5 papers
Measures of closeness to cordiality for graphs
Anand Brahmbhatt, Kartikeya Rai, Amitabha Tripathi
A graph is cordial if there exists a function from the vertices of to such that the number of vertices labelled and the number of vertices labelled di…
Label Differential Privacy via Aggregation
Anand Brahmbhatt, Rishi Saket, Shreyas Havaldar +3
This paper explores the use of linear aggregation to protect the privacy of sensitive training labels through the concept of \emph{label differential privacy} (label-DP) while main…
Towards Fair and Calibrated Models
Anand Brahmbhatt, Vipul Rathore, Mausam +1
Recent literature has seen a significant focus on building machine learning models with specific properties such as fairness, i.e., being non-biased with respect to a given set of…
PAC Learning Linear Thresholds from Label Proportions
Anand Brahmbhatt, Rishi Saket, Aravindan Raghuveer
Learning from label proportions (LLP) is a generalization of supervised learning in which the training data is available as sets or bags of feature-vectors (instances) along with t…
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions
Anand Brahmbhatt, Mohith Pokala, Rishi Saket +1
In the task of Learning from Label Proportions (LLP), a model is trained on groups (a.k.a bags) of instances and their corresponding label proportions to predict labels for individ…