54 citations · 173 across the 30 of their papers we have counts for
7 papers · 1 filter
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
Naman Goel, Alfonso Amayuelas, Amit Deshpande +1
Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes ind…
On the Problem of Underranking in Group-Fair Ranking
Sruthi Gorantla, Amit Deshpande, Anand Louis
Search and recommendation systems, such as search engines, recruiting tools, online marketplaces, news, and social media, output ranked lists of content, products, and sometimes, p…
Subspace approximation with outliers
Amit Deshpande, Rameshwar Pratap
The subspace approximation problem with outliers, for given points in dimensions , an integer , and an outlier parameter $0…
How do SGD hyperparameters in natural training affect adversarial robustness?
Sandesh Kamath, Amit Deshpande, K V Subrahmanyam
Learning rate, batch size and momentum are three important hyperparameters in the SGD algorithm. It is known from the work of Jastrzebski et al. arXiv:1711.04623 that large batch s…
On Universalized Adversarial and Invariant Perturbations
Sandesh Kamath, Amit Deshpande, K V Subrahmanyam
Convolutional neural networks or standard CNNs (StdCNNs) are translation-equivariant models that achieve translation invariance when trained on data augmented with sufficient trans…
Universalization of any adversarial attack using very few test examples
Sandesh Kamath, Amit Deshpande, K V Subrahmanyam +1
Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but also to input-agnostic or universal adversarial attacks. Dezfooli et al. \cite{D…