News Archive

2021

Oct 2021 NeurIPS 2021 paper accepted "Differentially Private Deep Learning under the Fairness Lens".
NeurIPS 2021 paper accepted "Learning Hard Optimization Problems: A Data Generation Perspective".
Sep 2021 • New preprint "A Fairness Analysis on Private Aggregation of Teacher Ensembles".
• I will be co-chairing two AAAI workshops: The workshop on Privacy Preserving AI: PPAI-22 and the workshop on Machine Learning for Operations Research: ML4OR.
Aug 2021 • Our NSF SaTC CORE proposal has been funded! Privacy and Fairness in Critical Decision Making. Thank you, NSF!
CP 2021 I will give a Keynote Talk: "Constrained-based Differential Privacy".
June 2021 New preprint "Differentially Private Deep Learning under the Fairness Lens".
New preprint "Learning Hard Optimization Problems: A Data Generation Perspective".
Our CUSE IIR proposal has been funded. "On the Potential Perils of Fairness Algorithms in Decision Making and Learning Tasks" with Sucheta Soundarajan.
New preprint "A Privacy-Preserving and Trustable Multi-agent Learning Framework".
May 2021 IJCAI 2021 paper accepted: "Decision Making with Differential Privacy under the Fairness Lens".
IJCAI 2021 journal track paper accepted: "Differential Privacy of Hierarchical Census Data: An Optimization Approach".
• New preprint "Decision Making with Differential Privacy under a Fairness Lens (Extended Version)".
Apr 2021 IJCAI 2021 paper accepted: "End-to-End Constrained Optimization Learning: A Survey".
Mar 2021 • Our paper "Privacy-preserving power system obfuscation: A bilevel optimization approach received the 2020 TPWRS Best Paper Award (IEEE Transaction of Power System) [awarded to 7 out of all papers published in 2018-2020].
• New preprint "End-to-End Constrained Optimization Learning: A Survey".
Feb 2021 AIJ paper accepted: "Differential privacy of hierarchical Census data: An optimization approach".
Jan 2021 • New preprint "Load Embeddings for Scalable AC-OPF Learning".
AAMAS 2021 paper accepted: "A Privacy-Preserving and Accountable Multi-agent Learning Framework".

2020

Dec 2020 AAAI 2021 paper accepted: "Bias and Variance of Post-processing in Differential Privacy".
AAAI 2021 paper accepted: "Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach".
Oct 2020 PRIMA 2020 paper accepted: "The Smart Appliance Scheduling Problem: A Bayesian Optimization Approach".
IEEE Transactions on Power Systems paper accepted: "Differentially Private Optimal Power Flow for Distribution Grids".
Aug 2020 • Our NSF proposal in the Robust Intelligence (RI) program has been funded. Check the Deep Constrained Learning project page.
Jul 2020 • New preprint "High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow".
• New preprint "Differential Privacy of Hierarchical Census Data: An Optimization Approach".
Jun 2020 • New preprint "Differentially Private Convex Optimization with Feasibility Guarantees".
ECML 2020 paper accepted: "Lagrangian Duality for Constrained Deep Learning".
Apr 2020 IJCAI 2020 paper accepted: "Differential Privacy for Stackebelg Games".
IJCAI 2020 paper accepted: "OptStream Releasing Time Series Privately".
Apr 2020 PSCC 2020 paper accepted: "Privacy-Preserving Obfuscation for Distributed Power Systems".
Jan 2020 • New preprint "Bilevel Optimization for Differentially Private Optimization".
• I am excited to be joining the EECS department at Syracuse University.
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