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of 52
pro vyhledávání: '"Gupta, Soumyajit"'
In algorithmic toxicity detection pipelines, it is important to identify which demographic group(s) are the subject of a post, a task commonly known as \textit{target (group) detection}. While accurate detection is clearly important, we further advoc
Externí odkaz:
http://arxiv.org/abs/2407.11933
Publikováno v:
Proceedings of the Web Conference, WWW 2023
Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comments targeting different demographic groups can vary markedly across grou
Externí odkaz:
http://arxiv.org/abs/2302.07372
Recent work has emphasized the importance of balancing competing objectives in model training (e.g., accuracy vs. fairness, or competing measures of fairness). Such trade-offs reflect a broader class of multi-objective optimization (MOO) problems in
Externí odkaz:
http://arxiv.org/abs/2204.07661
Multi-objective optimization (MOO) problems require balancing competing objectives, often under constraints. The Pareto optimal solution set defines all possible optimal trade-offs over such objectives. In this work, we present a novel method for Par
Externí odkaz:
http://arxiv.org/abs/2110.15442
Autor:
Roy, Aritra, Sen Gupta, Soumyajit, Samanta, Arunkumar, Sai Likhith, P.V.S., Das, Sandipan Kumar
Publikováno v:
In International Journal of Hydrogen Energy 19 June 2024 71:131-142
Autor:
Singh, Gurpreet, Gupta, Soumyajit
SVD serves as an exploratory tool in identifying the dominant features in the form of top rank-r singular factors corresponding to the largest singular values. For Big Data applications it is well known that Singular Value Decomposition (SVD) is rest
Externí odkaz:
http://arxiv.org/abs/2104.13968
Hyperspectral unmixing involves separating a pixel as a weighted combination of its constituent endmembers and corresponding fractional abundances, with the current state of the art results achieved by neural models on benchmark datasets. However, th
Externí odkaz:
http://arxiv.org/abs/2102.05713
Classification, recommendation, and ranking problems often involve competing goals with additional constraints (e.g., to satisfy fairness or diversity criteria). Such optimization problems are quite challenging, often involving non-convex functions a
Externí odkaz:
http://arxiv.org/abs/2101.11684
In a Big Data setting computing the dominant SVD factors is restrictive due to the main memory requirements. Recently introduced streaming Randomized SVD schemes work under the restrictive assumption that the singular value spectrum of the data has e
Externí odkaz:
http://arxiv.org/abs/2010.14226
Constrained Optimization solution algorithms are restricted to point based solutions. In practice, single or multiple objectives must be satisfied, wherein both the objective function and constraints can be non-convex resulting in multiple optimal so
Externí odkaz:
http://arxiv.org/abs/2009.06024