118 citations · 200 across the 58 of their papers we have counts for
17 papers · 1 filter
Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models
Jinhao Liang, Jacob K. Christopher, Sven Koenig +1
Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics, requiring the computation of collision-free paths for multiple agents moving from their respective start to go…
Differential Privacy Overview and Fundamental Techniques
Ferdinando Fioretto, Pascal Van Hentenryck, Juba Ziani
This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It sta…
Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF
Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker +1
Recent developments in applying machine learning to address Alternating Current Optimal Power Flow (AC OPF) problems have demonstrated significant potential in providing close to o…
End-to-End Optimization and Learning of Fair Court Schedules
My H Dinh, James Kotary, Lauryn P. Gouldin +2
Criminal courts across the United States handle millions of cases every year, and the scheduling of those cases must accommodate a diverse set of constraints, including the prefere…
Learning To Solve Differential Equation Constrained Optimization Problems
Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker +1
Differential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology,…
Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Cristopher +2
The Predict-Then-Optimize framework uses machine learning models to predict unknown parameters of an optimization problem from exogenous features before solving. This setting is co…
Differentially Private Data Release on Graphs: Inefficiencies and Unfairness
Ferdinando Fioretto, Diptangshu Sen, Juba Ziani
Networks are crucial components of many sectors, including telecommunications, healthcare, finance, energy, and transportation.The information carried in such networks often contai…
Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes
Joonhyuk Ko, Juba Ziani, Saswat Das +2
Statistical agencies rely on sampling techniques to collect socio-demographic data crucial for policy-making and resource allocation. This paper shows that surveys of important soc…
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion
Jacob K Christopher, Brian R Bartoldson, Tal Ben-Nun +3
Speculative decoding has emerged as a widely adopted method to accelerate large language model inference without sacrificing the quality of the model outputs. While this technique…
The Data Minimization Principle in Machine Learning
Prakhar Ganesh, Cuong Tran, Reza Shokri +1
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. R…
Low-rank finetuning for LLMs: A fairness perspective
Saswat Das, Marco Romanelli, Cuong Tran +3
Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements. This pa…
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming
Ethan King, James Kotary, Ferdinando Fioretto +1
Recent work has shown a variety of ways in which machine learning can be used to accelerate the solution of constrained optimization problems. Increasing demand for real-time decis…
Learning Constrained Optimization with Deep Augmented Lagrangian Methods
James Kotary, Ferdinando Fioretto
Learning to Optimize (LtO) is a problem setting in which a machine learning (ML) model is trained to emulate a constrained optimization solver. Learning to produce optimal and feas…
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
My H Dinh, James Kotary, Ferdinando Fioretto
Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferre…
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
My H. Dinh, James Kotary, Ferdinando Fioretto
Learning to Rank (LTR) is one of the most widely used machine learning applications. It is a key component in platforms with profound societal impacts, including job search, health…
Disparate Impact on Group Accuracy of Linearization for Private Inference
Saswat Das, Marco Romanelli, Ferdinando Fioretto
Ensuring privacy-preserving inference on cryptographically secure data is a well-known computational challenge. To alleviate the bottleneck of costly cryptographic computations in…
Constrained Synthesis with Projected Diffusion Models
Jacob K Christopher, Stephen Baek, Ferdinando Fioretto
This paper introduces an approach to endow generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed metho…