166 citations · 515 across the 60 of their papers we have counts for
10 papers · 1 filter
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…
Deep Submodular Networks for Extractive Data Summarization
Suraj Kothawade, Jiten Girdhar, Chandrashekhar Lavania +1
Deep Models are increasingly becoming prevalent in summarization problems (e.g. document, video and images) due to their ability to learn complex feature interactions and represent…
Improving Accuracy of Federated Learning in Non-IID Settings
Mustafa Safa Ozdayi, Murat Kantarcioglu, Rishabh Iyer
Federated Learning (FL) is a decentralized machine learning protocol that allows a set of participating agents to collaboratively train a model without sharing their data. This mak…
A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures
Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan +3
We study submodular information measures as a rich framework for generic, query-focused, privacy sensitive, and update summarization tasks. While past work generally treats these p…
How Out-of-Distribution Data Hurts Semi-Supervised Learning
Xujiang Zhao, Killamsetty Krishnateja, Rishabh Iyer +1
Recent semi-supervised learning algorithms have demonstrated greater success with higher overall performance due to better-unlabeled data representations. Nonetheless, recent resea…
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework
Vishal Kaushal, Suraj Kothawade, Rishabh Iyer +1
Automatic video summarization is still an unsolved problem due to several challenges. We take steps towards making automatic video summarization more realistic by addressing them.…
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…
Concave Aspects of Submodular Functions
Rishabh Iyer, Jeff Bilmes
Submodular Functions are a special class of set functions, which generalize several information-theoretic quantities such as entropy and mutual information [1]. Submodular function…
Submodular Combinatorial Information Measures with Applications in Machine Learning
Rishabh Iyer, Ninad Khargonkar, Jeff Bilmes +1
Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these…
Robust Submodular Minimization with Applications to Cooperative Modeling
Rishabh Iyer
Robust Optimization is becoming increasingly important in machine learning applications. This paper studies the problem of robust submodular minimization subject to combinatorial c…