885 citations · 1.7k across the 71 of their papers we have counts for
10 papers · 1 filter
Long-Term Fairness in Sequential Multi-Agent Selection with Positive Reinforcement
Bhagyashree Puranik, Ozgur Guldogan, Upamanyu Madhow +1
While much of the rapidly growing literature on fair decision-making focuses on metrics for one-shot decisions, recent work has raised the intriguing possibility of designing seque…
Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing
Bhagyashree Puranik, Upamanyu Madhow, Ramtin Pedarsani
Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we…
Adversarially Robust Classification based on GLRT
Bhagyashree Puranik, Upamanyu Madhow, Ramtin Pedarsani
Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore…
Asymptotic Behavior of Adversarial Training in Binary Classification
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
It has been consistently reported that many machine learning models are susceptible to adversarial attacks i.e., small additive adversarial perturbations applied to data points can…
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
Empirical Risk Minimization (ERM) algorithms are widely used in a variety of estimation and prediction tasks in signal-processing and machine learning applications. Despite their p…
Polarizing Front Ends for Robust CNNs
Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic +2
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up str…
CodedReduce: A Fast and Robust Framework for Gradient Aggregation in Distributed Learning
Amirhossein Reisizadeh, Saurav Prakash, Ramtin Pedarsani +1
We focus on the commonly used synchronous Gradient Descent paradigm for large-scale distributed learning, for which there has been a growing interest to develop efficient and robus…
Robust Adversarial Learning via Sparsifying Front Ends
Soorya Gopalakrishnan, Zhinus Marzi, Metehan Cekic +2
It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks. In this paper, we take a bottom-up signal processing perspect…
Combating Adversarial Attacks Using Sparse Representations
Soorya Gopalakrishnan, Zhinus Marzi, Upamanyu Madhow +1
It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks (DNNs). In this paper, we make the case that sparse representa…
Sparsity-based Defense against Adversarial Attacks on Linear Classifiers
Zhinus Marzi, Soorya Gopalakrishnan, Upamanyu Madhow +1
Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent wor…