Zobrazeno 1 - 10
of 352
pro vyhledávání: '"DASGUPTA, ANIRBAN"'
Individual fairness guarantees are often desirable properties to have, but they become hard to formalize when the dataset contains outliers. Here, we investigate the problem of developing an individually fair $k$-means clustering algorithm for datase
Externí odkaz:
http://arxiv.org/abs/2412.10923
Graph Neural Networks (GNNs) have shown remarkable success in various graph-based tasks, including node classification, node regression, graph classification, and graph regression. However, their scalability remains a significant challenge, particula
Externí odkaz:
http://arxiv.org/abs/2410.15001
While coresets have been growing in terms of their application, barring few exceptions, they have mostly been limited to unsupervised settings. We consider supervised classification problems, and non-decomposable evaluation measures in such settings.
Externí odkaz:
http://arxiv.org/abs/2312.09885
Optical Character Recognition (OCR) technology finds applications in digitizing books and unstructured documents, along with applications in other domains such as mobility statistics, law enforcement, traffic, security systems, etc. The state-of-the-
Externí odkaz:
http://arxiv.org/abs/2307.04245
In this paper, we propose localized versions of Weisfeiler-Leman (WL) algorithms in an effort to both increase the expressivity, as well as decrease the computational overhead. We focus on the specific problem of subgraph counting and give localized
Externí odkaz:
http://arxiv.org/abs/2305.19659
Autor:
Portnoy, Stephen, DasGupta, Anirban
Portnoy (2019) considered the problem of constructing an optimal confidence interval for the mean based on a single observation $\, X \sim {\cal{N}}(\mu , \, \sigma^2) \,$. Here we extend this result to obtaining 1-sample confidence intervals for $\,
Externí odkaz:
http://arxiv.org/abs/2202.03556
Autor:
Dasgupta, Anirban a, d, 1, Nandi, Sandhik b, c, Gupta, Sayan a, Roy, Siddhartha a, Das, Chandrima b, c, ⁎
Publikováno v:
In BBA - Gene Regulatory Mechanisms September 2024 1867(3)
Curriculum learning is a training strategy that sorts the training examples by some measure of their difficulty and gradually exposes them to the learner to improve the network performance. Motivated by our insights from implicit curriculum ordering,
Externí odkaz:
http://arxiv.org/abs/2103.00147
Low-latency gravitational wave search pipelines such as GstLAL take advantage of low-rank factorization of the template matrix via singular value decomposition (SVD). With unprecedented improvements in detector bandwidth and sensitivity in advanced-L
Externí odkaz:
http://arxiv.org/abs/2101.03226
We present algorithms that create coresets in an online setting for clustering problems according to a wide subset of Bregman divergences. Notably, our coresets have a small additive error, similar in magnitude to the lightweight coresets Bachem et.
Externí odkaz:
http://arxiv.org/abs/2012.06522