9 citations · 10 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings
Nathan Beck, Suraj Kothawade, Pradeep Shenoy +1
Deep neural networks have consistently shown great performance in several real-world use cases like autonomous vehicles, satellite imaging, etc., effectively leveraging large corpo…
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking
Rishabh Iyer, Jeff A. Bilmes
We extend the recently introduced theory of Lovasz-Bregman (LB) divergences (Iyer & Bilmes 2012) in several ways. We show that they represent a distortion between a "score" and an…
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications
Rishabh Iyer, Jeff A. Bilmes
We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a dierence between submodular functions. Similar to [30], our new algorithms are guar…