Ferdinando Fioretto

32 papers · 2019–2026 · 8 conferences · across top CS/AI conferences

Achievements

🏃 Academic Marathon (6) 🐝 Cross-Pollinator (13) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (8) 🗺️ Taxonomy Completionist (39) 🌍 Conference Polyglot (8) 🤝 Dynamic Duo (15) 🏆 Grand Slam 🔬 Deep Specialist (13) 🏆 Keyword Champion (3) 🗃️ Keyword Collector (107) ⚡ Prolific Year (6) 🔥 Unstoppable (7) 💎 Century Club (31)

Conferences

IJCAI (13) AAAI (7) NIPS (5) ICML (3) AISTATS (1) ICLR (1) NAACL (1) UAI (1)

Top co-authors

Pascal Van Hentenryck (15) Cuong Tran (10) James Kotary (6) Keyu Zhu (6) Saswat Das (3) Terrence W.K. Mak (2) Jacob K Christopher (2) Juba Ziani (2) Sven Koenig (2) Jinhao Liang (2)

Research topics

Differential Privacy (4) Privacy (2)

Keywords

differential privacy (11) combinatorial optimization (5) algorithmic fairness (4) constrained optimization (3) privacy preservation (3) disparate impact (3) census datum (3) diffusion model (3) deep learning (2) adversarial robustness (2) lagrangian duality (2) fairness optimization (2) constraint satisfaction (2) solution approximation (2) group disparity (2) supervised learning (1) knowledge distillation (1) constrained generation (1) motion synthesis (1) model compression (1)

Papers

Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning AAAI 2026

Learning to Solve Differential Equation Constrained Optimization Problems ICLR 2025

Differentially Private Graph Data Release: Inefficiencies & Unfairness AISTATS 2025

Fairness Issues and Mitigations in (Differentially Private) Socio-Demographic Data Processes AAAI 2025

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion NAACL 2025

Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models ICML 2025

End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty UAI 2024

On the Effects of Fairness to Adversarial Vulnerability IJCAI 2024

Constrained Synthesis with Projected Diffusion Models NIPS 2024

Finding ε and δ of Traditional Disclosure Control Systems AAAI 2024

Disparate Impact on Group Accuracy of Linearization for Private Inference ICML 2024

On The Fairness Impacts of Hardware Selection in Machine Learning ICML 2024

SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles IJCAI 2023

Backpropagation of Unrolled Solvers with Folded Optimization IJCAI 2023

Differentiable Model Selection for Ensemble Learning IJCAI 2023

On the Fairness Impacts of Private Ensembles Models IJCAI 2023

Data Minimization at Inference Time NIPS 2023

Post-processing of Differentially Private Data: A Fairness Perspective IJCAI 2022

Integrating Machine Learning and Optimization to Boost Decision Making IJCAI 2022

Pruning has a disparate impact on model accuracy NIPS 2022

Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method AAAI 2022

Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey IJCAI 2022

Learning Hard Optimization Problems: A Data Generation Perspective NIPS 2021

Differentially Private Empirical Risk Minimization under the Fairness Lens NIPS 2021

Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach AAAI 2021

Bias and Variance of Post-processing in Differential Privacy AAAI 2021

Decision Making with Differential Privacy under a Fairness Lens IJCAI 2021

End-to-End Constrained Optimization Learning: A Survey IJCAI 2021

OptStream: Releasing Time Series Privately (Extended Abstract) IJCAI 2020

Differential Privacy for Stackelberg Games IJCAI 2020

Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods AAAI 2020

Privacy-Preserving Obfuscation of Critical Infrastructure Networks IJCAI 2019