83 citations · 84 across the 8 of their papers we have counts for
9 papers
Energy and Latency First: How Deployment Constraints Reverse Time-Series Model Selection
Shruti Bothe, Illyyne Saffar, Ayush K. Varshney
AI models, and time-series models in particular are typically ranked by accuracy and deployed under constraints absent from that ranking. In this paper, we study a real setting whe…
Scale When Needed: Adaptive Neuron-level Mixed Precision Quantization Aware Training
Ayush K. Varshney, Konstantinos Vandikas, Šarūnas Girdzijauskas +2
Deploying deep neural networks on resource-constrained 6G edge devices demands aggressive compression with minimal accuracy loss. Quantization-Aware Training (QAT) has emerged as a…
When to restart? Exploring escalating restarts on convergence
Ayush K. Varshney, Šarūnas Girdzijauskas, Konstantinos Vandikas +1
Learning rate scheduling plays a critical role in the optimization of deep neural networks, directly influencing convergence speed, stability, and generalization. While existing sc…
Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention
Ayush K. Varshney, Vicenç Torra
Machine Unlearning allows participants to remove their data from a trained machine learning model in order to preserve their privacy, and security. However, the machine unlearning…
Unlearning Clients, Features and Samples in Vertical Federated Learning
Ayush K. Varshney, Konstantinos Vandikas, Vicenç Torra
Federated Learning (FL) has emerged as a prominent distributed learning paradigm. Within the scope of privacy preservation, information privacy regulations such as GDPR entitle use…
Efficient Federated Unlearning under Plausible Deniability
Ayush K. Varshney, Vicenç Torra
Privacy regulations like the GDPR in Europe and the CCPA in the US allow users the right to remove their data ML applications. Machine unlearning addresses this by modifying the ML…
Concept Drift Detection using Ensemble of Integrally Private Models
Ayush K. Varshney, Vicenc Torra
Deep neural networks (DNNs) are one of the most widely used machine learning algorithm. DNNs requires the training data to be available beforehand with true labels. This is not fea…
Fair Differentially Private Federated Learning Framework
Ayush K. Varshney, Sonakshi Garg, Arka Ghosh +1
Federated learning (FL) is a distributed machine learning strategy that enables participants to collaborate and train a shared model without sharing their individual datasets. Priv…
Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems
Ayush K. Varshney, Vicenç Torra
Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have b…