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ORCIDFerdinando Fioretto
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- affiliation: University of Virginia, Charlottesville, VA, USA
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2020 – today
- 2024
[j13]Jayanta Mandi
, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, Ferdinando Fioretto:
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities. J. Artif. Intell. Res. 80: 1623-1701 (2024)- [c63]Saswat Das, Keyu Zhu, Christine Task, Pascal Van Hentenryck, Ferdinando Fioretto:
Finding ε and δ of Traditional Disclosure Control Systems. AAAI 2024: 22013-22020 - [c62]James Kotary, Vincenzo Di Vito, Jacob Christopher, Pascal Van Hentenryck, Ferdinando Fioretto:
Learning Joint Models of Prediction and Optimization. ECAI 2024: 2476-2483 - [c61]My H. Dinh, James Kotary, Ferdinando Fioretto:
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages. FAccT 2024: 2508-2517 - [c60]Saswat Das, Marco Romanelli, Ferdinando Fioretto:
Disparate Impact on Group Accuracy of Linearization for Private Inference. ICML 2024 - [c59]Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, Ferdinando Fioretto:
On The Fairness Impacts of Hardware Selection in Machine Learning. ICML 2024 - [c58]Cuong Tran, Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto:
On the Effects of Fairness to Adversarial Vulnerability. IJCAI 2024: 521-529 - [i65]Jacob K. Christopher, Stephen Baek, Ferdinando Fioretto:
Projected Generative Diffusion Models for Constraint Satisfaction. CoRR abs/2402.03559 (2024) - [i64]Saswat Das, Marco Romanelli, Ferdinando Fioretto:
Disparate Impact on Group Accuracy of Linearization for Private Inference. CoRR abs/2402.03629 (2024) - [i63]My H. Dinh, James Kotary, Ferdinando Fioretto:
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages. CoRR abs/2402.05252 (2024) - [i62]My H. Dinh, James Kotary, Ferdinando Fioretto:
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty. CoRR abs/2402.07772 (2024) - [i61]James Kotary, Ferdinando Fioretto:
Learning Constrained Optimization with Deep Augmented Lagrangian Methods. CoRR abs/2403.03454 (2024) - [i60]Ethan King, James Kotary, Ferdinando Fioretto, Ján Drgona:
Metric Learning to Accelerate Convergence of Operator Splitting Methods for Differentiable Parametric Programming. CoRR abs/2404.00882 (2024) - [i59]Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya Kailkhura, Ferdinando Fioretto:
Low-rank finetuning for LLMs: A fairness perspective. CoRR abs/2405.18572 (2024) - [i58]Prakhar Ganesh, Cuong Tran, Reza Shokri, Ferdinando Fioretto:
The Data Minimization Principle in Machine Learning. CoRR abs/2405.19471 (2024) - [i57]Ferdinando Fioretto, Diptangshu Sen, Juba Ziani:
Differentially Private Data Release on Graphs: Inefficiencies and Unfairness. CoRR abs/2408.05246 (2024) - [i56]Jacob K. Christopher, Brian R. Bartoldson, Bhavya Kailkhura, Ferdinando Fioretto:
Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion. CoRR abs/2408.05636 (2024) - [i55]Joonhyuk Ko, Juba Ziani, Saswat Das, Matt Williams, Ferdinando Fioretto:
Fairness Issues and Mitigations in (Differentially Private) Socio-demographic Data Processes. CoRR abs/2408.08471 (2024) - [i54]James Kotary, Vincenzo Di Vito, Jacob Christopher, Pascal Van Hentenryck, Ferdinando Fioretto:
Learning Joint Models of Prediction and Optimization. CoRR abs/2409.04898 (2024) - [i53]Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto:
Learning To Solve Differential Equation Constrained Optimization Problems. CoRR abs/2410.01786 (2024) - [i52]My H. Dinh, James Kotary, Lauryn P. Gouldin, William Yeoh, Ferdinando Fioretto:
End-to-End Optimization and Learning of Fair Court Schedules. CoRR abs/2410.17415 (2024) - [i51]Vincenzo Di Vito, Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto:
Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF. CoRR abs/2410.19157 (2024) - 2023
- [c57]James Kotary, Vincenzo Di Vito, Ferdinando Fioretto:
End-to-End Optimization and Learning for Multiagent Ensembles. AAMAS 2023: 2613-2615 - [c56]Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck:
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles. IJCAI 2023: 501-509 - [c55]Cuong Tran, Ferdinando Fioretto:
On the Fairness Impacts of Private Ensembles Models. IJCAI 2023: 510-518 - [c54]James Kotary, Vincenzo Di Vito, Ferdinando Fioretto:
Differentiable Model Selection for Ensemble Learning. IJCAI 2023: 1954-1962 - [c53]James Kotary, My H. Dinh, Ferdinando Fioretto:
Backpropagation of Unrolled Solvers with Folded Optimization. IJCAI 2023: 1963-1970 - [c52]Cuong Tran, Ferdinando Fioretto:
Data Minimization at Inference Time. NeurIPS 2023 - [i50]James Kotary, My H. Dinh, Ferdinando Fioretto:
Folded Optimization for End-to-End Model-Based Learning. CoRR abs/2301.12047 (2023) - [i49]Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck, Saswat Das, Christine Task:
Privacy and Bias Analysis of Disclosure Avoidance Systems. CoRR abs/2301.12204 (2023) - [i48]My H. Dinh, Ferdinando Fioretto:
Context-Aware Differential Privacy for Language Modeling. CoRR abs/2301.12288 (2023) - [i47]Cuong Tran, Ferdinando Fioretto:
Personalized Privacy Auditing and Optimization at Test Time. CoRR abs/2302.00077 (2023) - [i46]Cuong Tran, Ferdinando Fioretto:
On the Fairness Impacts of Private Ensembles Models. CoRR abs/2305.11807 (2023) - [i45]Khang Tran, Ferdinando Fioretto, Issa Khalil, My T. Thai, NhatHai Phan:
FairDP: Certified Fairness with Differential Privacy. CoRR abs/2305.16474 (2023) - [i44]Cuong Tran, Ferdinando Fioretto:
Data Minimization at Inference Time. CoRR abs/2305.17593 (2023) - [i43]Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, Ferdinando Fioretto:
Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities. CoRR abs/2307.13565 (2023) - [i42]Vladimir Dvorkin, Ferdinando Fioretto:
Price-Aware Deep Learning for Electricity Markets. CoRR abs/2308.01436 (2023) - [i41]James Kotary, Vincenzo Di Vito, Jacob Christopher, Pascal Van Hentenryck, Ferdinando Fioretto:
Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization. CoRR abs/2311.13087 (2023) - [i40]Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, Ferdinando Fioretto:
On The Fairness Impacts of Hardware Selection in Machine Learning. CoRR abs/2312.03886 (2023) - [i39]James Kotary, Jacob Christopher, My H. Dinh, Ferdinando Fioretto:
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization. CoRR abs/2312.17394 (2023) - 2022
- [j12]Khoi D. Hoang, Ferdinando Fioretto, Ping Hou, William Yeoh, Makoto Yokoo, Roie Zivan:
Proactive Dynamic Distributed Constraint Optimization Problems. J. Artif. Intell. Res. 74: 179-225 (2022) - [c51]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck:
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method. AAAI 2022: 7239-7246 - [c50]Awa Dieng, Miriam Rateike, Golnoosh Farnadi, Ferdinando Fioretto, Matt J. Kusner, Jessica Schrouff:
Algorithmic Fairness through the Lens of Causality and Privacy (AFCP) 2022. AFCP 2022: 1-6 - [c49]Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck:
Post-processing of Differentially Private Data: A Fairness Perspective. IJCAI 2022: 4029-4035 - [c48]Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu:
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey. IJCAI 2022: 5470-5477 - [c47]Ferdinando Fioretto:
Integrating Machine Learning and Optimization to Boost Decision Making. IJCAI 2022: 5808-5812 - [c46]Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu:
Pruning has a disparate impact on model accuracy. NeurIPS 2022 - [c45]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu:
End-to-End Learning for Fair Ranking Systems. WWW 2022: 3520-3530 - [e1]Awa Dieng, Miriam Rateike, Golnoosh Farnadi, Ferdinando Fioretto, Matt J. Kusner, Jessica Schrouff:
Algorithmic Fairness through the Lens of Causality and Privacy Workshop, AFCP 2022, New Orleans, LA, USA (hybrid), 03 December 2022. Proceedings of Machine Learning Research 214, PMLR 2022 [contents] - [i38]Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck:
Post-processing of Differentially Private Data: A Fairness Perspective. CoRR abs/2201.09425 (2022) - [i37]Lesia Mitridati, Emma Romei, Gabriela Hug, Ferdinando Fioretto:
Differentially-Private Heat and Electricity Markets Coordination. CoRR abs/2201.10634 (2022) - [i36]Sawinder Kaur, Ferdinando Fioretto, Asif Salekin:
Deadwooding: Robust Global Pruning for Deep Neural Networks. CoRR abs/2202.05226 (2022) - [i35]Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu:
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey. CoRR abs/2202.08187 (2022) - [i34]Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck:
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles. CoRR abs/2204.05157 (2022) - [i33]Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu:
Pruning has a disparate impact on model accuracy. CoRR abs/2205.13574 (2022) - [i32]Mostafa Mohammadian, Kyri Baker, Ferdinando Fioretto:
Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support. CoRR abs/2206.10579 (2022) - [i31]James Kotary, Vincenzo Di Vito, Ferdinando Fioretto:
End-to-End Optimization and Learning for Multiagent Ensembles. CoRR abs/2211.00251 (2022) - [i30]Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck:
Fairness Increases Adversarial Vulnerability. CoRR abs/2211.11835 (2022) - 2021
- [j11]Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu:
Differential privacy of hierarchical Census data: An optimization approach. Artif. Intell. 296: 103475 (2021) - [c44]Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck:
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach. AAAI 2021: 9932-9939 - [c43]Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto:
Bias and Variance of Post-processing in Differential Privacy. AAAI 2021: 11177-11184 - [c42]Anudit Nagar, Cuong Tran, Ferdinando Fioretto:
Privacy-Preserving and Accountable Multi-agent Learning. AAMAS 2021: 1605-1606 - [c41]Ferdinando Fioretto:
Constrained-Based Differential Privacy (Invited Talk). CP 2021: 2:1-2:1 - [c40]Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, Zhiyan Yao:
Decision Making with Differential Privacy under a Fairness Lens. IJCAI 2021: 560-566 - [c39]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder:
End-to-End Constrained Optimization Learning: A Survey. IJCAI 2021: 4475-4482 - [c38]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck:
Learning Hard Optimization Problems: A Data Generation Perspective. NeurIPS 2021: 24981-24992 - [c37]Cuong Tran, My H. Dinh, Ferdinando Fioretto:
Differentially Private Empirical Risk Minimization under the Fairness Lens. NeurIPS 2021: 27555-27565 - [i29]Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck:
Load Embeddings for Scalable AC-OPF Learning. CoRR abs/2101.03973 (2021) - [i28]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Bryan Wilder:
End-to-End Constrained Optimization Learning: A Survey. CoRR abs/2103.16378 (2021) - [i27]Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck:
Decision Making with Differential Privacy under a Fairness Lens. CoRR abs/2105.07513 (2021) - [i26]Anudit Nagar, Cuong Tran, Ferdinando Fioretto:
A Privacy-Preserving and Trustable Multi-agent Learning Framework. CoRR abs/2106.01242 (2021) - [i25]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck:
Learning Hard Optimization Problems: A Data Generation Perspective. CoRR abs/2106.02601 (2021) - [i24]Cuong Tran, My H. Dinh, Ferdinando Fioretto:
Differentially Private Deep Learning under the Fairness Lens. CoRR abs/2106.02674 (2021) - [i23]Cuong Tran, My H. Dinh, Kyle Beiter, Ferdinando Fioretto:
A Fairness Analysis on Private Aggregation of Teacher Ensembles. CoRR abs/2109.08630 (2021) - [i22]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck:
Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method. CoRR abs/2110.06365 (2021) - [i21]James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, Ziwei Zhu:
End-to-end Learning for Fair Ranking Systems. CoRR abs/2111.10723 (2021) - [i20]My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, Kyri Baker:
Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions. CoRR abs/2111.11168 (2021) - 2020
- [j10]Grace Bang, Guy Barash, Ryan Beal, Jacques Calì, 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, Jian Zhang:
The Association for the Advancement of Artificial Intelligence 2020 Workshop Program. AI Mag. 41(4): 100-114 (2020) - [j9]Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
Differential Privacy for Power Grid Obfuscation. IEEE Trans. Smart Grid 11(2): 1356-1366 (2020) - [c36]Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods. AAAI 2020: 630-637 - [c35]Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck:
Differential Privacy for Stackelberg Games. IJCAI 2020: 3480-3486 - [c34]Ferdinando Fioretto, Pascal Van Hentenryck:
OptStream: Releasing Time Series Privately (Extended Abstract). IJCAI 2020: 5135-5139 - [c33]Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W. K. Mak, Cuong Tran, Federico Baldo, Michele Lombardi:
Lagrangian Duality for Constrained Deep Learning. ECML/PKDD (5) 2020: 118-135 - [c32]Atena M. Tabakhi, William Yeoh, Ferdinando Fioretto:
The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach. PRIMA 2020: 100-115 - [i19]Ferdinando Fioretto, Terrence W. K. Mak, Federico Baldo, Michele Lombardi, Pascal Van Hentenryck:
A Lagrangian Dual Framework for Deep Neural Networks with Constraints. CoRR abs/2001.09394 (2020) - [i18]Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
Bilevel Optimization for Differentially Private Optimization. CoRR abs/2001.09508 (2020) - [i17]Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck:
Differential Privacy for Stackelberg Games. CoRR abs/2002.00944 (2020) - [i16]Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Jalal Kazempour, Pierre Pinson:
Differentially Private Optimal Power Flow for Distribution Grids. CoRR abs/2004.03921 (2020) - [i15]Vladimir Dvorkin, Ferdinando Fioretto, Pascal Van Hentenryck, Jalal Kazempour, Pierre Pinson:
Differentially Private Convex Optimization with Feasibility Guarantees. CoRR abs/2006.12338 (2020) - [i14]Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu:
Differential Privacy of Hierarchical Census Data: An Optimization Approach. CoRR abs/2006.15673 (2020) - [i13]Minas Chatzos, Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow. CoRR abs/2006.16356 (2020) - [i12]Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck:
Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach. CoRR abs/2009.12562 (2020) - [i11]Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto:
Bias and Variance of Post-processing in Differential Privacy. CoRR abs/2010.04327 (2020)
2010 – 2019
- 2019
- [j8]Ferdinando Fioretto, Pascal Van Hentenryck:
OptStream: Releasing Time Series Privately. J. Artif. Intell. Res. 65: 423-456 (2019) - [c31]Ferdinando Fioretto, Pascal Van Hentenryck:
Privacy-Preserving Federated Data Sharing. AAMAS 2019: 638-646 - [c30]Ferdinando Fioretto, Pascal Van Hentenryck:
Differential Privacy of Hierarchical Census Data: An Optimization Approach. CP 2019: 639-655 - [c29]Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
Privacy-Preserving Obfuscation of Critical Infrastructure Networks. IJCAI 2019: 1086-1092 - [i10]Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
Differential Privacy for Power Grid Obfuscation. CoRR abs/1901.06949 (2019) - [i9]Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
Privacy-Preserving Obfuscation of Critical Infrastructure Networks. CoRR abs/1905.09778 (2019) - [i8]Ferdinando Fioretto, Terrence W. K. Mak, Pascal Van Hentenryck:
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods. CoRR abs/1909.10461 (2019) - [i7]Terrence W. K. Mak, Ferdinando Fioretto, Pascal Van Hentenryck:
Privacy-Preserving Obfuscation for Distributed Power Systems. CoRR abs/1910.04250 (2019) - [i6]Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck:
PPSM: A Privacy-Preserving Stackelberg Mechanism: Privacy Guarantees for the Coordination of Sequential Electricity and Gas Markets. CoRR abs/1911.10178 (2019) - 2018
- [j7]Ferdinando Fioretto, William Yeoh:
AI buzzwords explained: distributed constraint optimization problems. AI Matters 3(4): 8-13 (2018) - [j6]Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter:
Accelerating exact and approximate inference for (distributed) discrete optimization with GPUs. Constraints An Int. J. 23(1): 1-43 (2018) - [j5]Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli:
Distributed multi-agent optimization for smart grids and home automation. Intelligenza Artificiale 12(2): 67-87 (2018) - [j4]Ferdinando Fioretto, Enrico Pontelli, William Yeoh:
Distributed Constraint Optimization Problems and Applications: A Survey. J. Artif. Intell. Res. 61: 623-698 (2018) - [j3]Ferdinando Fioretto, Enrico Pontelli:
Past and present (and future) of parallel and distributed computation in (constraint) logic programming. Theory Pract. Log. Program. 18(5-6): 722-724 (2018) - [c28]Ferdinando Fioretto, Chansoo Lee, Pascal Van Hentenryck:
Constrained-Based Differential Privacy for Mobility Services. AAMAS 2018: 1405-1413 - [c27]Khoi D. Hoang, Ferdinando Fioretto, William Yeoh, Enrico Pontelli, Roie Zivan:
A Large Neighboring Search Schema for Multi-agent Optimization. CP 2018: 688-706 - [c26]Ferdinando Fioretto, Pascal Van Hentenryck:
Constrained-Based Differential Privacy: Releasing Optimal Power Flow Benchmarks Privately - Releasing Optimal Power Flow Benchmarks Privately. CPAIOR 2018: 215-231 - [c25]Ferdinando Fioretto, Hong Xu, Sven Koenig, T. K. Satish Kumar:
Constraint Composite Graph-Based Lifted Message Passing for Distributed Constraint Optimization Problems. ISAIM 2018 - [c24]Ferdinando Fioretto, Hong Xu, Sven Koenig, T. K. Satish Kumar:
Solving Multiagent Constraint Optimization Problems on the Constraint Composite Graph. PRIMA 2018: 106-122 - [i5]Ferdinando Fioretto, Pascal Van Hentenryck:
Differential Private Stream Processing of Energy Consumption. CoRR abs/1808.01949 (2018) - 2017
- [c23]Ferdinando Fioretto, William Yeoh, Enrico Pontelli:
A Multiagent System Approach to Scheduling Devices in Smart Homes. AAAI Workshops 2017 - [c22]William Kluegel, Muhammad A. Iqbal, Ferdinando Fioretto, William Yeoh, Enrico Pontelli:
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs. AAMAS Workshops (Visionary Papers) 2017: 125-142 - [c21]Khoi D. Hoang, Ping Hou, Ferdinando Fioretto, William Yeoh, Roie Zivan, Makoto Yokoo:
Infinite-Horizon Proactive Dynamic DCOPs. AAMAS 2017: 212-220 - [c20]Ferdinando Fioretto, William Yeoh, Enrico Pontelli:
A Multiagent System Approach to Scheduling Devices in Smart Homes. AAMAS 2017: 981-989 - [c19]Ferdinando Fioretto, William Yeoh, Enrico Pontelli, Ye Ma, Satishkumar J. Ranade:
A Distributed Constraint Optimization (DCOP) Approach to the Economic Dispatch with Demand Response. AAMAS 2017: 999-1007 - [c18]Atena M. Tabakhi, Tiep Le, Ferdinando Fioretto, William Yeoh:
Preference Elicitation for DCOPs. CP 2017: 278-296 - [i4]Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli, William Yeoh, Roie Zivan:
Solving DCOPs with Distributed Large Neighborhood Search. CoRR abs/1702.06915 (2017) - [i3]William Kluegel, Muhammad Aamir Iqbal, Ferdinando Fioretto, William Yeoh, Enrico Pontelli:
A Realistic Dataset for the Smart Home Device Scheduling Problem for DCOPs. CoRR abs/1702.06970 (2017) - 2016
- [b1]Ferdinando Fioretto:
Exploiting the Structure of Distributed Constraint Optimization Problems. University of Udine, Italy, 2016 - [c17]Ferdinando Fioretto, William Yeoh, Enrico Pontelli:
Multi-Variable Agents Decomposition for DCOPs. AAAI 2016: 2480-2486 - [c16]Khoi D. Hoang, Ferdinando Fioretto, Ping Hou, Makoto Yokoo, William Yeoh, Roie Zivan:
Proactive Dynamic DCOPs. AAAI Workshop: AI for Smart Grids and Smart Buildings 2016 - [c15]Moinul Morshed Porag Chowdhury, Russell Y. Folk, Ferdinando Fioretto, Christopher Kiekintveld, William Yeoh:
Investigation of Learning Strategies for the SPOT Broker in Power TAC. AMEC/TADA 2016: 96-111 - [c14]Khoi D. Hoang, Ferdinando Fioretto, Ping Hou, Makoto Yokoo, William Yeoh, Roie Zivan:
Proactive Dynamic Distributed Constraint Optimization. AAMAS 2016: 597-605 - [c13]Tiep Le, Ferdinando Fioretto, William Yeoh, Tran Cao Son, Enrico Pontelli:
ER-DCOPs: A Framework for Distributed Constraint Optimization with Uncertainty in Constraint Utilities. AAMAS 2016: 606-614 - [c12]Ferdinando Fioretto, William Yeoh, Enrico Pontelli:
A Dynamic Programming-Based MCMC Framework for Solving DCOPs with GPUs. CP 2016: 813-831 - [i2]Ferdinando Fioretto, Enrico Pontelli, William Yeoh:
Distributed Constraint Optimization Problems and Applications: A Survey. CoRR abs/1602.06347 (2016) - [i1]Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter:
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs. CoRR abs/1608.05288 (2016) - 2015
- [j2]Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli:
Constrained Community-Based Gene Regulatory Network Inference. ACM Trans. Model. Comput. Simul. 25(2): 11:1-11:26 (2015) - [c11]Ferdinando Fioretto:
Exploiting the Structure of Distributed Constraint Optimization Problems. AAAI 2015: 4233- - [c10]Ferdinando Fioretto, William Yeoh, Enrico Pontelli:
Multi-Variable Agents Decomposition for DCOPs to Exploit Multi-Level Parallelism. AAMAS 2015: 1823-1824 - [c9]Ferdinando Fioretto, Federico Campeotto, Agostino Dovier, Enrico Pontelli, William Yeoh:
Large Neighborhood Search with Quality Guarantees for Distributed Constraint Optimization Problems. AAMAS 2015: 1835-1836 - [c8]Ferdinando Fioretto:
Exploiting the Structure of Distributed Constraint Optimization Problems. AAMAS 2015: 2007-2008 - [c7]Ferdinando Fioretto, Tiep Le, Enrico Pontelli, William Yeoh, Tran Cao Son:
Exploiting GPUs in Solving (Distributed) Constraint Optimization Problems with Dynamic Programming. CP 2015: 121-139 - 2014
- [c6]Ferdinando Fioretto, Federico Campeotto, Luca Da Rin Fioretto, William Yeoh, Enrico Pontelli:
GD-GIBBS: a GPU-based sampling algorithm for solving distributed constraint optimization problems. AAMAS 2014: 1339-1340 - [c5]Ferdinando Fioretto, Tiep Le, William Yeoh, Enrico Pontelli, Tran Cao Son:
Improving DPOP with Branch Consistency for Solving Distributed Constraint Optimization Problems. CP 2014: 307-323 - [c4]Federico Campeotto, Agostino Dovier, Ferdinando Fioretto, Enrico Pontelli:
A GPU Implementation of Large Neighborhood Search for Solving Constraint Optimization Problems. ECAI 2014: 189-194 - [c3]Federico Campeotto, Alessandro Dal Palù, Agostino Dovier, Ferdinando Fioretto, Enrico Pontelli:
Exploring the Use of GPUs in Constraint Solving. PADL 2014: 152-167 - 2013
- [j1]Federico Campeotto, Alessandro Dal Palù, Agostino Dovier, Ferdinando Fioretto, Enrico Pontelli:
A Constraint Solver for Flexible Protein Model. J. Artif. Intell. Res. 48: 953-1000 (2013) - [c2]Ferdinando Fioretto, Enrico Pontelli:
Constraint Programming in Community-Based Gene Regulatory Network Inference. CMSB 2013: 135-149 - 2012
- [c1]Federico Campeotto, Alessandro Dal Palù, Agostino Dovier, Ferdinando Fioretto, Enrico Pontelli:
A Filtering Technique for Fragment Assembly- Based Proteins Loop Modeling with Constraints. CP 2012: 850-866
Coauthor Index
[c63] [c62] [c58] [i54] [c56] [i49] [i41] [c51] [c49] [c48] [c45] [i38] [i35] [i34] [i30] [j11] [c44] [c43] [c40] [c39] [c38] [i29] [i28] [i27] [i25] [i22] [i21] [j10] [j9] [c36] [c35] [c34] [c33] [i19] [i18] [i17] [i16] [i15] [i14] [i13] [i12] [i11] [j8] [c31] [c30] [c29] [i10] [i9] [i8] [i7] [i6] [c28] [c26] [i5]
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