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07/25/2023
Exploring the Sharpened Cosine Similarity
Convolutional layers have long served as the primary workhorse for image...
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06/27/2023
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile
Differential privacy (DP) is the prevailing technique for protecting use...
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04/24/2023
Sparse Private LASSO Logistic Regression
LASSO regularized logistic regression is particularly useful for its bui...
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03/18/2023
The Challenge of Differentially Private Screening Rules
Linear L_1-regularized models have remained one of the simplest and most...
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01/15/2023
A Coreset Learning Reality Check
Subsampling algorithms are a natural approach to reduce data size before...
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10/16/2022
A General Framework for Auditing Differentially Private Machine Learning
We present a framework to statistically audit the privacy guarantee conf...
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06/09/2022
Neural Bregman Divergences for Distance Learning
Many metric learning tasks, such as triplet learning, nearest neighbor r...
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02/18/2022
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
We explore the utility of information contained within a dropout based B...
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02/14/2022
Continuously Generalized Ordinal Regression for Linear and Deep Models
Ordinal regression is a classification task where classes have an order ...
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09/29/2021
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants
Deep neural networks (DNN) have been used successfully in many scientifi...
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06/05/2020
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Learning disentangled representations is regarded as a fundamental task ...
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11/30/2018
