166 citations · 515 across the 60 of their papers we have counts for
16 papers · 1 filter
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information
Suraj Kothawade, Saikat Ghosh, Sumit Shekhar +2
Deep neural networks based object detectors have shown great success in a variety of domains like autonomous vehicles, biomedical imaging, etc. It is known that their success depen…
GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer +1
Continual learning (CL) aims to develop techniques by which a single model adapts to an increasing number of tasks encountered sequentially, thereby potentially leveraging learning…
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation
Suraj Kothawade, Anmol Mekala, Chandra Sekhara D +4
State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent…
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…
Independence Properties of Generalized Submodular Information Measures
Himanshu Asnani, Jeff Bilmes, Rishabh Iyer
Recently a class of generalized information measures was defined on sets of items parametrized by submodular functions. In this paper, we propose and study various notions of indep…
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…
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios
Suraj Kothawade, Nathan Beck, Krishnateja Killamsetty +1
Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples. However, existing active learning methods do not work well in reali…
Training Data Subset Selection for Regression with Controlled Generalization Error
Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan +1
Data subset selection from a large number of training instances has been a successful approach toward efficient and cost-effective machine learning. However, models trained on a sm…
Effective Evaluation of Deep Active Learning on Image Classification Tasks
Nathan Beck, Durga Sivasubramanian, Apurva Dani +2
With the goal of making deep learning more label-efficient, a growing number of papers have been studying active learning (AL) for deep models. However, there are a number of issue…
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning
Krishnateja Killamsetty, Xujiang Zhao, Feng Chen +1
Semi-supervised learning (SSL) algorithms have had great success in recent years in limited labeled data regimes. However, the current state-of-the-art SSL algorithms are computati…
Fair Machine Learning under Limited Demographically Labeled Data
Mustafa Safa Ozdayi, Murat Kantarcioglu, Rishabh Iyer
Research has shown that, machine learning models might inherit and propagate undesired social biases encoded in the data. To address this problem, fair training algorithms are deve…
Rule Augmented Unsupervised Constituency Parsing
Atul Sahay, Anshul Nasery, Ayush Maheshwari +2
Recently, unsupervised parsing of syntactic trees has gained considerable attention. A prototypical approach to such unsupervised parsing employs reinforcement learning and auto-en…
Submodular Mutual Information for Targeted Data Subset Selection
Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan +2
With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data. We show that this problem can be effect…
PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection
Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan +2
With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to…
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan +2
The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to add…
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization
Vishal Kaushal, Suraj Kothawade, Anshul Tomar +2
Automatic video summarization is still an unsolved problem due to several challenges. The currently available datasets either have very short videos or have few long videos of only…