun 2021 • Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.
2 code implementations • 11 Jan 2021 • Umang Gupta, Aaron M Ferber, Bistra Dilkina, Greg Ver Steeg
Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications.
no code implementations • 10 Dec 2020 • Taoan Huang, Bistra Dilkina, Sven Koenig
In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work.
no code implementations • NeurIPS Workshop LMCA 2020 • Taoan Huang, Bistra Dilkina, Sven Koenig
Multi-Agent Path Finding is an NP-hard problem that is difficult for current approaches to solve optimally.
1 code implementation • 13 Oct 2020 • Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover, Julian Togelius, Bistra Dilkina, Stefanos Nikolaidis
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.
2 code implementations • 9 Aug 2020 • Lucas Hu, Caleb Robinson, Bistra Dilkina
Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery.
no code implementations • NeurIPS 2020 • Jialin Song, Ravi Lanka, Yisong Yue, Bistra Dilkina
This paper studies a strategy for data-driven algorithm design for large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways.
no code implementations • 12 Jul 2019 • Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe
It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization.
no code implementations • 10 Jun 2019 • Caleb Robinson, Anthony Ortiz, Kolya Malkin, Blake Elias, Andi Peng, Dan Morris, Bistra Dilkina, Nebojsa Jojic
This bi-directional feedback loop allows humans to learn how the model responds to new data.
no code implementations • 6 Jun 2019 • Payam Siyari, Bistra Dilkina, Constantine Dovrolis
Contrary to Evo-Lexis, in iGEM the amount of reuse decreases during the timeline of the dataset.
1 code implementation • NeurIPS 2019 • Bryan Wilder, Eric Ewing, Bistra Dilkina, Milind Tambe
However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization.
1 code implementation • 8 Mar 2019 • Lily Xu, Shahrzad Gholami, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Rohit Singh, Mustapha Nsubuga, Joshua Mabonga, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Tom Okello, Eric Enyel
We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia.
no code implementations • 3 Mar 2019 • Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, Milind Tambe
Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.
no code implementations • 5 Feb 2019 • Jackson A. Killian, Bryan Wilder, Amit Sharma, Daksha Shah, Vinod Choudhary, Bistra Dilkina, Milind Tambe
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.
no code implementations • ICLR 2019 • Elias B. Khalil, Amrita Gupta, Bistra Dilkina
We propose a Mixed Integer Linear Programming (MILP) formulation of the problem.
no code implementations • 14 Sep 2018 • Bryan Wilder, Bistra Dilkina, Milind Tambe
These components are typically approached separately: a machine learning model is first trained via a measure of predictive accuracy, and then its predictions are used as input into an optimization algorithm which produces a decision.
1 code implementation • 13 May 2018 • Payam Siyari, Bistra Dilkina, Constantine Dovrolis
It is well known that many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions.
no code implementations • 12 Dec 2017 • Amrita Gupta, Mehrdad Farajtabar, Bistra Dilkina, Hongyuan Zha
The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses in agriculture and forestry.
no code implementations • 15 Nov 2017 • Caleb Robinson, Bistra Dilkina
Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only.
no code implementations • 30 Aug 2017 • Caleb Robinson, Fred Hohman, Bistra Dilkina
We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs.
8 code implementations • NeurIPS 2017 • Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song
The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.
1 code implementation • 23 Feb 2016 • Ilias Fountalis, Annalisa Bracco, Bistra Dilkina, Constantine Dovrolis, Shella Keilholz
The proposed edge inference method examines the statistical significance of each lagged cross-correlation between two domains, infers a range of lag values for each edge, and assigns a weight to each edge based on the covariance of the two domains.
Other Computer Science
no code implementations • 17 Feb 2016 • Payam Siyari, Bistra Dilkina, Constantine Dovrolis
We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the "core" of a Lexis-DAG, which is important in the analysis of Lexis-DAGs.