publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2024

  1. Finding ε and δ of Traditional Disclosure Control Systems
    Ferdinando Fioretto, Keyu Zhu, Pascal Van Hentenryck, Saswat Das, and Christine Task
    2024
  2. ArXiv
    End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
    My H. Dinh, James Kotary, and Ferdinando Fioretto
    CoRR, 2024
  3. ArXiv
    Projected Generative Diffusion Models for Constraint Satisfaction
    Jacob K Christopher, Stephen Baek, and Ferdinando Fioretto
    CoRR, 2024
  4. ArXiv
    Disparate Impact on Group Accuracy of Linearization for Private Inference
    Saswat Das, Marco Romanelli, and Ferdinando Fioretto
    CoRR, 2024
  5. ArXiv
    Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
    My H. Dinh, James Kotary, and Ferdinando Fioretto
    CoRR, 2024
  6. ArXiv
    Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
    James Kotary, Jacob Christopher, My H Dinh, and Ferdinando Fioretto
    CoRR, 2024

2023

  1. Gradient-enhanced physics-informed neural networks for power systems operational support
    Mostafa Mohammadian, Kyri Baker, and Ferdinando Fioretto
    Electric Power Systems Research, 2023
  2. Data Minimization at Inference Time
    Cuong Tran, and Ferdinando Fioretto
    In Advances in Neural Information Processing Systems, 2023
  3. Price-Aware Deep Learning for Electricity Markets
    Vladimir Dvorkin, and Ferdinando Fioretto
    In NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning, 2023
  4. SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles
    Cuong Tran, Keyu Zhu, Ferdinando Fioretto, and Pascal Van Hentenryck
    In International Joint Conference on Artificial Intelligence, 2023
  5. On the Fairness Impacts of Private Ensembles Models
    Cuong Tran, and Ferdinando Fioretto
    In International Joint Conference on Artificial Intelligence, 2023
    Also appeared in  PPAI 2022
  6. Differentiable Model Selection for Ensemble Learning
    James Kotary, Vincenzo Di Vito, and Ferdinando Fioretto
    In International Joint Conference on Artificial Intelligence, 2023
  7. Backpropagation of Unrolled Solvers with Folded Optimization
    James Kotary, My H. Dinh, and Ferdinando Fioretto
    In International Joint Conference on Artificial Intelligence, 2023
  8. End-to-End Optimization and Learning for Multiagent Ensembles
    James Kotary, Vincenzo Di Vito, and Ferdinando Fioretto
    In International Conference on Autonomous Agents and Multiagent Systems, 2023
  9. An Analysis of the Reliability of AC Optimal Power Flow Deep Learning Proxies
    My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, and Kyri Baker
    In IEEE PES Innovative Smart Grid Technologies, 2023
  10. ArXiv
    On The Fairness Impacts of Hardware Selection in Machine Learning
    Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, and Ferdinando Fioretto
    CoRR, 2023
  11. ArXiv
    Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization
    James Kotary, Vincenzo Di Vito, Jacob Christopher, Pascal Van Hentenryck, and Ferdinando Fioretto
    CoRR, 2023
  12. ArXiv
    Context-Aware Differential Privacy for Language Modeling
    My H. Dinh, and Ferdinando Fioretto
    CoRR, 2023
  13. ArXiv
    FairDP: Certified Fairness with Differential Privacy
    Khang Tran, Ferdinando Fioretto, Issa Khalil, My T. Thai, and NhatHai Phan
    CoRR, 2023
  14. ArXiv
    Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities
    Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, and Ferdinando Fioretto
    CoRR, 2023

2022

  1. Proactive Dynamic Distributed Constraint Optimization Problems
    Khoi D. Hoang, Ferdinando Fioretto, Ping Hou, William Yeoh, Makoto Yokoo, and Roie Zivan
    Journal of Artificial Intelligence Research, 2022
  2. Pruning has a disparate impact on model accuracy
    Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu
    In Advances in Neural Information Processing Systems, 2022
    Spotlight Presentation
  3. Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method
    James Kotary, Ferdinando Fioretto, and Pascal Van Hentenryck
    In AAAI Conference on Artificial Intelligence, 2022
  4. Post-processing of Differentially Private Data: A Fairness Perspective
    Keyu Zhu, Ferdinando Fioretto, and Pascal Van Hentenryck
    In International Joint Conference on Artificial Intelligence, 2022
  5. Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey
    Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, and Keyu Zhu
    In International Joint Conference on Artificial Intelligence, 2022
  6. Integrating Machine Learning and Optimization to Boost Decision Making
    Ferdinando Fioretto
    In International Joint Conference on Artificial Intelligence, 2022
    Early Career Spotlight
  7. End-to-End Learning for Fair Ranking Systems
    James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, and Ziwei Zhu
    In The ACM Web Conference, 2022
  8. Differentially-Private Heat and Electricity Markets Coordination
    Lesia Mitridati, Emma Romei, Gabriela Hug, and Ferdinando Fioretto
    In International Conference on Probabilistic Methods Applied to Power Systems, 2022
  9. Learning Solutions for Intertemporal Power Systems Optimization with Recurrent Neural Networks
    Mostafa Mohammadian, Kyri Baker, My H. Dinh, and Ferdinando Fioretto
    In International Conference on Probabilistic Methods Applied to Power Systems, 2022
  10. ArXiv
    Fairness Increases Adversarial Vulnerability
    Cuong Tran, Keyu Zhu, Ferdinando Fioretto, and Pascal Van Hentenryck
    CoRR, 2022
  11. ArXiv
    Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming
    Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Pierre Pinson, and Jalal Kazempour
    CoRR, 2022
  12. ArXiv
    Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support
    Mostafa Mohammadian, Kyri Baker, and Ferdinando Fioretto
    CoRR, 2022
  13. ArXiv
    Deadwooding: Robust Global Pruning for Deep Neural Networks
    Sawinder Kaur, Ferdinando Fioretto, and Asif Salekin
    CoRR, 2022

2021

  1. Differential Privacy of Hierarchical Census Data: An Optimization Approach
    Ferdinando Fioretto, Pascal Van Hentenryck, and Keyu Zhu
    Artificial Intelligence Journal, 2021
    Invited in the IJCAI-21 Journal Track
  2. Differentially Private Optimal Power Flow for Distribution Grids
    Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Pierre Pinson, and Jalal Kazempour
    IEEE Transactions on Power Systems, 2021
    2022 TPWRS Best Paper Award
  3. Differentially Private Empirical Risk Minimization under the Fairness Lens
    Cuong Tran, My Dinh, and Ferdinando Fioretto
    In Advances in Neural Information Processing Systems, 2021
  4. Learning Hard Optimization Problems: A Data Generation Perspective
    James Kotary, Ferdinando Fioretto, and Pascal Van Hentenryck
    In Advances in Neural Information Processing Systems, 2021
  5. Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach
    Cuong Tran, Ferdinando Fioretto, and Pascal Van Hentenryck
    In AAAI Conference on Artificial Intelligence, 2021
    Also appeared in  PPAI 2021
  6. Bias and Variance of Post-processing in Differential Privacy
    Keyu Zhu, Pascal Van Hentenryck, and Ferdinando Fioretto
    In AAAI Conference on Artificial Intelligence, 2021
  7. Decision Making with Differential Privacy under a Fairness Lens
    Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, and Zhiyan Yao
    In International Joint Conference on Artificial Intelligence, 2021
    Also appeared in  TPDP 2021
    2022 Caspar Bowden PET award
  8. End-to-End Constrained Optimization Learning: A Survey
    James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, and Bryan Wilder
    In International Joint Conference on Artificial Intelligence, 2021
  9. Privacy-Preserving and Accountable Multi-agent Learning
    Anudit Nagar, Cuong Tran, and Ferdinando Fioretto
    In International Conference on Autonomous Agents and Multiagent Systems, 2021
  10. Constrained-Based Differential Privacy (Invited Talk)
    Ferdinando Fioretto
    In International Conference on Principles and Practice of Constraint Programming, 2021
  11. ArXiv
    Load Embeddings for Scalable AC-OPF Learning
    Terrence W. K. Mak, Ferdinando Fioretto, and Pascal Van Hentenryck
    CoRR, 2021
  12. ArXiv
    A Fairness Analysis on Private Aggregation of Teacher Ensembles
    Cuong Tran, My H. Dinh, Kyle Beiter, and Ferdinando Fioretto
    CoRR, 2021
  13. ArXiv
    Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions
    My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, and Kyri Baker
    CoRR, 2021

2020

  1. AI Mag.
    The Association for the Advancement of Artificial Intelligence 2020 Workshop Program
    Grace Bang, Guy Barash, Ryan Beal, Jacques Calı̀, Mauricio Castillo-Effen, Xin Cynthia Chen, Niyati Chhaya, Rachel Cummings, Rohan Dhoopar, Sebastijan Dumancic, Huáscar Espinoza, Eitan Farchi, Ferdinando Fioretto, Raquel Fuentetaja, Christopher William Geib, Odd Erik Gundersen, José Hernández-Orallo, Xiaowei Huang, Kokil Jaidka, Sarah Keren, Seokhwan Kim, Michel Galley, Xiaomo Liu, Tyler Lu, Zhiqiang Ma, Richard Mallah, John A. McDermid, Martin Michalowski, Reuth Mirsky, Seán Ó hÉigeartaigh, Deepak Ramachandran, Javier Segovia Aguas, Onn Shehory, Arash Shaban-Nejad, Vered Shwartz, Siddharth Srivastava, Kartik Talamadupula, Jian Tang, Pascal Van Hentenryck, Dell Zhang, and Jian Zhang
    AI Magazine, 2020
  2. Differential Privacy for Power Grid Obfuscation
    Ferdinando Fioretto, Terrence W. K. Mak, and Pascal Van Hentenryck
    IEEE Transactions on Smart Grids, 2020
  3. Privacy-Preserving Power System Obfuscation: A Bilevel Optimization Approach
    Terrence W. K. Mak, Ferdinando Fioretto, Lyndon Shi, and Pascal Van Hentenryck
    IEEE Transactions on Power Systems, 2020
    2021 TPWRS Best Paper Award
  4. Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods
    Ferdinando Fioretto, Terrence W. K. Mak, and Pascal Van Hentenryck
    In AAAI Conference on Artificial Intelligence, 2020
  5. Differential Privacy for Stackelberg Games
    Ferdinando Fioretto, Lesia Mitridati, and Pascal Van Hentenryck
    In International Joint Conference on Artificial Intelligence, 2020
  6. OptStream: Releasing Time Series Privately (Extended Abstract)
    Ferdinando Fioretto, and Pascal Van Hentenryck
    In International Joint Conference on Artificial Intelligence, 2020
  7. Lagrangian Duality for Constrained Deep Learning
    Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W. K. Mak, Cuong Tran, Federico Baldo, and Michele Lombardi
    In European Conference on Machine Learning, 2020
  8. Privacy-preserving obfuscation for distributed power systems
    Terrence W.K. Mak, Ferdinando Fioretto, and Pascal Van Hentenryck
    Electric Power Systems Research, 2020
  9. The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach
    Atena M. Tabakhi, William Yeoh, and Ferdinando Fioretto
    In Principles and Practice of Multi-Agent Systems, 2020
  10. ArXiv
    Bilevel Optimization for Differentially Private Optimization
    Ferdinando Fioretto, Terrence W. K. Mak, and Pascal Van Hentenryck
    CoRR, 2020
  11. ArXiv
    Differentially Private Optimal Power Flow for Distribution Grids
    Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Jalal Kazempour, and Pierre Pinson
    CoRR, 2020
  12. ArXiv
    Differentially Private Convex Optimization with Feasibility Guarantees
    Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Jalal Kazempour, and Pierre Pinson
    CoRR, 2020
  13. ArXiv
    High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
    Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak, and Pascal Van Hentenryck
    CoRR, 2020

2019

  1. OptStream: Releasing Time Series Privately
    Ferdinando Fioretto, and Pascal Van Hentenryck
    Journal of Artificial Intelligence Research, 2019
  2. Distributed multi-agent optimization for smart grids and home automation
    Ferdinando Fioretto, Agostino Dovier, and Enrico Pontelli
    Intelligenza Artificial, 2019
    Best AI*IA Dissertation in AI
  3. Privacy-Preserving Federated Data Sharing
    Ferdinando Fioretto, and Pascal Van Hentenryck
    In International Conference on Autonomous Agents and Multiagent Systems, 2019
  4. Differential Privacy of Hierarchical Census Data: An Optimization Approach
    Ferdinando Fioretto, and Pascal Van Hentenryck
    In International Conference on Principles and Practice of Constraint Programming, 2019
    Invited to Constraint journal - selected paper
  5. Privacy-Preserving Obfuscation of Critical Infrastructure Networks
    Ferdinando Fioretto, Terrence W. K. Mak, and Pascal Van Hentenryck
    In International Joint Conference on Artificial Intelligence, 2019
  6. ArXiv
    PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets
    Ferdinando Fioretto, Lesia Mitridati, and Pascal Van Hentenryck
    CoRR, 2019

2018

  1. Distributed Constraint Optimization Problems and Applications: A Survey
    Ferdinando Fioretto, Enrico Pontelli, and William Yeoh
    Journal of Artificial Intelligence Research, 2018
  2. Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs
    Ferdinando Fioretto, Enrico Pontelli, William Yeoh, and Rina Dechter
    Constraints - An International Journal, 2018
  3. AI Matters
    AI buzzwords explained: distributed constraint optimization problems
    Ferdinando Fioretto, and William Yeoh
    AI Matters, 2018
  4. TPLP
    Past and present (and future) of parallel and distributed computation in (constraint) logic programming
    Ferdinando Fioretto, and Enrico Pontelli
    Theory Pract. Log. Program., 2018
  5. Constrained-Based Differential Privacy for Mobility Services
    Ferdinando Fioretto, Chansoo Lee, and Pascal Van Hentenryck
    In International Conference on Autonomous Agents and Multiagent Systems, 2018
  6. A Large Neighboring Search Schema for Multi-agent Optimization
    Khoi D. Hoang, Ferdinando Fioretto, William Yeoh, Enrico Pontelli, and Roie Zivan
    In International Conference on Principles and Practice of Constraint Programming, 2018
  7. Constrained-Based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately - Releasing Optimal Power Flow Benchmarks Privately
    Ferdinando Fioretto, and Pascal Van Hentenryck
    In Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2018
  8. Constraint Composite Graph-Based Lifted Message Passing for Distributed Constraint Optimization Problems
    Ferdinando Fioretto, Hong Xu, Sven Koenig, and T. K. Satish Kumar
    In International Symposium on Artificial Intelligence and Mathematics, ISAIM 2018, Fort Lauderdale, Florida, USA, January 3-5, 2018, 2018
  9. Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph
    Ferdinando Fioretto, Hong Xu, Sven Koenig, and T. K. Satish Kumar
    In Principles and Practice of Multi-Agent Systems, 2018

2017

  1. AMEC
    Investigation of Learning Strategies for the SPOT Broker in Power TAC
    Porag Chowdhury, Russell Y. Folk, Ferdinando Fioretto, Christopher Kiekintveld, and William Yeoh
    Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets (AMEC/TADA), 2017
  2. A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs
    William Kluegel, Muhammad A. Iqbal, Ferdinando Fioretto, William Yeoh, and Enrico Pontelli
    In AAMAS 2017 Workshops, Visionary Papers, Revised Selected Papers, 2017
    Visionary Workshop Paper Award
  3. Infinite-Horizon Proactive Dynamic DCOPs
    Khoi D. Hoang, Ping Hou, Ferdinando Fioretto, William Yeoh, Roie Zivan, and Makoto Yokoo
    In International Conference on Autonomous Agents and Multiagent Systems, 2017
  4. A Multiagent System Approach to Scheduling Devices in Smart Homes
    Ferdinando Fioretto, William Yeoh, and Enrico Pontelli
    In International Conference on Autonomous Agents and Multiagent Systems, 2017
  5. A Distributed Constraint Optimization (DCOP) Approach to the Economic Dispatch with Demand Response
    Ferdinando Fioretto, William Yeoh, Enrico Pontelli, Ye Ma, and Satishkumar J. Ranade
    In International Conference on Autonomous Agents and Multiagent Systems, 2017
  6. Preference Elicitation for DCOPs
    Atena M. Tabakhi, Tiep Le, Ferdinando Fioretto, and William Yeoh
    In International Conference on Principles and Practice of Constraint Programming, 2017
  7. ArXiv
    Solving DCOPs with Distributed Large Neighborhood Search
    Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli, William Yeoh, and Roie Zivan
    CoRR, 2017

2016

  1. PhD Thesis
    Exploiting the Structure of Distributed Constraint Optimization Problems
    Ferdinando Fioretto
    University of Udine, Italy, 2016
  2. AMEC/TADA
    Investigation of Learning Strategies for the SPOT Broker in Power TAC
    Moinul Morshed Porag Chowdhury, Russell Y. Folk, Ferdinando Fioretto, Christopher Kiekintveld, and William Yeoh
    In Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets - AMEC/TADA Revised Selected Papers, 2016
  3. Multi-Variable Agents Decomposition for DCOPs
    Ferdinando Fioretto, William Yeoh, and Enrico Pontelli
    In AAAI Conference on Artificial Intelligence, 2016
  4. Proactive Dynamic Distributed Constraint Optimization
    Khoi D. Hoang, Ferdinando Fioretto, Ping Hou, Makoto Yokoo, William Yeoh, and Roie Zivan
    In International Conference on Autonomous Agents and Multiagent Systems, 2016
  5. ER-DCOPs: A Framework for Distributed Constraint Optimization with Uncertainty in Constraint Utilities
    Tiep Le, Ferdinando Fioretto, William Yeoh, Tran Cao Son, and Enrico Pontelli
    In International Conference on Autonomous Agents and Multiagent Systems, 2016
  6. A Dynamic Programming-Based MCMC Framework for Solving DCOPs with GPUs
    Ferdinando Fioretto, William Yeoh, and Enrico Pontelli
    In International Conference on Principles and Practice of Constraint Programming, 2016
  7. AISGSB
    Proactive Dynamic DCOPs
    Khoi D. Hoang, Ferdinando Fioretto, Ping Hou, Makoto Yokoo, William Yeoh, and Roie Zivan
    In AI for Smart Grids and Smart Buildings, Papers from the 2016 AAAI Workshop, Phoenix, Arizona, USA, February 12, 2016, 2016

2015

  1. Constrained Community-Based Gene Regulatory Network Inference
    Ferdinando Fioretto, Agostino Dovier, and Enrico Pontelli
    ACM Transaction on Modeling and Computer Simulation, 2015
  2. Exploiting the Structure of Distributed Constraint Optimization Problems
    Ferdinando Fioretto
    In AAAI Conference on Artificial Intelligence, 2015
  3. Multi-Variable Agents Decomposition for DCOPs to Exploit Multi-Level Parallelism
    Ferdinando Fioretto, William Yeoh, and Enrico Pontelli
    In International Conference on Autonomous Agents and Multiagent Systems, 2015
  4. Large Neighborhood Search with Quality Guarantees for Distributed Constraint Optimization Problems
    Ferdinando Fioretto, Federico Campeotto, Agostino Dovier, Enrico Pontelli, and William Yeoh
    In International Conference on Autonomous Agents and Multiagent Systems, 2015
  5. Exploiting the Structure of Distributed Constraint Optimization Problems
    Ferdinando Fioretto
    In International Conference on Autonomous Agents and Multiagent Systems, 2015
  6. Exploiting GPUs in Solving (Distributed) Constraint Optimization Problems with Dynamic Programming
    Ferdinando Fioretto, Tiep Le, Enrico Pontelli, William Yeoh, and Tran Cao Son
    In International Conference on Principles and Practice of Constraint Programming, 2015

2014

  1. GD-GIBBS: a GPU-based sampling algorithm for solving distributed constraint optimization problems
    Ferdinando Fioretto, Federico Campeotto, Luca Da Rin Fioretto, William Yeoh, and Enrico Pontelli
    In AAMAS, 2014
  2. Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems
    Ferdinando Fioretto, Tiep Le, William Yeoh, Enrico Pontelli, and Tran Cao Son
    In CP, 2014
  3. A GPU Implementation of Large Neighborhood Search for Solving Constraint Optimization Problems
    Federico Campeotto, Agostino Dovier, Ferdinando Fioretto, and Enrico Pontelli
    In European Conference on Artificial Intelligence, 2014
  4. Exploring the Use of GPUs in Constraint Solving
    Federico Campeotto, Alessandro Dal Palù, Agostino Dovier, Ferdinando Fioretto, and Enrico Pontelli
    In Practical Aspects of Declarative Languages, 2014

2013

  1. A Constraint Solver for Flexible Protein Model
    Federico Campeotto, Alessandro Dal Palù, Agostino Dovier, Ferdinando Fioretto, and Enrico Pontelli
    Journal of Artificial Intelligence Research, 2013
  2. Constraint Programming in Community-Based Gene Regulatory Network Inference
    Ferdinando Fioretto, and Enrico Pontelli
    In Computational Methods in Systems Biology, 2013
    Best Paper Award

2012

  1. A Filtering Technique for Fragment Assembly-Based Proteins Loop Modeling with Constraints
    Federico Campeotto, Alessandro Dal Palù, Agostino Dovier, Ferdinando Fioretto, and Enrico Pontelli
    In International Conference on Principles and Practice of Constraint Programming, 2012
    Also appeared in  WCB 2012