21 citations · 47 across the 19 of their papers we have counts for
5 papers · 1 filter
Aggregating Data for Optimal and Private Learning
Sushant Agarwal, Yukti Makhija, Rishi Saket +1
Multiple Instance Regression (MIR) and Learning from Label Proportions (LLP) are learning frameworks arising in many applications, where the training data is partitioned into disjo…
Weak to Strong Learning from Aggregate Labels
Yukti Makhija, Rishi Saket
In learning from aggregate labels, the training data consists of sets or "bags" of feature-vectors (instances) along with an aggregate label for each bag derived from the (usually…
Learning from Label Proportions and Covariate-shifted Instances
Sagalpreet Singh, Navodita Sharma, Shreyas Havaldar +2
In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only…
FRACTAL: Fine-Grained Scoring from Aggregate Text Labels
Yukti Makhija, Priyanka Agrawal, Rishi Saket +1
Large language models (LLMs) are being increasingly tuned to power complex generation tasks such as writing, fact-seeking, querying and reasoning. Traditionally, human or model fee…
Hardness of Learning Boolean Functions from Label Proportions
Venkatesan Guruswami, Rishi Saket
In recent years the framework of learning from label proportions (LLP) has been gaining importance in machine learning. In this setting, the training examples are aggregated into s…