Search Results for author: Bistra Dilkina

Found 32 papers, 10 papers with code

GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature

no code implementations3 Oct 2023 Aaron Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian

Generating diverse objects (e. g., images) using generative models (such as GAN or VAE) has achieved impressive results in the recent years, to help solve many design problems that are traditionally done by humans.

Combinatorial Optimization Image Generation

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information

1 code implementation18 Jul 2023 Arman Zharmagambetov, Brandon Amos, Aaron Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian

The implicit approach may not require optimal solutions as labels and is capable of handling problem uncertainty; however, it is slow to train and deploy due to frequent calls to optimizer $\mathbf{g}$ during both training and testing.

Portfolio Optimization

Moccasin: Efficient Tensor Rematerialization for Neural Networks

1 code implementation27 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.


Artificial Intelligence/Operations Research Workshop 2 Report Out

no code implementations10 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.


SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems

no code implementations22 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.

Combinatorial Optimization

Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework

no code implementations16 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.

Learning Pseudo-Backdoors for Mixed Integer Programs

no code implementations9 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.

Combinatorial Optimization

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

2 code implementations11 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.


Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search

no code implementations10 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.

Multi-Agent Path Finding

Video Game Level Repair via Mixed Integer Linear Programming

1 code implementation13 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.

Model Generalization in Deep Learning Applications for Land Cover Mapping

2 code implementations9 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.


A General Large Neighborhood Search Framework for Solving Integer Linear Programs

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.

Combinatorial Optimization

MIPaaL: Mixed Integer Program as a Layer

no code implementations12 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.

Decision Making

End to end learning and optimization on graphs

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.

Link Prediction

End-to-End Game-Focused Learning of Adversary Behavior in Security Games

no code implementations3 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.

Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

no code implementations5 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.

Combinatorial Attacks on Binarized Neural Networks

no code implementations ICLR 2019 Elias B. Khalil, Amrita Gupta, Bistra Dilkina

We propose a Mixed Integer Linear Programming (MILP) formulation of the problem.

Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization

no code implementations14 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.

Combinatorial Optimization

Emergence and Evolution of Hierarchical Structure in Complex Systems

1 code implementation13 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.

Hawkes Processes for Invasive Species Modeling and Management

no code implementations12 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.


A Machine Learning Approach to Modeling Human Migration

no code implementations15 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.

BIG-bench Machine Learning

A Deep Learning Approach for Population Estimation from Satellite Imagery

no code implementations30 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.

Decision Making Management

Learning Combinatorial Optimization Algorithms over Graphs

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.

Combinatorial Optimization Graph Embedding

δ-MAPS: From spatio-temporal data to a weighted and lagged network between functional domains

1 code implementation23 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

Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data

no code implementations17 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.

Text Compression

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