Vikash Sehwag | DeepAI

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∙ 06/08/2023

Differentially Private Image Classification by Learning Priors from Random Processes

In privacy-preserving machine learning, differentially private stochasti...

0 Xinyu Tang, et al. ∙

∙ 01/30/2023

Extracting Training Data from Diffusion Models

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion ha...

0 Nicholas Carlini, et al. ∙

∙ 09/15/2022

A Light Recipe to Train Robust Vision Transformers

In this paper, we ask whether Vision Transformers (ViTs) can serve as an...

18 Edoardo Debenedetti, et al. ∙

∙ 06/20/2022

Understanding Robust Learning through the Lens of Representation Similarities

Representation learning, i.e. the generation of representations useful f...

33 Christian Cianfarani, et al. ∙

∙ 03/31/2022

Generating High Fidelity Data from Low-density Regions using Diffusion Models

Our work focuses on addressing sample deficiency from low-density region...

1 Vikash Sehwag, et al. ∙

∙ 04/19/2021

Improving Adversarial Robustness Using Proxy Distributions

We focus on the use of proxy distributions, i.e., approximations of the ...

9 Vikash Sehwag, et al. ∙

∙ 04/16/2021

Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries

Understanding the fundamental limits of robust supervised learning has e...

8 Arjun Nitin Bhagoji, et al. ∙

∙ 03/22/2021

SSD: A Unified Framework for Self-Supervised Outlier Detection

We ask the following question: what training information is required to ...

18 Vikash Sehwag, et al. ∙

∙ 10/19/2020

RobustBench: a standardized adversarial robustness benchmark

Evaluation of adversarial robustness is often error-prone leading to ove...

4 Francesco Croce, et al. ∙

∙ 07/26/2020

Fast-Convergent Federated Learning

Federated learning has emerged recently as a promising solution for dist...

0 Hung T. Nguyen, et al. ∙

∙ 07/08/2020

A Critical Evaluation of Open-World Machine Learning

Open-world machine learning (ML) combines closed-world models trained on...

0 Liwei Song, et al. ∙

∙ 06/24/2020

Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks

With increasing expressive power, deep neural networks have significantl...

0 Vikash Sehwag, et al. ∙

∙ 05/17/2020

PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields

Localized adversarial patches aim to induce misclassification in machine...

0 Chong Xiang, et al. ∙

∙ 02/24/2020

On Pruning Adversarially Robust Neural Networks

In safety-critical but computationally resource-constrained applications...

0 Vikash Sehwag, et al. ∙

∙ 06/14/2019

Towards Compact and Robust Deep Neural Networks

Deep neural networks have achieved impressive performance in many applic...

0 Vikash Sehwag, et al. ∙

∙ 05/05/2019

Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples

A large body of recent work has investigated the phenomenon of evasion a...

18 Vikash Sehwag, et al. ∙

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