10 citations · 19 across the 17 of their papers we have counts for
6 papers · 1 filter
FAIR: Filtering of Automatically Induced Rules
Divya Jyoti Bajpai, Ayush Maheshwari, Manjesh Kumar Hanawal +1
The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervi…
Adaptive Mixing of Auxiliary Losses in Supervised Learning
Durga Sivasubramanian, Ayush Maheshwari, Pradeep Shenoy +2
In several supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective. For instance…
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan +3
A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time consuming to obtain. However, it has been shown that…
SPEAR : Semi-supervised Data Programming in Python
Guttu Sai Abhishek, Harshad Ingole, Parth Laturia +4
We present SPEAR, an open-source python library for data programming with semi supervision. The package implements several recent data programming approaches including facility to…
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan +1
We consider the problem of multi-label classification where the labels lie in a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not…
Semi-Supervised Data Programming with Subset Selection
Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty +2
The paradigm of data programming, which uses weak supervision in the form of rules/labelling functions, and semi-supervised learning, which augments small amounts of labelled data…