Rishabh Iyer


Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Ayush Maheshwari | Krishnateja Killamsetty | Ganesh Ramakrishnan | Rishabh Iyer | Marina Danilevsky | Lucian Popa
Findings of the Association for Computational Linguistics: ACL 2022

A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time-consuming to obtain. Although a small amount of labeled data cannot be used to train a model, it can be used effectively for the generation of humaninterpretable labeling functions (LFs). These LFs, in turn, have been used to generate a large amount of additional noisy labeled data in a paradigm that is now commonly referred to as data programming. Previous methods of generating LFs do not attempt to use the given labeled data further to train a model, thus missing opportunities for improving performance. Additionally, since the LFs are generated automatically, they are likely to be noisy, and naively aggregating these LFs can lead to suboptimal results. In this work, we propose an LF-based bi-level optimization framework WISDOM to solve these two critical limitations. WISDOM learns a joint model on the (same) labeled dataset used for LF induction along with any unlabeled data in a semi-supervised manner, and more critically, reweighs each LF according to its goodness, influencing its contribution to the semi-supervised loss using a robust bi-level optimization algorithm. We show that WISDOM significantly outperforms prior approaches on several text classification datasets.

Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Ashish Mittal | Durga Sivasubramanian | Rishabh Iyer | Preethi Jyothi | Ganesh Ramakrishnan
Findings of the Association for Computational Linguistics: EMNLP 2022

Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost. Training with a subset of training data could mitigate this problem if the subset selected could achieve on-par performance with training with the entire dataset. Although there are many data subset selection(DSS) algorithms, direct application to the RNN-T is difficult, especially the DSS algorithms that are adaptive and use learning dynamics such as gradients, as RNN-T tend to have gradients with a significantly larger memory footprint. In this paper, we propose Partitioned Gradient Matching (PGM) a novel distributable DSS algorithm, suitable for massive datasets like those used to train RNN-T. Through extensive experiments on Librispeech 100H and Librispeech 960H, we show that PGM achieves between 3x to 6x speedup with only a very small accuracy degradation (under 1% absolute WER difference). In addition, we demonstrate similar results for PGM even in settings where the training data is corrupted with noise.

SPEAR : Semi-supervised Data Programming in Python
Guttu Abhishek | Harshad Ingole | Parth Laturia | Vineeth Dorna | Ayush Maheshwari | Ganesh Ramakrishnan | Rishabh Iyer
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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 programmatically label and build training data. SPEAR facilitates weak supervision in the form of heuristics (or rules) and association of noisy labels to the training dataset. These noisy labels are aggregated to assign labels to the unlabeled data for downstream tasks. We have implemented several label aggregation approaches that aggregate the noisy labels and then train using the noisily labeled set in a cascaded manner. Our implementation also includes other approaches that jointly aggregate and train the model for text classification tasks. Thus, in our python package, we integrate several cascade and joint data-programming approaches while also providing the facility of data programming by letting the user define labeling functions or rules. The code and tutorial notebooks are available at https://github.com/decile-team/spear. Further, extensive documentation can be found at https://spear-decile.readthedocs.io/. Video tutorials demonstrating the usage of our package are available https://youtube.com/playlist?list=PLW8agt_HvkVnOJoJAqBpaerFb-z-ZlqlP. We also present some real-world use cases of SPEAR.


Semi-Supervised Data Programming with Subset Selection
Ayush Maheshwari | Oishik Chatterjee | Krishnateja Killamsetty | Ganesh Ramakrishnan | Rishabh Iyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Rule Augmented Unsupervised Constituency Parsing
Atul Sahay | Anshul Nasery | Ayush Maheshwari | Ganesh Ramakrishnan | Rishabh Iyer
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures
Ramakrishna Bairi | Rishabh Iyer | Ganesh Ramakrishnan | Jeff Bilmes
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)