1 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
Learning Predictive Checklists with Probabilistic Logic Programming
Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan
Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their inter…
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…
Modularity aided consistent attributed graph clustering via coarsening
Samarth Bhatia, Yukti Makhija, Manoj Kumar +1
Graph clustering is an important unsupervised learning technique for partitioning graphs with attributes and detecting communities. However, current methods struggle to accurately…
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…
Learning predictive checklists from continuous medical data
Yukti Makhija, Edward De Brouwer, Rahul G. Krishnan
Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpre…