Amit Deshpande | DeepAI

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Arpita Biswas
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∙ 09/06/2023

Improved Outlier Robust Seeding for k-means

The k-means is a popular clustering objective, although it is inherently...

0 Amit Deshpande, et al. ∙

∙ 08/25/2023

Optimizing Group-Fair Plackett-Luce Ranking Models for Relevance and Ex-Post Fairness

In learning-to-rank (LTR), optimizing only the relevance (or the expecte...

0 Sruthi Gorantla, et al. ∙

∙ 06/21/2023

Sampling Individually-Fair Rankings that are Always Group Fair

Rankings on online platforms help their end-users find the relevant info...

0 Sruthi Gorantla, et al. ∙

∙ 02/12/2023

On Testing and Comparing Fair classifiers under Data Bias

In this paper, we consider a theoretical model for injecting data bias, ...

2 Mohit Sharma, et al. ∙

∙ 08/22/2022

Socially Fair Center-based and Linear Subspace Clustering

Center-based clustering (e.g., k-means, k-medians) and clustering using ...

0 Sruthi Gorantla, et al. ∙

∙ 04/26/2022

One-pass additive-error subset selection for ℓ_p subspace approximation

We consider the problem of subset selection for ℓ_p subspace approximati...

0 Amit Deshpande, et al. ∙

∙ 03/02/2022

Sampling Random Group Fair Rankings

In this paper, we consider the problem of randomized group fair ranking ...

0 Sruthi Gorantla, et al. ∙

∙ 06/19/2021

Learning and Generalization in Overparameterized Normalizing Flows

In supervised learning, it is known that overparameterized neural networ...

0 Kulin Shah, et al. ∙

∙ 05/31/2021

Rawlsian Fair Adaptation of Deep Learning Classifiers

Group-fairness in classification aims for equality of a predictive utili...

0 Kulin Shah, et al. ∙

∙ 03/20/2021

On Subspace Approximation and Subset Selection in Fewer Passes by MCMC Sampling

We consider the problem of subset selection for ℓ_p subspace approximati...

0 Amit Deshpande, et al. ∙

∙ 12/21/2020

The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective

Training datasets for machine learning often have some form of missingne...

0 Naman Goel, et al. ∙

∙ 09/24/2020

Ranking for Individual and Group Fairness Simultaneously

Search and recommendation systems, such as search engines, recruiting to...

0 Sruthi Gorantla, et al. ∙

∙ 06/30/2020

Subspace approximation with outliers

The subspace approximation problem with outliers, for given n points in ...

39 Amit Deshpande, et al. ∙

∙ 06/20/2020

How do SGD hyperparameters in natural training affect adversarial robustness?

Learning rate, batch size and momentum are three important hyperparamete...

11 Sandesh Kamath, et al. ∙

∙ 06/08/2020

On Universalized Adversarial and Invariant Perturbations

Convolutional neural networks or standard CNNs (StdCNNs) are translation...

0 Sandesh Kamath, et al. ∙

∙ 05/18/2020

Universalization of any adversarial attack using very few test examples

Deep learning models are known to be vulnerable not only to input-depend...

3 Sandesh Kamath, et al. ∙

∙ 02/26/2020

Invariance vs. Robustness of Neural Networks

We study the performance of neural network models on random geometric tr...

10 Sandesh Kamath, et al. ∙

∙ 09/03/2019

Quantifying Infra-Marginality and Its Trade-off with Group Fairness

In critical decision-making scenarios, optimizing accuracy can lead to a...

0 Arpita Biswas, et al. ∙

∙ 04/28/2018

Clustering Perturbation Resilient Instances

Euclidean k-means is a problem that is NP-hard in the worst-case but oft...

0 Amit Deshpande, et al. ∙

∙ 02/12/2018

Fair and Diverse DPP-based Data Summarization

Sampling methods that choose a subset of the data proportional to its di...

0 L. Elisa Celis, et al. ∙

∙ 08/01/2016

On the Complexity of Constrained Determinantal Point Processes

Determinantal Point Processes (DPPs) are probabilistic models that arise...

0 L. Elisa Celis, et al. ∙

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