141 citations · 237 across the 46 of their papers we have counts for
14 papers · 1 filter
Match the Canvas, Not Just the Answer: Predictability-Aware SFT for Diffusion Language Models
Miao Li, Hanyang Jiang, Hengyu Fu +8
Diffusion language models (DLMs) enable flexible parallel generation by resolving tokens according to the model's evolving predictability rather than a fixed left-to-right order. T…
Fusing Backdoors, Machine Learning, and Optimization for Large-Scale Parametric Mixed-Integer Programs
El Mehdi Er Raqabi, Pascal Van Hentenryck
Large-scale optimization problems are often solved repeatedly under similar structural conditions, leading to substantial computational overhead. This occurs in applications such a…
Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
Wyame Benslimane, Tinghan Ye, Pascal Van Hentenryck +1
Decision-focused learning (DFL) trains predictive models by optimizing downstream decision quality rather than standalone prediction accuracy. For contextual linear optimization, m…
CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing
Sikai Cheng, Reza Zandehshahvar, Haoruo Zhao +4
Channel state information (CSI) prediction is a promising strategy for ensuring reliable and efficient operation of massive multiple-input multiple-output (mMIMO) systems by provid…
Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data
Sheng Chai, Gus Chadney, Charlot Avery +3
Access to granular demand data is essential for the net zero transition; it allows for accurate profiling and active demand management as our reliance on variable renewable generat…
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality
Andrew Rosemberg, Alexandre Street, Davi M. Valladão +1
Constrained Markov Decision Processes (CMDPs) are critical in many high-stakes applications, where decisions must optimize cumulative rewards while strictly adhering to complex non…
Dual Interior Point Optimization Learning
Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck
In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of h…
Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flow
Seonho Park, Pascal Van Hentenryck
Security-Constrained Optimal Power Flow (SCOPF) plays a crucial role in power grid stability but becomes increasingly complex as systems grow. This paper introduces PDL-SCOPF, a se…
Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks
Andrew Rosemberg, Mathieu Tanneau, Bruno Fanzeres +2
The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints…
Dual Conic Proxies for AC Optimal Power Flow
Guancheng Qiu, Mathieu Tanneau, Pascal Van Hentenryck
In recent years, there has been significant interest in the development of machine learning-based optimization proxies for AC Optimal Power Flow (AC-OPF). Although significant prog…
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Seonho Park, Wenbo Chen, Dahye Han +2
Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased…
Self-Supervised Primal-Dual Learning for Constrained Optimization
Seonho Park, Pascal Van Hentenryck
This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. This is an empirical risk minimization…
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck +1
This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contras…
Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch
Wenbo Chen, Seonho Park, Mathieu Tanneau +1
The Security-Constrained Economic Dispatch (SCED) is a fundamental optimization model for Transmission System Operators (TSO) to clear real-time energy markets while ensuring relia…