20 citations · 34 across the 8 of their papers we have counts for
8 papers
Gradient Coreset for Federated Learning
Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh Iyer +1
Federated Learning (FL) is used to learn machine learning models with data that is partitioned across multiple clients, including resource-constrained edge devices. It is therefore…
Using Early Readouts to Mediate Featural Bias in Distillation
Rishabh Tiwari, Durga Sivasubramanian, Anmol Mekala +2
Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a student model may…
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Ashish Mittal, Durga Sivasubramanian, Rishabh Iyer +2
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 proble…
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…
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…
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…