Publications (95)
Data Minimization at Inference Time
Cuong Tran, Ferdinando Fioretto
In domains with high stakes such as law, recruitment, and healthcare, learning models frequently rely on sensitive user data for inference, necessitating the complete set of featur…
Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
Convex optimization finds many real-life applications, where--optimized on real data--optimization results may expose private data attributes (e.g., individual health records, comm…
Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects
Jingyi Cui, Jacob K. Christopher, Ankita Biswas +2
Point defects affect material properties by altering electronic states and modifying local bonding environments. However, high-throughput first-principles simulations of point defe…
Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions
My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian +1
Optimal Power Flow (OPF) is a fundamental problem in power systems. It is computationally challenging and a recent line of research has proposed the use of Deep Neural Networks (DN…
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…
On The Fairness Impacts of Hardware Selection in Machine Learning
Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran +2
In the machine learning ecosystem, hardware selection is often regarded as a mere utility, overshadowed by the spotlight on algorithms and data. This oversight is particularly prob…
Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation
Jacob K. Christopher, Michael Cardei, Jinhao Liang +1
Despite the remarkable generative capabilities of diffusion models, their integration into safety-critical or scientifically rigorous applications remains hindered by the need to e…
Privacy and Bias Analysis of Disclosure Avoidance Systems
Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck +2
Disclosure avoidance (DA) systems are used to safeguard the confidentiality of data while allowing it to be analyzed and disseminated for analytic purposes. These methods, e.g., ce…
Privacy-Preserving Obfuscation for Distributed Power Systems
Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck
This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF…
Constrained Code Generation with Discrete Diffusion
Lize Shao, Michael Cardei, Zichen Xie +2
Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corrupted token sequences and ena…
Fairness Increases Adversarial Vulnerability
Cuong Tran, Keyu Zhu, Ferdinando Fioretto +1
The remarkable performance of deep learning models and their applications in consequential domains (e.g., facial recognition) introduces important challenges at the intersection of…
Beyond Jailbreaking: Auditing Contextual Privacy in LLM Agents
Saswat Das, Jameson Sandler, Ferdinando Fioretto
LLM agents have begun to appear as personal assistants, customer service bots, and clinical aides. While these applications deliver substantial operational benefits, they also requ…
Gen-DFL: Decision-Focused Generative Learning for Robust Decision Making
Prince Zizhuang Wang, Shuyi Chen, Jinhao Liang +2
Decision-focused learning (DFL) integrates predictive models with downstream optimization, directly training machine learning models to minimize decision errors. While DFL has been…
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…
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 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…
Context-Aware Differential Privacy for Language Modeling
My H. Dinh, Ferdinando Fioretto
The remarkable ability of language models (LMs) has also brought challenges at the interface of AI and security. A critical challenge pertains to how much information these models…
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…
Differentially Empirical Risk Minimization under the Fairness Lens
Cuong Tran, My H. Dinh, Ferdinando Fioretto
Differential Privacy (DP) is an important privacy-enhancing technology for private machine learning systems. It allows to measure and bound the risk associated with an individual p…
Differentially Private Optimal Power Flow for Distribution Grids
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
Although distribution grid customers are obliged to share their consumption data with distribution system operators (DSOs), a possible leakage of this data is often disregarded in…
Constrained Diffusion for Protein Design with Hard Structural Constraints
Jacob K. Christopher, Austin Seamann, Jingyi Cui +2
Diffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches…
FairDP: Certified Fairness with Differential Privacy
Khang Tran, Ferdinando Fioretto, Issa Khalil +2
This paper introduces FairDP, a novel training mechanism designed to provide group fairness certification for the trained model's decisions, along with a differential privacy (DP)…
SoK: Data Minimization in Machine Learning
Robin Staab, Nikola JovanoviÄ, Kimberly Mai +4
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulati…
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…
Distributed Constraint Optimization Problems and Applications: A Survey
Ferdinando Fioretto, Enrico Pontelli, William Yeoh
The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applica…
Differentially Private Convex Optimization with Feasibility Guarantees
Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck +2
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.…
Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Shuyi Chen, Ferdinando Fioretto, Feng Qiu +1
Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services. Recently, predict-then-opti…
Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion
Vincenzo Di Vito, Mehdi Taghizadeh, Deepjyoti Deka +2
This paper proposes a novel learning-based approach to approximately solve instances of mixed-integer optimization problems. These problems are computationally challenging, as they…
NeuroFilter: Activation-Based Guardrails for Privacy-Conscious LLM Agents
Saswat Das, Ferdinando Fioretto
Agentic Large Language Models (LLMs) are models able to reason, plan, and execute tools over unstructured data. These abilities are enabling transformative applications in domains…
Differentiable Model Selection for Ensemble Learning
James Kotary, Vincenzo Di Vito, Ferdinando Fioretto
Model selection is a strategy aimed at creating accurate and robust models. A key challenge in designing these algorithms is identifying the optimal model for classifying any parti…
Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support
Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto
The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results. However, power system dynamics a…
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…
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method
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. It models the optimal schedu…
Optimal Allocation of Privacy Budget on Hierarchical Data Release
Joonhyuk Ko, Juba Ziani, Ferdinando Fioretto
Releasing useful information from datasets with hierarchical structures while preserving individual privacy presents a significant challenge. Standard privacy-preserving mechanisms…
Privacy-Preserving Obfuscation of Critical Infrastructure Networks
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 ca…
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…
Backpropagation of Unrolled Solvers with Folded Optimization
James Kotary, My H. Dinh, Ferdinando Fioretto
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…
OptStream: Releasing Time Series Privately
Ferdinando Fioretto, Pascal Van Hentenryck
Many applications of machine learning and optimization operate on data streams. While these datasets are fundamental to fuel decision-making algorithms, often they contain sensitiv…
Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
Jacob K. Christopher, James E. Warner, Ferdinando Fioretto
Deep generative models provide state-of-the-art performance across a wide array of applications, with recent studies showing increasing applicability for science and engineering. D…
SpecDiff-2: Scaling Diffusion Drafter Alignment For Faster Speculative Decoding
Jameson Sandler, Jacob K. Christopher, Thomas Hartvigsen +1
Speculative decoding has become the standard approach for accelerating Large Language Model (LLM) inference. It exploits a lossless draft-then-verify procedure to circumvent the la…
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
James Kotary, Vincenzo Di Vito, Jacob Christopher +2
Many real-world decision processes are modeled by optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize f…
Simple Self-Conditioning Adaptation for Masked Diffusion Models
Michael Cardei, Huu Binh Ta, Ferdinando Fioretto
Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process. In standard masked diffusion, if a token remains masked after…
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…
Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
Jinhao Liang, Yixuan Sun, Anirban Samaddar +2
Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specificat…
Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning
Jinhao Liang, Sven Koenig, Ferdinando Fioretto
Multi-Robot Motion Planning (MRMP) involves generating collision-free trajectories for multiple robots operating in a shared continuous workspace. While discrete multi-agent path f…
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…
Bias and Variance of Post-processing in Differential Privacy
Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto
Post-processing immunity is a fundamental property of differential privacy: it enables the application of arbitrary data-independent transformations to the results of differentiall…
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
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. It is nonlinear and nonconvex and computes the generator setp…
Post-processing of Differentially Private Data: A Fairness Perspective
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 affectin…
The Disparate Impacts of Speculative Decoding
Jameson Sandler, Ahmet Ãstün, Marco Romanelli +2
The practice of speculative decoding, whereby inference is probabilistically supported by a smaller, cheaper, ``drafter'' model, has become a standard technique for systematically…
Stability-Constrained AC Optimal Power Flow--A Gaussian Process-Based Approach
Vincenzo Di Vito, Kaarthik Sundar, Ferdinando Fioretto +1
The Alternating Current Optimal Power Flow (ACOPF) problem is a core task in power system operations, aimed at determining cost-effective generation dispatch while satisfying physi…
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…
Training-Free Constrained Generation With Stable Diffusion Models
Stefano Zampini, Jacob K. Christopher, Luca Oneto +2
Stable diffusion models represent the state-of-the-art in data synthesis across diverse domains and hold transformative potential for applications in science and engineering, e.g.,…
Differential Privacy of Hierarchical Census Data: An Optimization Approach
Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu
This paper is motivated by applications of a Census Bureau interested in releasing aggregate socio-economic data about a large population without revealing sensitive information ab…
Deadwooding: Robust Global Pruning for Deep Neural Networks
Sawinder Kaur, Ferdinando Fioretto, Asif Salekin
The ability of Deep Neural Networks to approximate highly complex functions is key to their success. This benefit, however, comes at the expense of a large model size, which challe…
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…
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…
Price-Aware Deep Learning for Electricity Markets
Vladimir Dvorkin, Ferdinando Fioretto
While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction error…
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…
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
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. To ens…
Personalized Privacy Auditing and Optimization at Test Time
Cuong Tran, Ferdinando Fioretto
A number of learning models used in consequential domains, such as to assist in legal, banking, hiring, and healthcare decisions, make use of potentially sensitive users' informati…
Bilevel Optimization for Differentially Private Optimization in Energy Systems
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. This task raises significant challenges since random perturbat…
PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets
Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck
This paper introduces a differentially private mechanism to protect the information exchanged during the coordination of the sequential market-clearing of electricity and natural g…
End-to-End Constrained Optimization Learning: A Survey
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck +1
This paper surveys the recent attempts at leveraging machine learning to solve constrained optimization problems. It focuses on surveying the work on integrating combinatorial solv…
Drift Flow Matching
Chenrui Ma, Xi Xiao, Lin Zhao +3
Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference computation can improve gener…
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
Cuong Tran, Keyu Zhu, Ferdinando Fioretto +1
A critical concern in data-driven processes is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure no…
Solving DCOPs with Distributed Large Neighborhood Search
Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli +2
The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Neve…
Lagrangian Duality for Constrained Deep Learning
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence WK Mak +3
This paper explores the potential of Lagrangian duality for learning applications that feature complex constraints. Such constraints arise in many science and engineering domains,…
Differentially-Private Heat and Electricity Markets Coordination
Lesia Mitridati, Emma Romei, Gabriela Hug +1
Sector coordination between heat and electricity systems has been identified has an energy-efficient and cost-effective way to transition towards a more sustainable energy system.…
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs
Ferdinando Fioretto, Enrico Pontelli, William Yeoh +1
Discrete optimization is a central problem in artificial intelligence. The optimization of the aggregated cost of a network of cost functions arises in a variety of problems includ…
Learning to Solve Optimization Problems Constrained with Partial Differential Equations
Yusuf Guven, Vincenzo Di Vito, Ferdinando Fioretto
Partial differential equation (PDE)-constrained optimization arises in many scientific and engineering domains, such as energy systems, fluid dynamics and material design. In these…
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…
On the Fairness Impacts of Private Ensembles Models
Cuong Tran, Ferdinando Fioretto
The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models…
Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
Mason Nakamura, Abhinav Kumar, Saswat Das +5
Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a unique safety…
A Privacy-Preserving and Trustable Multi-agent Learning Framework
Anudit Nagar, Cuong Tran, Ferdinando Fioretto
Distributed multi-agent learning enables agents to cooperatively train a model without requiring to share their datasets. While this setting ensures some level of privacy, it has b…
Decision Making with Differential Privacy under a Fairness Lens
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. To conform…
Constrained Discrete Diffusion
Michael Cardei, Jacob K Christopher, Thomas Hartvigsen +2
Discrete diffusion models are a class of generative models that construct sequences by progressively denoising samples from a categorical noise distribution. Beyond their rapidly g…
Learning to Solve Constrained Bilevel Control Co-Design Problems
James Kotary, Himanshu Sharma, Ethan King +3
Learning to Optimize (L2O) is a subfield of machine learning (ML) in which ML models are trained to solve parametric optimization problems. The general goal is to learn a fast appr…
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak +1
The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while sa…
Pruning has a disparate impact on model accuracy
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim +1
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that prunin…
Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning
Jinhao Liang, Sven Koenig, Ferdinando Fioretto
Decentralized multi-robot motion planning requires each robot to generate collision-free trajectories from local observations, without global sensing or reliable communication. How…
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…
Differential Privacy for Stackelberg Games
Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck
This paper introduces a differentially private (DP) mechanism to protect the information exchanged during the coordination of sequential and interdependent markets. This coordinati…
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…
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…
Search-Augmented Masked Diffusion Models for Constrained Generation
Huu Binh Ta, Michael Cardei, Alvaro Velasquez +1
Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for struc…
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
William Kluegel, Muhammad Aamir Iqbal, Ferdinando Fioretto +2
The field of Distributed Constraint Optimization has gained momentum in recent years thanks to its ability to address various applications related to multi-agent cooperation. While…
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
Jinhao Liang, Jacob K Christopher, Sven Koenig +1
Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the envi…
Load Encoding for Learning AC-OPF
Terrence W. K. Mak, Ferdinando Fioretto, Pascal VanHentenryck
The AC Optimal Power Flow (AC-OPF) problem is a core building block in electrical transmission system. It seeks the most economical active and reactive generation dispatch to meet…
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
James Kotary, Jacob Christopher, My H Dinh +1
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…
Differential Privacy for Power Grid Obfuscation
Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck
The availability of high-fidelity energy networks brings significant value to academic and commercial research. However, such releases also raise fundamental concerns related to pr…
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
Jayanta Mandi, James Kotary, Senne Berden +4
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning (ML) and constrained optimization to enhance decision quality by training ML models in an e…
Learning Hard Optimization Problems: A Data Generation Perspective
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck
Optimization problems are ubiquitous in our societies and are present in almost every segment of the economy. Most of these optimization problems are NP-hard and computationally de…
A Fairness Analysis on Private Aggregation of Teacher Ensembles
Cuong Tran, My H. Dinh, Kyle Beiter +1
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework. It combines multiple learning models used as teachers for a student model th…
End-to-end Learning for Fair Ranking Systems
James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck +1
The learning-to-rank problem aims at ranking items to maximize exposure of those most relevant to a user query. A desirable property of such ranking systems is to guarantee some no…