Search Results for author: Pascal Van Hentenryck

Found 50 papers, 4 papers with code

Learning Regionally Decentralized AC Optimal Power Flows with ADMM

no code implementations8 May 2022 Terrence W. K. Mak, Minas Chatzos, Mathieu Tanneau, Pascal Van Hentenryck

One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e. g., wind/solar), dispatchable devices (e. g., coal/gas/nuclear generations), demand response, battery & storage facilities, and topology optimization.

SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles

no code implementations11 Apr 2022 Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck

A key characteristic of the proposed model is to enable the adoption of off-the-selves and non-private fair models to create a privacy-preserving and fair model.


Risk-Aware Control and Optimization for High-Renewable Power Grids

no code implementations2 Apr 2022 Neil Barry, Minas Chatzos, Wenbo Chen, Dahye Han, Chaofan Huang, Roshan Joseph, Michael Klamkin, Seonho Park, Mathieu Tanneau, Pascal Van Hentenryck, Shangkun Wang, Hanyu Zhang, Haoruo Zhao

The transition of the electrical power grid from fossil fuels to renewable sources of energy raises fundamental challenges to the market-clearing algorithms that drive its operations.

Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey

no code implementations16 Feb 2022 Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu

This paper surveys recent work in the intersection of differential privacy (DP) and fairness.


Post-processing of Differentially Private Data: A Fairness Perspective

no code implementations24 Jan 2022 Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck

Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees.


Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatch

no code implementations27 Dec 2021 Wenbo Chen, Seonho Park, Mathieu Tanneau, Pascal Van Hentenryck

Motivated by a principled analysis of the market-clearing optimizations of MISO, the paper proposes a novel ML pipeline that addresses the main challenges of learning SCED solutions, i. e., the variability in load, renewable output and production costs, as well as the combinatorial structure of commitment decisions.

End-to-end Learning for Fair Ranking Systems

no code implementations21 Nov 2021 James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu

The end-to-end SPOFR framework includes a constrained optimization sub-model and produces ranking policies that are guaranteed to satisfy fairness constraints while allowing for fine control of the fairness-utility tradeoff.

Fairness Learning-To-Rank

Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions

no code implementations5 Nov 2021 Enpeng Yuan, Pascal Van Hentenryck

The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization.

Data-Driven Time Series Reconstruction for Modern Power Systems Research

no code implementations26 Oct 2021 Minas Chatzos, Mathieu Tanneau, Pascal Van Hentenryck

A critical aspect of power systems research is the availability of suitable data, access to which is limited by privacy concerns and the sensitive nature of energy infrastructure.

Time Series

Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method

no code implementations12 Oct 2021 James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck

The Jobs shop Scheduling Problem (JSP) is a canonical combinatorial optimization problem that is routinely solved for a variety of industrial purposes.

Combinatorial Optimization

Public Transit for Special Events: Ridership Prediction and Train Optimization

no code implementations9 Jun 2021 Tejas Santanam, Anthony Trasatti, Pascal Van Hentenryck, Hanyu Zhang

This paper proposes a suite of data-driven techniques that exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events.

Learning Hard Optimization Problems: A Data Generation Perspective

no code implementations NeurIPS 2021 James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck

Optimization problems are ubiquitous in our societies and are present in almost every segment of the economy.

Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization

no code implementations27 May 2021 Enpeng Yuan, Pascal Van Hentenryck

The MPC optimization operates over a longer time horizon to compensate for the inherent myopic nature of the real-time dispatching.

Decision Making with Differential Privacy under a Fairness Lens

no code implementations16 May 2021 Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck

Agencies, such as the U. S. Census Bureau, release data sets and statistics about groups of individuals that are used as input to a number of critical decision processes.

Decision Making Fairness

End-to-End Constrained Optimization Learning: A Survey

no code implementations30 Mar 2021 James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder

This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems.

Ridesharing and Fleet Sizing For On-Demand Multimodal Transit Systems

no code implementations26 Jan 2021 Ramon Auad, Pascal Van Hentenryck

The results demonstrate the substantial potential of ridesharing for ODMTS, as costs are reduced by about 26% with respect to allowing only individual shuttle rides, at the expense of a minimal increase in transit times.

Optimization and Control

Spatial Network Decomposition for Fast and Scalable AC-OPF Learning

no code implementations17 Jan 2021 Minas Chatzos, Terrence W. K. Mak, Pascal Van Hentenryck

This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training.

Capturing Travel Mode Adoption in Designing On-demand Multimodal Transit Systems

no code implementations4 Jan 2021 Beste Basciftci, Pascal Van Hentenryck

Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude.

Bilevel Optimization Optimization and Control

Commuting with Autonomous Vehicles: A Branch and Cut Algorithm with Redundant Modeling

no code implementations4 Jan 2021 Mohd. Hafiz Hasan, Pascal Van Hentenryck

To remedy this limitation, the mini-route MIP is complemented by a DARP formulation which is not as effective in obtaining primal solutions but has a stronger relaxation.

Autonomous Vehicles Optimization and Control

Bias and Variance of Post-processing in Differential Privacy

no code implementations9 Oct 2020 Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto

Moreover, when the feasible region is convex, a widely adopted class of post-processing steps is also guaranteed to improve accuracy.

Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach

no code implementations26 Sep 2020 Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck

A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age.

Decision Making Fairness

The Benefits of Autonomous Vehicles for Community-Based Trip Sharing

no code implementations28 Aug 2020 Mohd. Hafiz Hasan, Pascal Van Hentenryck

Prior work motivated by reducing parking pressure and congestion in the city of Ann Arbor, Michigan, showed that a car-pooling platform for community-based trip sharing could reduce the number of vehicles by close to 60%.

Autonomous Vehicles

Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow

no code implementations14 Jul 2020 Alexandre Velloso, Pascal Van Hentenryck

The security-constrained optimal power flow (SCOPF) is fundamental in power systems and connects the automatic primary response (APR) of synchronized generators with the short-term schedule.

Differential Privacy of Hierarchical Census Data: An Optimization Approach

no code implementations28 Jun 2020 Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu

To address them, this paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.

Differentially Private Convex Optimization with Feasibility Guarantees

1 code implementation22 Jun 2020 Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Jalal Kazempour, Pierre Pinson

This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.

Electricity-Aware Bid Format for Heat Commitment and Dispatch

no code implementations6 May 2020 Lesia Mitridati, Pascal Van Hentenryck, Jalal Kazempour

Coordination between heat and electricity markets is essential to achieve a cost-effective and efficient operation of the overall energy system.

Real-Time Dispatching of Large-Scale Ride-Sharing Systems: Integrating Optimization, Machine Learning, and Model Predictive Control

no code implementations24 Mar 2020 Connor Riley, Pascal Van Hentenryck, Enpeng Yuan

This paper considers the dispatching of large-scale real-time ride-sharing systems to address congestion issues faced by many cities.

Bilevel Optimization for Differentially Private Optimization in Energy Systems

no code implementations26 Jan 2020 Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck

This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive.

Bilevel Optimization

Lagrangian Duality for Constrained Deep Learning

no code implementations26 Jan 2020 Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak, Cuong Tran, Federico Baldo, Michele Lombardi

In energy domains, the combination of Lagrangian duality and deep learning can be used to obtain state-of-the-art results to predict optimal power flows, in energy systems, and optimal compressor settings, in gas networks.


Distilling Black-Box Travel Mode Choice Model for Behavioral Interpretation

no code implementations30 Oct 2019 Xilei Zhao, Zhengze Zhou, Xiang Yan, Pascal Van Hentenryck

Furthermore, the paper provides a comprehensive comparison of student models with the benchmark model (decision tree) and the teacher model (gradient boosting trees) to quantify the fidelity and accuracy of the students' interpretations.

Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

no code implementations19 Sep 2019 Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems.

Privacy-Preserving Obfuscation of Critical Infrastructure Networks

no code implementations23 May 2019 Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck

The paper studies how to release data about a critical infrastructure network (e. g., the power network or a transportation network) without disclosing sensitive information that can be exploited by malevolent agents, while preserving the realism of the network.

The Commute Trip Sharing Problem

no code implementations24 Apr 2019 Mohd. Hafiz Hasan, Pascal Van Hentenryck, Antoine Legrain

In particular, the paper formalizes the Commute Trip Sharing Problem (CTSP) to find a routing plan that maximizes ride sharing for a set of commute trips.

Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach

no code implementations8 Feb 2019 Xilei Zhao, Xiang Yan, Pascal Van Hentenryck

The results on the case study show that the machine-learning classifier, together with model-agnostic interpretation tools, provides valuable insights on travel mode switching behavior for different individuals and population segments.

Decision Making Interpretable Machine Learning

Differential Privacy for Power Grid Obfuscation

1 code implementation21 Jan 2019 Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck

To address these concerns, this paper presents a novel differential privacy mechanism that guarantees AC-feasibility and largely preserves the fidelity of the obfuscated network.

Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models

no code implementations4 Nov 2018 Xilei Zhao, Xiang Yan, Alan Yu, Pascal Van Hentenryck

In other words, how to draw behavioral insights from the high-performance "black-box" machine-learning models remains largely unsolved in the field of travel behavior modeling.

OptStream: Releasing Time Series Privately

no code implementations6 Aug 2018 Ferdinando Fioretto, Pascal Van Hentenryck

Then, the perturbation module adds noise to the sampled data points to guarantee privacy.

Decision Making Load Forecasting +1

Constraint and Mathematical Programming Models for Integrated Port Container Terminal Operations

no code implementations14 Dec 2017 Damla Kizilay, Deniz T. Eliiyi, Pascal Van Hentenryck

This paper considers the integrated problem of quay crane assignment, quay crane scheduling, yard location assignment, and vehicle dispatching operations at a container terminal.

Graphical Models for Optimal Power Flow

no code implementations21 Jun 2016 Krishnamurthy Dvijotham, Pascal Van Hentenryck, Michael Chertkov, Sidhant Misra, Marc Vuffray

In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors.

Interdependent Scheduling Games

no code implementations31 May 2016 Andres Abeliuk, Haris Aziz, Gerardo Berbeglia, Serge Gaspers, Petr Kalina, Nicholas Mattei, Dominik Peters, Paul Stursberg, Pascal Van Hentenryck, Toby Walsh

We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently.

Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity

1 code implementation19 Feb 2016 Marian-Andrei Rizoiu, Lexing Xie, Scott Sanner, Manuel Cebrian, Honglin Yu, Pascal Van Hentenryck

Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption.

Social and Information Networks

Benders Decomposition for the Design of a Hub and Shuttle Public Transit System

no code implementations30 Dec 2015 Arthur Maheo, Philip Kilby, Pascal Van Hentenryck

The BusPlus project aims at improving the off-peak hours public transit service in Canberra, Australia.

A Constraint Programming Approach for Non-Preemptive Evacuation Scheduling

no code implementations11 May 2015 Caroline Even, Andreas Schutt, Pascal Van Hentenryck

Large-scale controlled evacuations require emergency services to select evacuation routes, decide departure times, and mobilize resources to issue orders, all under strict time constraints.

The QC Relaxation: Theoretical and Computational Results on Optimal Power Flow

1 code implementation27 Feb 2015 Carleton Coffrin, Hassan L. Hijazi, Pascal Van Hentenryck

Convex relaxations of the power flow equations and, in particular, the Semi-Definite Programming (SDP) and Second-Order Cone (SOC) relaxations, have attracted significant interest in recent years.

Computational Engineering, Finance, and Science Optimization and Control

Domain Views for Constraint Programming

no code implementations21 Jan 2014 Pascal Van Hentenryck, Laurent Michel

Traditional constraint-programming systems provide the concept of {\em variable views} which implement a view of the type $y = f(x)$ by delegating all (domain and constraint) operations on variable $y$ to variable $x$.

A Microkernel Architecture for Constraint Programming

no code implementations21 Jan 2014 Laurent Michel, Pascal Van Hentenryck

This paper presents a microkernel architecture for constraint programming organized around a number of small number of core functionalities and minimal interfaces.

Propagating Regular Counting Constraints

no code implementations27 Sep 2013 Nicolas Beldiceanu, Pierre Flener, Justin Pearson, Pascal Van Hentenryck

Constraints over finite sequences of variables are ubiquitous in sequencing and timetabling.

A Linear-Programming Approximation of AC Power Flows

no code implementations16 Jun 2012 Carleton Coffrin, Pascal Van Hentenryck

Linear active-power-only DC power flow approximations are pervasive in the planning and control of power systems.

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