30 citations · 75 across the 14 of their papers we have counts for
11 papers · 1 filter
Membership Inference Attacks on Diffusion Models via Quantile Regression
Shuai Tang, Zhiwei Steven Wu, Sergul Aydore +2
Recently, diffusion models have become popular tools for image synthesis because of their high-quality outputs. However, like other large-scale models, they may leak private inform…
On the Usefulness of Synthetic Tabular Data Generation
Dionysis Manousakas, Sergül Aydöre
Despite recent advances in synthetic data generation, the scientific community still lacks a unified consensus on its usefulness. It is commonly believed that synthetic data can be…
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection
Haleh Akrami, Anand Joshi, Sergul Aydore +1
Despite impressive state-of-the-art performance on a wide variety of machine learning tasks, deep learning methods can produce over-confident predictions, particularly with limited…
Differentially Private Query Release Through Adaptive Projection
Sergul Aydore, William Brown, Michael Kearns +4
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like -way marginals, subject to differential privacy. Our…
Adversarial Robustness with Non-uniform Perturbations
Ecenaz Erdemir, Jeffrey Bickford, Luca Melis +1
Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Pr…
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression
Haleh Akrami, Anand A. Joshi, Sergul Aydore +1
Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could l…
Robust Variational Autoencoder for Tabular Data with Beta Divergence
Haleh Akrami, Sergul Aydore, Richard M. Leahy +1
We propose a robust variational autoencoder with divergence for tabular data (RTVAE) with mixed categorical and continuous features. Variational autoencoders (VAE) and their va…
DropCluster: A structured dropout for convolutional networks
Liyan Chen, Philippos Mordohai, Sergul Aydore
Dropout as a common regularizer to prevent overfitting in deep neural networks has been less effective in convolutional layers than in fully connected layers. This is because Dropo…
Dynamic Local Regret for Non-convex Online Forecasting
Sergul Aydore, Tianhao Zhu, Dean Foster
We consider online forecasting problems for non-convex machine learning models. Forecasting introduces several challenges such as (i) frequent updates are necessary to deal with co…
Time-Smoothed Gradients for Online Forecasting
Tianhao Zhu, Sergul Aydore
Here, we study different update rules in stochastic gradient descent (SGD) for online forecasting problems. The selection of the learning rate parameter is critical in SGD. However…
A Local Regret in Nonconvex Online Learning
Sergul Aydore, Lee Dicker, Dean Foster
We consider an online learning process to forecast a sequence of outcomes for nonconvex models. A typical measure to evaluate online learning algorithms is regret but such standard…