176 citations · 360 across the 13 of their papers we have counts for
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A Unified Framework for Continual Learning and Unlearning
Romit Chatterjee, Vikram Chundawat, Ayush Tarun +2
Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while…
EcoVal: An Efficient Data Valuation Framework for Machine Learning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +3
Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley va…
Deep Regression Unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +1
With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model. In the last few…
TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data
Vikram S Chundawat, Ayush K Tarun, Murari Mandal +2
Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality…
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher
Vikram S Chundawat, Ayush K Tarun, Murari Mandal +1
Machine unlearning has become an important area of research due to an increasing need for machine learning (ML) applications to comply with the emerging data privacy regulations. I…
Zero-Shot Machine Unlearning
Vikram S Chundawat, Ayush K Tarun, Murari Mandal +1
Modern privacy regulations grant citizens the right to be forgotten by products, services and companies. In case of machine learning (ML) applications, this necessitates deletion o…
Fast Yet Effective Machine Unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal +1
Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-bas…