Sergul Aydore | DeepAI

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Featured Co-authors

Zhiwei Steven Wu
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Yu-Xiang Wang
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Aaron Roth
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Gaël Varoquaux
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Bertrand Thirion
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Michael Kearns
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Cedric Archambeau
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Krishnaram Kenthapadi
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Shuai Tang
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Yichen Wang
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Haleh Akrami
13 publications

∙ 09/14/2023

Beta quantile regression for robust estimation of uncertainty in the presence of outliers

Quantile Regression (QR) can be used to estimate aleatoric uncertainty i...

0 Haleh Akrami, et al. ∙

∙ 06/27/2023

On the Usefulness of Synthetic Tabular Data Generation

Despite recent advances in synthetic data generation, the scientific com...

0 Dionysis Manousakas, et al. ∙

∙ 03/06/2023

Improved Differentially Private Regression via Gradient Boosting

We revisit the problem of differentially private squared error linear re...

0 Shuai Tang, et al. ∙

∙ 09/15/2022

Private Synthetic Data for Multitask Learning and Marginal Queries

We provide a differentially private algorithm for producing synthetic da...

8 Giuseppe Vietri, et al. ∙

∙ 09/20/2021

Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection

Despite impressive state-of-the-art performance on a wide variety of mac...

28 Haleh Akrami, et al. ∙

∙ 03/11/2021

Differentially Private Query Release Through Adaptive Projection

We propose, implement, and evaluate a new algorithm for releasing answer...

9 Sergul Aydore, et al. ∙

∙ 02/24/2021

Adversarial Robustness with Non-uniform Perturbations

Robustness of machine learning models is critical for security related a...

0 Ecenaz Erdemir, et al. ∙

∙ 10/18/2020

Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression

Estimation of uncertainty in deep learning models is of vital importance...

8 Haleh Akrami, et al. ∙

∙ 06/15/2020

Robust Variational Autoencoder for Tabular Data with Beta Divergence

We propose a robust variational autoencoder with β divergence for tabula...

0 Haleh Akrami, et al. ∙

∙ 02/07/2020

DropCluster: A structured dropout for convolutional networks

Dropout as a regularizer in deep neural networks has been less effective...

16 Liyan Chen, et al. ∙

∙ 10/16/2019

Dynamic Local Regret for Non-convex Online Forecasting

We consider online forecasting problems for non-convex machine learning ...

0 Sergul Aydore, et al. ∙

∙ 10/02/2019

ROMark: A Robust Watermarking System Using Adversarial Training

The availability and easy access to digital communication increase the r...

0 Bingyang Wen, et al. ∙

∙ 05/21/2019

Time-Smoothed Gradients for Online Forecasting

Here, we study different update rules in stochastic gradient descent (SG...

0 Tianhao Zhu, et al. ∙

∙ 11/13/2018

A Local Regret in Nonconvex Online Learning

We consider an online learning process to forecast a sequence of outcome...

0 Sergul Aydore, et al. ∙

∙ 07/31/2018

Using Feature Grouping as a Stochastic Regularizer for High-Dimensional Noisy Data

The use of complex models --with many parameters-- is challenging with h...

16 Sergul Aydore, et al. ∙

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