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Found 41 papers, 12 papers with code
Multi-task Representation Learning for Mixed Integer Linear Programming
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).
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem
Mixed-Integer Programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems.
RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
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.
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods
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.
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search
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.
Application-Driven Innovation in Machine Learning
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.
Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning
Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method.
Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet
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.
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
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.
Learning Lagrangian Multipliers for the Travelling Salesman Problem
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).
GenCO: Generating Diverse Designs with Combinatorial Constraints
Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images.
Moccasin: Efficient Tensor Rematerialization for Neural Networks
The deployment and training of neural networks on edge computing devices pose many challenges.
Artificial Intelligence/Operations Research Workshop 2 Report Out
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.
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems.
Local Branching Relaxation Heuristics for Integer Linear Programs
LNS relies on heuristics to select neighborhoods to search in.
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems
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.
Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework
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.
Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling
We introduce a novel decomposed GP regression to incorporate the subgroup decomposed feedback.
Learning Pseudo-Backdoors for Mixed Integer Programs
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.
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications.
Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search
In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work.
Learning to Select Nodes in Bounded Suboptimal Conflict-Based Search for Multi-Agent Path Finding
Multi-Agent Path Finding is an NP-hard problem that is difficult for current approaches to solve optimally.
Video Game Level Repair via Mixed Integer Linear Programming
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples.
Model Generalization in Deep Learning Applications for Land Cover Mapping
Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery.
A General Large Neighborhood Search Framework for Solving Integer Linear Programs
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.
MIPaaL: Mixed Integer Program as a Layer
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.
Human-Machine Collaboration for Fast Land Cover Mapping
This bi-directional feedback loop allows humans to learn how the model responds to new data.
Evolution of Hierarchical Structure & Reuse in iGEM Synthetic DNA Sequences
Contrary to Evo-Lexis, in iGEM the amount of reuse decreases during the timeline of the dataset.
End to end learning and optimization on graphs
However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization.
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
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.
End-to-End Game-Focused Learning of Adversary Behavior in Security Games
Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary.
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications.
Combinatorial Attacks on Binarized Neural Networks
We propose a Mixed Integer Linear Programming (MILP) formulation of the problem.
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
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.
Emergence and Evolution of Hierarchical Structure in Complex Systems
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.
Hawkes Processes for Invasive Species Modeling and Management
The spread of invasive species to new areas threatens the stability of ecosystems and causes major economic losses in agriculture and forestry.
A Machine Learning Approach to Modeling Human Migration
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.
A Deep Learning Approach for Population Estimation from Satellite Imagery
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.
Learning Combinatorial Optimization Algorithms over Graphs
The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.
δ-MAPS: From spatio-temporal data to a weighted and lagged network between functional domains
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
Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data
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.
