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07/27/2023
Fair Machine Unlearning: Data Removal while Mitigating Disparities
As public consciousness regarding the collection and use of personal inf...
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06/15/2023
Arbitrariness Lies Beyond the Fairness-Accuracy Frontier
Machine learning tasks may admit multiple competing models that achieve ...
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05/30/2023
Adapting Fairness Interventions to Missing Values
Missing values in real-world data pose a significant and unique challeng...
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03/10/2023
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization
We study the multi-agent Bayesian optimization (BO) problem, where multi...
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02/28/2023
Arbitrary Decisions are a Hidden Cost of Differentially-Private Training
Mechanisms used in privacy-preserving machine learning often aim to guar...
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01/27/2023
Aleatoric and Epistemic Discrimination in Classification
Machine learning (ML) models can underperform on certain population grou...
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09/17/2022
Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks
Lung cancer is the leading cause of cancer related mortality by a signif...
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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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07/11/2022
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
Bottleneck problems are an important class of optimization problems that...
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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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06/15/2022
Beyond Adult and COMPAS: Fairness in Multi-Class Prediction
We consider the problem of producing fair probabilistic classifiers for ...
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09/21/2021
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
We investigate the fairness concerns of training a machine learning mode...
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09/01/2021
Measuring Information from Moments
We investigate the problem of representing information measures in terms...
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02/11/2021
Polynomial Approximations of Conditional Expectations in Scalar Gaussian Channels
We consider a channel Y=X+N where X is a random variable satisfying 𝔼[|X...
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02/08/2021
Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation
In this paper, we propose a framework for privacy-preserving approximate...
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02/05/2021
Learning While Dissipating Information: Understanding the Generalization Capability of SGLD
Understanding the generalization capability of learning algorithms is at...
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02/02/2021
Local Differential Privacy Is Equivalent to Contraction of E_γ-Divergence
We investigate the local differential privacy (LDP) guarantees of a rand...
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12/20/2020
Privacy Analysis of Online Learning Algorithms via Contraction Coefficients
We propose an information-theoretic technique for analyzing privacy guar...
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09/09/2020
On Perfect Obfuscation: Local Information Geometry Analysis
We consider the problem of privacy-preserving data release for a specifi...
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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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06/12/2020
CPR: Classifier-Projection Regularization for Continual Learning
We propose a general, yet simple patch that can be applied to existing r...
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02/12/2020
To Split or Not to Split: The Impact of Disparate Treatment in Classification
Disparate treatment occurs when a machine learning model produces differ...
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02/04/2020
Privacy-Preserving Image Sharing via Sparsifying Layers on Convolutional Groups
We propose a practical framework to address the problem of privacy-aware...
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01/17/2020
Privacy Amplification of Iterative Algorithms via Contraction Coefficients
We investigate the framework of privacy amplification by iteration, rece...
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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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02/21/2019
Correspondence Analysis Using Neural Networks
Correspondence analysis (CA) is a multivariate statistical tool used to ...
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01/29/2019
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
When the average performance of a prediction model varies significantly ...
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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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11/29/2018
Correspondence Analysis of Government Expenditure Patterns
We analyze expenditure patterns of discretionary funds by Brazilian cong...
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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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06/21/2018
Deep Orthogonal Representations: Fundamental Properties and Applications
Several representation learning and, more broadly, dimensionality reduct...
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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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02/16/2018
Generalizing Bottleneck Problems
Given a pair of random variables (X,Y)∼ P_XY and two convex functions f_...
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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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01/16/2018
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning
In the context of machine learning, disparate impact refers to a form of...
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04/11/2017
