11 citations · 11 across the 10 of their papers we have counts for
12 papers
Constrained Adversarial Perturbation
Virendra Nishad, Bhaskar Mukhoty, Hilal AlQuabeh +2
Deep neural networks have achieved remarkable success in a wide range of classification tasks. However, they remain highly susceptible to adversarial examples - inputs that are sub…
Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery
William de Vazelhes, Bhaskar Mukhoty, Xiao-Tong Yuan +1
Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive…
Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory
Hilal AlQuabeh, Bhaskar Mukhoty, Bin Gu
Pairwise learning is essential in machine learning, especially for problems involving loss functions defined on pairs of training examples. Online gradient descent (OGD) algorithms…
Energy Efficient Training of SNN using Local Zeroth Order Method
Bhaskar Mukhoty, Velibor Bojkovic, William de Vazelhes +3
Spiking neural networks are becoming increasingly popular for their low energy requirement in real-world tasks with accuracy comparable to the traditional ANNs. SNN training algori…
Corruption-tolerant Algorithms for Generalized Linear Models
Bhaskar P Mukhoty, Debojyoti Dey, Purushottam Kar
This paper presents SVAM (Sequential Variance-Altered MLE), a unified framework for learning generalized linear models under adversarial label corruption in training data. SVAM ext…
AGGLIO: Global Optimization for Locally Convex Functions
Debojyoti Dey, Bhaskar Mukhoty, Purushottam Kar
This paper presents AGGLIO (Accelerated Graduated Generalized LInear-model Optimization), a stage-wise, graduated optimization technique that offers global convergence guarantees f…