4 papers
rSDNet: Unified Robust Neural Learning against Label Noise and Adversarial Attacks
Suryasis Jana, Abhik Ghosh
Neural networks are central to modern artificial intelligence, yet their training remains highly sensitive to data contamination. Standard neural classifiers are trained by minimiz…
Provably robust learning of regression neural networks using -divergences
Abhik Ghosh, Suryasis Jana
Regression neural networks (NNs) are most commonly trained by minimizing the mean squared prediction error, which is highly sensitive to outliers and data contamination. Existing r…
Robust Inference for Non-Linear Regression Models with Applications in Enzyme Kinetics
Suryasis Jana, Abhik Ghosh
Despite linear regression being the most popular statistical modelling technique, in real-life we often need to deal with situations where the true relationship between the respons…
Asymptotic breakdown point analysis of the minimum density power divergence estimator under independent non-homogeneous setups
Suryasis Jana, Subhrajyoty Roy, Ayanendranath Basu +1
The minimum density power divergence estimator (MDPDE) has gained significant attention in the literature of robust inference due to its strong robustness properties and high asymp…