The k-means is a popular clustering objective, although it is inherently...
In learning-to-rank (LTR), optimizing only the relevance (or the expecte...
Rankings on online platforms help their end-users find the relevant
info...
In this paper, we consider a theoretical model for injecting data bias,
...
Center-based clustering (e.g., k-means, k-medians) and clustering using
...
We consider the problem of subset selection for ℓ_p subspace
approximati...
In this paper, we consider the problem of randomized group fair ranking ...
In supervised learning, it is known that overparameterized neural networ...
Group-fairness in classification aims for equality of a predictive utili...
We consider the problem of subset selection for ℓ_p subspace
approximati...
Training datasets for machine learning often have some form of missingne...
Search and recommendation systems, such as search engines, recruiting to...
The subspace approximation problem with outliers, for given n points in ...
Learning rate, batch size and momentum are three important hyperparamete...
Convolutional neural networks or standard CNNs (StdCNNs) are
translation...
Deep learning models are known to be vulnerable not only to input-depend...
We study the performance of neural network models on random geometric
tr...
In critical decision-making scenarios, optimizing accuracy can lead to a...
Euclidean k-means is a problem that is NP-hard in the worst-case but oft...
Sampling methods that choose a subset of the data proportional to its
di...
Determinantal Point Processes (DPPs) are probabilistic models that arise...