22 citations · 73 across the 11 of their papers we have counts for
12 papers
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification
Nathan Beck, Krishnateja Killamsetty, Suraj Kothawade +1
Active Learning (AL) is a human-in-the-loop framework to interactively and adaptively label data instances, thereby enabling significant gains in model performance compared to rand…
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models
H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia +4
A salient characteristic of pre-trained language models (PTLMs) is a remarkable improvement in their generalization capability and emergence of new capabilities with increasing mod…
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning
Krishnateja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati +3
Training deep networks and tuning hyperparameters on large datasets is computationally intensive. One of the primary research directions for efficient training is to reduce trainin…
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti +4
Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. I…
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…
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…
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…
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…
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…
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan +1
Large scale machine learning and deep models are extremely data-hungry. Unfortunately, obtaining large amounts of labeled data is expensive, and training state-of-the-art models (w…
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Krishnateja Killamsetty, Changbin Li, Chen Zhao +2
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, i…
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…