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Durga S

3 accepted papers

2023

Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models

Parth Vipul Sangani, Arjun Shashank Kashettiwar, Pritish Chakraborty, Bhuvan Reddy Gangula, Durga S, Ganesh Ramakrishnan +2
ICML 2023poster

We study a new framework of learning mixture models via automatic clustering called PRESTO, wherein we optimize a joint objective function on the model parameters and the partitioning, with each model tailored to perform well on its specific cluster. In contrast to prior work, we do not assume any g…

Cited by 0Source ↗PDF ↗Scholar ↗
2021

GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

Krishnateja Killamsetty, Durga S, Ganesh Ramakrishnan, Abir De, Rishabh Iyer
ICML 2021spotlight

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 address this is by extracting subsets that generalize on par with the full data. In this work, we propose a general framewo…

Cited by 267Source ↗PDF ↗code ↗Scholar ↗
2021

Training Data Subset Selection for Regression with Controlled Generalization Error

Durga S, Rishabh Iyer, Ganesh Ramakrishnan, Abir De
ICML 2021spotlight

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 smaller subset may show poor generalization ability. In this paper, our goal is to design an algorithm for selecting a sub…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗