Somesh Jha

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University of Wisconsin-Madison

Professor of Computer Science at University of Wisconsin, Chief Scientist at Tala Systems

Generating Semantic Adversarial Examples with Differentiable Rendering

Machine learning (ML) algorithms, especially deep neural networks, have ...
10/02/2019 ∙ by Lakshya Jain, et al. ∙ 23 ∙ share

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On Need for Topology Awareness of Generative Models

Manifold assumption in learning states that: the data lie approximately ...
09/07/2019 ∙ by Uyeong Jang, et al. ∙ 20 ∙ share

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Model Extraction and Active Learning

Machine learning is being increasingly used by individuals, research ins...
11/05/2018 ∙ by Varun Chandrasekaran, et al. ∙ 14 ∙ share

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Attribution-driven Causal Analysis for Detection of Adversarial Examples

Attribution methods have been developed to explain the decision of a mac...
03/14/2019 ∙ by Susmit Jha, et al. ∙ 6 ∙ share

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Robust Attribution Regularization

An emerging problem in trustworthy machine learning is to train models t...
05/23/2019 ∙ by Jiefeng Chen, et al. ∙ 4 ∙ share

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Adversarially Robust Learning Could Leverage Computational Hardness

Over recent years, devising classification algorithms that are robust to...
05/28/2019 ∙ by Sanjam Garg, et al. ∙ 4 ∙ share

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Privacy-Preserving Collaborative Prediction using Random Forests

We study the problem of privacy-preserving machine learning (PPML) for e...
11/21/2018 ∙ by Irene Giacomelli, et al. ∙ 2 ∙ share

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Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

Directed graphical models (DGMs) are a class of probabilistic models tha...
05/30/2019 ∙ by Amrita Roy Chowdhury, et al. ∙ 1 ∙ share

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Practical Black-Box Attacks against Machine Learning

Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
02/08/2016 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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The Unintended Consequences of Overfitting: Training Data Inference Attacks

Machine learning algorithms that are applied to sensitive data pose a di...
09/05/2017 ∙ by Samuel Yeom, et al. ∙ 0 ∙ share

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Analyzing the Robustness of Nearest Neighbors to Adversarial Examples

Motivated by applications such as autonomous vehicles, test-time attacks...
06/13/2017 ∙ by Yizhen Wang, et al. ∙ 0 ∙ share

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Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics

While significant progress has been made separately on analytics systems...
06/15/2016 ∙ by Xi Wu, et al. ∙ 0 ∙ share

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The Limitations of Deep Learning in Adversarial Settings

Deep learning takes advantage of large datasets and computationally effi...
11/24/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

Deep learning algorithms have been shown to perform extremely well on ma...
11/14/2015 ∙ by Nicolas Papernot, et al. ∙ 0 ∙ share

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Manifold Assumption and Defenses Against Adversarial Perturbations

In the adversarial perturbation problem of neural networks, an adversary...
11/21/2017 ∙ by Xi Wu, et al. ∙ 0 ∙ share

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The Manifold Assumption and Defenses Against Adversarial Perturbations

In the adversarial-perturbation problem of neural networks, an adversary...
11/21/2017 ∙ by Xi Wu, et al. ∙ 0 ∙ share

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OEI: Operation Execution Integrity for Embedded Devices

We formulate a new security property, called "Operation Execution Integr...
02/09/2018 ∙ by Zhichuang Sun, et al. ∙ 0 ∙ share

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Semantic Adversarial Deep Learning

Fueled by massive amounts of data, models produced by machine-learning (...
04/19/2018 ∙ by Tommaso Dreossi, et al. ∙ 0 ∙ share

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Improving Adversarial Robustness by Data-Specific Discretization

A recent line of research proposed (either implicitly or explicitly) gra...
05/20/2018 ∙ by Jiefeng Chen, et al. ∙ 0 ∙ share

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ROSA: R Optimizations with Static Analysis

R is a popular language and programming environment for data scientists....
04/10/2017 ∙ by Rathijit Sen, et al. ∙ 0 ∙ share

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Adversarial Learning and Explainability in Structured Datasets

We theoretically and empirically explore the explainability benefits of ...
10/15/2018 ∙ by Prasad Chalasani, et al. ∙ 0 ∙ share

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Adversarial Binaries for Authorship Identification

Binary code authorship identification determines authors of a binary pro...
09/21/2018 ∙ by Xiaozhu Meng, et al. ∙ 0 ∙ share

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Explainable Black-Box Attacks Against Model-based Authentication

Establishing unique identities for both humans and end systems has been ...
09/28/2018 ∙ by Washington Garcia, et al. ∙ 0 ∙ share

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Outis: Crypto-Assisted Differential Privacy on Untrusted Servers

Differential privacy has steadily become the de-facto standard for achie...
02/20/2019 ∙ by Amrita Roy Chowdhury, et al. ∙ 0 ∙ share

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Neural-Augmented Static Analysis of Android Communication

We address the problem of discovering communication links between applic...
09/11/2018 ∙ by Jinman Zhao, et al. ∙ 0 ∙ share

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Enhancing ML Robustness Using Physical-World Constraints

Recent advances in Machine Learning (ML) have demonstrated that neural n...
05/26/2019 ∙ by Varun Chandrasekaran, et al. ∙ 0 ∙ share

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On Need for Topology-Aware Generative Models for Manifold-Based Defenses

ML algorithms or models, especially deep neural networks (DNNs), have sh...
09/07/2019 ∙ by Uyeong Jang, et al. ∙ 0 ∙ share

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Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...
08/30/2019 ∙ by Tianhao Wang, et al. ∙ 0 ∙ share

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MURS: Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

When collecting information, local differential privacy (LDP) alleviates...
08/30/2019 ∙ by Tianhao Wang, et al. ∙ 0 ∙ share

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Somesh Jha

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