Amit Deshpande | DeepAI

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Rajiv Ratn Shah
71 publications
Nisheeth K. Vishnoi
49 publications
Amit Sharma
32 publications
Siddharth Barman
31 publications
Navin Goyal
27 publications
L. Elisa Celis
26 publications
Mohit Sharma
24 publications
Anand Louis
22 publications
Chiranjib Bhattacharyya
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Anay Mehrotra
14 publications
Arpita Biswas
14 publications

∙ 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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