21 citations · 22 across the 19 of their papers we have counts for
4 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…
Preserving Expert-Level Privacy in Offline Reinforcement Learning
Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta +4
The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an…
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…