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Aaron M Ferber

10 accepted papers

2026

Unsupervised Combinatorial Probabilistic Reasoning: Probabilistic Coin Change Problem

Zhongdi Qu, Yingheng Wang, Utku Umur Acikalin, Aaron M. Ferber, Goncalo J. Gouveia, Brandon Bills +6
AAAI 2026technical

We introduce the Probabilistic Coin Change Problem (PCCP), a novel variant of the classical Combination Coin Change Problem (CCCP), motivated by a real-world scientific inverse task. The goal of CCCP is to enumerate all unordered combinations of coin denominations that sum to a given target. In PCCP

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2025

Constraint-aware Pareto Optimization for Tree-Structured Networks: Addressing Decarbonization Targets with Hydropower Expansion

Marc Grimson, Zhongdi Qu, Yue Mao, Aaron M Ferber, Felipe Siqueira Pacheco, Sebastian Heilpern +3
AAAI 2025technical

Addressing global sustainability challenges as outlined by the United Nations (UN) Sustainable Development Goals (SDGs) often requires navigating many potentially conflicting societal objectives simultaneously. For instance, increasing hydropower production enhances renewable energy supply but may a…

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2025

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints

Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu +5
AISTATS 2025poster

Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scenarios where feasibility constraints are not given…

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2025

Learning to Explore and Exploit with GNNs for Unsupervised Combinatorial Optimization

Utku Umur ACIKALIN, Aaron M Ferber, Carla P Gomes
ICLR 2025poster

Combinatorial optimization (CO) problems are pervasive across various domains, but their NP-hard nature often necessitates problem-specific heuristic algorithms. Recent advancements in deep learning have led to the development of learning-based heuristics, yet these approaches often struggle with li…

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2024

Contrastive Predict-and-Search for Mixed Integer Linear Programs

Taoan Huang, Aaron M Ferber, Arman Zharmagambetov, Yuandong Tian, Bistra Dilkina
ICML 2024poster

Mixed integer linear programs (MILP) are flexible and powerful tools for modeling and solving many difficult real-world combinatorial optimization problems. In this paper, we propose a novel machine learning (ML)-based framework ConPaS that learns to predict solutions to MILPs with contrastive learn…

Cited by 5Source ↗PDF ↗Scholar ↗
2024

GenCO: Generating Diverse Designs with Combinatorial Constraints

Aaron M Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian
ICML 2024poster

Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images. However, many design settings arising in industrial design, material science, computer graphics and more require that the generated objects satisfy hard combinatorial constraints or…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

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

Arman Zharmagambetov, Brandon Amos, Aaron M Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian
NeurIPS 2023poster

Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer $\mathbf{g}$ to tackle these challenging problems with $f$ as t…

Cited by 18Source ↗PDF ↗code ↗Scholar ↗
2023

Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning

Taoan Huang, Aaron M Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
ICML 2023poster

Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems. Recently, it has been shown that Large Neighborhood Search (LNS), as a heuristic algorithm, can find high-quality solutions to ILPs faster than Branch and Bound. However,…

Cited by 40Source ↗PDF ↗Scholar ↗
2023

SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems

Aaron M Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina +1
ICML 2023poster

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. To bridge this gap, we propose $\textbf{\emph{\texttt{SurCo}}}$ that learns linear $\underline{\…

Cited by 32Source ↗PDF ↗Scholar ↗
2021

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

Umang Gupta, Aaron M Ferber, Bistra Dilkina, Greg Ver Steeg
AAAI 2021technical

Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the data so that it is statistically independent of group membership, but this may throw away too much information when a reas…

Cited by 59Source ↗PDF ↗code ↗Scholar ↗