141 citations · 237 across the 46 of their papers we have counts for
50 papers
CoCoNet: Convex Conformal Network for Multi-Dimensional Uncertainty Regions
Alireza Moradi, Thomas Bruys, Pascal Van Hentenryck +1
Conformal prediction (CP) provides distribution-free coverage guarantees under exchangeability, but the efficiency of its prediction regions depends critically on the choice of the…
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…
Distributed Linear Programming on GPU Clusters at Extreme Scale
Arnaud Deza, Santanu Dey, Pascal Van Hentenryck
Large linear programs can exceed the memory of a single compute node. Although first-order methods replace sparse factorizations with GPU-suited matrix-vector products, other solve…
End-to-End Supply Chain Planning in the Paper Industry Via Column Generation and Benders Decomposition
Changkun Guan, Amira Hijazi, Pascal Van Hentenryck
Problem definition: The paper studies an integrated end-to-end planning problem in large-scale paper manufacturing, where production scheduling, trimming decisions, vehicle loading…
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…
The Proxy Benders Decomposition
Changkun Guan, El Mehdi Er Raqabi, Mathieu Tanneau +1
Benders decomposition is a fundamental framework for solving large-scale mixed-integer optimization problems with complicating variables that, when fixed, yield significantly easie…
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…
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
Tinghan Ye, Arnaud Deza, Ved Mohan +2
Optimization models developed by operations research (OR) experts are often deployed as decision-support systems in industrial settings. However, real-world environments are dynami…
A Hybrid Decomposition Approach for Stochastic Unit Commitment with Combined-Cycle Generators
Rosemary Barrass, Harsha Nagarajan, Mathieu Tanneau +2
The U.S. power grid is undergoing a paradigm shift as energy demand grows in scale and volatility. In response to this growing need, the U.S. has increased adoption of combined-cyc…
Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models
Miao Li, Hanyang Jiang, Sikai Cheng +6
Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches. However, current decoding s…
Towards AC Feasibility of DCOPF Dispatch
Michael A. Boateng, Russell Bent, Sidhant Misra +3
DC Optimal Power Flow (DCOPF) is widely utilized in power system operations due to its simplicity and computational efficiency. However, its lossless, reactive power-agnostic model…
Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction
Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1
Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations. As renewable penetration grows, reliable probabilistic forecasting is…
High-Resolution PTDF-Based Planning of Storage and Transmission Under High Renewables
Kevin Wu, Rabab Haider, Pascal Van Hentenryck
Transmission Expansion Planning (TEP) optimizes power grid upgrades and investments to ensure reliable, efficient, and cost-effective electricity delivery while addressing grid con…
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…
Paratransit Optimization with Constraint Programming: A Case Study in Savannah, Georgia
Liam Jagrowski, Kevin Dalmeijer, Tinghan Ye +1
Paratransit services are vital for individuals who cannot use fixed-route public transit, including those with disabilities. Optimizing these services is essential for transit agen…
Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease
Andrew G. Breithaupt, Michael Weiner, Alice Tang +14
United States healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias (ADRD). Generative AI bu…
Contextual Stochastic Optimization for School Desegregation Policymaking
Hongzhao Guan, Nabeel Gillani, Tyler Simko +2
Most US school districts draw geographic "attendance zones" to assign children to schools based on their home address, a process that can replicate existing neighborhood racial/eth…
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…
Compact Optimality Verification for Optimization Proxies
Wenbo Chen, Haoruo Zhao, Mathieu Tanneau +1
Recent years have witnessed increasing interest in optimization proxies, i.e., machine learning models that approximate the input-output mapping of parametric optimization problems…
Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
Rahul Nellikkath, Mathieu Tanneau, Pascal Van Hentenryck +1
Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems. Machine Learning (ML) algorithms, especially Neura…
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 Lagrangian Learning for Conic Optimization
Mathieu Tanneau, Pascal Van Hentenryck
This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies. DLL leverages conic duality and the representation power…
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…
Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification
Haoruo Zhao, Hassan Hijazi, Haydn Jones +3
Neural network verification aims at providing formal guarantees on the output of trained neural networks, to ensure their robustness against adversarial examples and enable their d…