Search Results for author: Vikram S Chundawat

Found 7 papers, 5 papers with code

ConDa: Fast Federated Unlearning with Contribution Dampening

no code implementations5 Oct 2024 Vikram S Chundawat, Pushkar Niroula, Prasanna Dhungana, Stefan Schoepf, Murari Mandal, Alexandra Brintrup

Our technique does not require clients data or any kind of retraining and it does not put any computational overhead on either the client or server side.

Federated Learning

EcoVal: An Efficient Data Valuation Framework for Machine Learning

no code implementations14 Feb 2024 Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Hong Ming Tan, Bowei Chen, Mohan Kankanhalli

In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner.

Data Valuation

Deep Regression Unlearning

1 code implementation15 Oct 2022 Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Mohan Kankanhalli

In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models.

Inference Attack Machine Unlearning +2

TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data

1 code implementation12 Jul 2022 Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mukund Lahoti, Pratik Narang

We present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models.

Tabular Data Generation

Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher

1 code implementation17 May 2022 Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mohan Kankanhalli

It facilitates the provision for removal of certain set or class of data from an already trained ML model without requiring retraining from scratch.

Machine Unlearning

Zero-Shot Machine Unlearning

1 code implementation14 Jan 2022 Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mohan Kankanhalli

In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models.

Machine Unlearning Transfer Learning

Fast Yet Effective Machine Unlearning

1 code implementation17 Nov 2021 Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Mohan Kankanhalli

In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model.

Machine Unlearning

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