research
∙
09/06/2023
Improved Outlier Robust Seeding for k-means
The k-means is a popular clustering objective, although it is inherently...
share
research
∙
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...
research
∙
06/21/2023
Sampling Individually-Fair Rankings that are Always Group Fair
Rankings on online platforms help their end-users find the relevant info...
research
∙
02/12/2023
On Testing and Comparing Fair classifiers under Data Bias
In this paper, we consider a theoretical model for injecting data bias, ...
research
∙
08/22/2022
Socially Fair Center-based and Linear Subspace Clustering
Center-based clustering (e.g., k-means, k-medians) and clustering using ...
research
∙
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...
research
∙
03/02/2022
Sampling Random Group Fair Rankings
In this paper, we consider the problem of randomized group fair ranking ...
research
∙
06/19/2021
Learning and Generalization in Overparameterized Normalizing Flows
In supervised learning, it is known that overparameterized neural networ...
research
∙
05/31/2021
Rawlsian Fair Adaptation of Deep Learning Classifiers
Group-fairness in classification aims for equality of a predictive utili...
research
∙
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...
research
∙
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...
research
∙
09/24/2020
Ranking for Individual and Group Fairness Simultaneously
Search and recommendation systems, such as search engines, recruiting to...
research
∙
06/30/2020
Subspace approximation with outliers
The subspace approximation problem with outliers, for given n points in ...
research
∙
06/20/2020
How do SGD hyperparameters in natural training affect adversarial robustness?
Learning rate, batch size and momentum are three important hyperparamete...
research
∙
06/08/2020
On Universalized Adversarial and Invariant Perturbations
Convolutional neural networks or standard CNNs (StdCNNs) are translation...
research
∙
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...
research
∙
02/26/2020
Invariance vs. Robustness of Neural Networks
We study the performance of neural network models on random geometric tr...
research
∙
09/03/2019
Quantifying Infra-Marginality and Its Trade-off with Group Fairness
In critical decision-making scenarios, optimizing accuracy can lead to a...
research
∙
04/28/2018
Clustering Perturbation Resilient Instances
Euclidean k-means is a problem that is NP-hard in the worst-case but oft...
research
∙
02/12/2018
Fair and Diverse DPP-based Data Summarization
Sampling methods that choose a subset of the data proportional to its di...
research
∙
08/01/2016
