19 citations · 28 across the 19 of their papers we have counts for
19 papers
Learn2Zinc: Fine-tuning Small Language Models for Text-to-Model Translation in MiniZinc
Serdar Kadioglu, Karthik Uppuluri
Large language models excel at code generation for mainstream programming languages but struggle with rare, domain-specific languages such as MiniZinc, a constraint modeling langua…
Text2Model: Modeling Copilots for Text-to-Model Translation
Serdar Kadioglu, Karthik Uppuluri, Akash Singirikonda
There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance this line of research by intro…
BoolXLLM: LLM-Assisted Explainability for Boolean Models
Du Cheng, Serdar Kadioglu, Xin Wang
Interpretable machine learning aims to provide transparent models whose decision-making processes can be readily understood by humans. Recent advances in rule-based approaches, suc…
Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations
Koyena Pal, Serdar Kadioglu
Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These embeddings were shown to enab…
Gala: Global LLM Agents for Text-to-Model Translation
Junyang Cai, Serdar Kadioglu, Bistra Dilkina
Natural language descriptions of optimization or satisfaction problems are challenging to translate into correct MiniZinc models, as this process demands both logical reasoning and…
ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search
Alican Yilmaz, Junyang Cai, Serdar Kadioglu +1
Solving Mixed-Integer Programming (MIP) problems often requires substantial computational resources due to their combinatorial nature. Parallelization has emerged as a critical str…
FORGE: Foundational Optimization Representations from Graph Embeddings
Zohair Shafi, Serdar Kadioglu
Combinatorial optimization problems are ubiquitous in science and engineering. Still, learning-based approaches to accelerate combinatorial optimization often require solving a lar…
Text2Zinc: A Cross-Domain Dataset for Modeling Optimization and Satisfaction Problems in MiniZinc
Akash Singirikonda, Serdar Kadioglu, Karthik Uppuluri
There is growing interest in utilizing large language models (LLMs) as co-pilots for combinatorial optimization and constraint programming tasks across various problems. This paper…
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem
Junyang Cai, Serdar Kadioglu, Bistra Dilkina
Mixed-integer programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems. Recently, learning-based approaches have show…
Scalable iterative pruning of large language and vision models using block coordinate descent
Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz +4
Pruning neural networks, which involves removing a fraction of their weights, can often maintain high accuracy while significantly reducing model complexity, at least up to a certa…
Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth
Serdar Kadioglu, Filip Michalsky
The acceleration in the adoption of AI-based automated decision-making systems poses a challenge for evaluating the fairness of algorithmic decisions, especially in the absence of…
The Design and Organization of Educational Competitions with Anonymous and Real-Time Leaderboards in Academic and Industrial Settings
Serdar Kadıoğlu, Bernard Kleynhans
The goal of this paper is to share our experience in designing and organizing educational competitions with anonymous and (near) real-time leaderboards in both academic and industr…
Explainable AI using expressive Boolean formulas
Gili Rosenberg, J. Kyle Brubaker, Martin J. A. Schuetz +6
We propose and implement an interpretable machine learning classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit…
Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets
Xin Wang, Serdar Kadioglu
We introduce a pattern mining framework that operates on semi-structured datasets and exploits the dichotomy between outcomes. Our approach takes advantage of constraint reasoning…
Active Learning Meets Optimized Item Selection
Bernard Kleynhans, Xin Wang, Serdar Kadıoğlu
Designing recommendation systems with limited or no available training data remains a challenge. To that end, a new combinatorial optimization problem is formulated to generate opt…
Integrated Vehicle Routing and Monte Carlo Scheduling Approach for the Home Service Assignment, Routing, and Scheduling Problem
Shamay G. Samuel, Enrique Areyan Viqueira, Serdar Kadioglu
We formulate and solve the H-SARA Problem, a Vehicle Routing and Appointment Scheduling Problem motivated by home services management. We assume that travel times, service duration…
Column Generation for Interaction Coverage in Combinatorial Software Testing
Serdar Kadioglu
This paper proposes a novel column generation framework for combinatorial software testing. In particular, it combines Mathematical Programming and Constraint Programming in a hybr…
Transformation-based Feature Computation for Algorithm Portfolios
Barry Hurley, Serdar Kadioglu, Yuri Malitsky +1
Instance-specific algorithm configuration and algorithm portfolios have been shown to offer significant improvements over single algorithm approaches in a variety of application do…
DASH: Dynamic Approach for Switching Heuristics
Giovanni Di Liberto, Serdar Kadioglu, Kevin Leo +1
Complete tree search is a highly effective method for tackling MIP problems, and over the years, a plethora of branching heuristics have been introduced to further refine the techn…