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09/18/2023
A Semi-Supervised Approach for Power System Event Identification
Event identification is increasingly recognized as crucial for enhancing...
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04/15/2023
(α,β)-Leakage: A Unified Privacy Leakage Measure
We introduce a family of information leakage measures called maximal (α,...
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02/28/2023
Towards Addressing GAN Training Instabilities: Dual-objective GANs with Tunable Parameters
In an effort to address the training instabilities of GANs, we introduce...
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02/17/2023
Smoothly Giving up: Robustness for Simple Models
There is a growing need for models that are interpretable and have reduc...
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11/28/2022
An Alphabet of Leakage Measures
We introduce a family of information leakage measures called maximal α,β...
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11/10/2022
Robust Model Selection of Non Tree-Structured Gaussian Graphical Models
We consider the problem of learning the structure underlying a Gaussian ...
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09/28/2022
An Operational Approach to Information Leakage via Generalized Gain Functions
We introduce a gain function viewpoint of information leakage by proposi...
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09/08/2022
PMU Tracker: A Visualization Platform for Epicentric Event Propagation Analysis in the Power Grid
The electrical power grid is a critical infrastructure, with disruptions...
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08/20/2022
The Saddle-Point Accountant for Differential Privacy
We introduce a new differential privacy (DP) accountant called the saddl...
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08/09/2022
Parameter Estimation in Ill-conditioned Low-inertia Power Systems
This paper examines model parameter estimation in dynamic power systems ...
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06/25/2022
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime
Most differential privacy mechanisms are applied (i.e., composed) numero...
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06/05/2022
AugLoss: A Learning Methodology for Real-World Dataset Corruption
Deep Learning (DL) models achieve great successes in many domains. Howev...
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05/12/2022
α-GAN: Convergence and Estimation Guarantees
We prove a two-way correspondence between the min-max optimization of ge...
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02/14/2022
A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Power systems are prone to a variety of events (e.g. line trips and gene...
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02/12/2022
A Variational Formula for Infinity-Rényi Divergence with Applications to Information Leakage
We present a variational characterization for the Rényi divergence of or...
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01/19/2022
A Complex-LASSO Approach for Localizing Forced Oscillations in Power Systems
We study the problem of localizing multiple sources of forced oscillatio...
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01/19/2022
Localization and Estimation of Unknown Forced Inputs: A Group LASSO Approach
We model and study the problem of localizing a set of sparse forcing inp...
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08/29/2021
Lower Bounds for the Minimum Mean-Square Error via Neural Network-based Estimation
The minimum mean-square error (MMSE) achievable by optimal estimation of...
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08/19/2021
Evaluating Multiple Guesses by an Adversary via a Tunable Loss Function
We consider a problem of guessing, wherein an adversary is interested in...
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06/18/2021
Being Properly Improper
In today's ML, data can be twisted (changed) in various ways, either for...
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06/09/2021
Realizing GANs via a Tunable Loss Function
We introduce a tunable GAN, called α-GAN, parameterized by α∈ (0,∞], whi...
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08/14/2020
Three Variants of Differential Privacy: Lossless Conversion and Applications
We consider three different variants of differential privacy (DP), namel...
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06/22/2020
On the alpha-loss Landscape in the Logistic Model
We analyze the optimization landscape of a recently introduced tunable c...
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01/16/2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences
We derive the optimal differential privacy (DP) parameters of a mechanis...
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11/08/2019
Theoretical Guarantees for Model Auditing with Finite Adversaries
Privacy concerns have led to the development of privacy-preserving appro...
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09/27/2019
Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints
We present Generative Adversarial rePresentations (GAP) as a data-driven...
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06/05/2019
A Tunable Loss Function for Classification
Recently, a parametrized class of loss functions called α-loss, α∈ [1,∞]...
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02/12/2019
A Tunable Loss Function for Binary Classification
We present α-loss, α∈ [1,∞], a tunable loss function for binary classifi...
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01/21/2019
Robustness of Maximal α-Leakage to Side Information
Maximal α-leakage is a tunable measure of information leakage based on t...
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11/14/2018
On the Robustness of Information-Theoretic Privacy Measures and Mechanisms
Consider a data publishing setting for a dataset composed of non-private...
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09/24/2018
Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs
In the first half of the paper, we introduce a tunable measure for infor...
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07/13/2018
Generative Adversarial Privacy
We present a data-driven framework called generative adversarial privacy...
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06/08/2018
A Tunable Measure for Information Leakage
A tunable measure for information leakage called maximal α-leakage is in...
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05/31/2018
Privacy Under Hard Distortion Constraints
We study the problem of data disclosure with privacy guarantees, wherein...
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01/18/2018
On the Contractivity of Privacy Mechanisms
We present a novel way to compare the statistical cost of privacy mechan...
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01/18/2018
The Utility Cost of Robust Privacy Guarantees
Consider a data publishing setting for a data set with public and privat...
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10/26/2017
