Projects - Ph.D

LIME(Local Interpretable Model- Agnostic Explanations) suffers from lack of robustness due to random sampling
Presented a modification of LIME based on CTGAN sampling and analysed it on a popular adversarial attack that takes into account that perturbed samples obtained using random sampling are out of distribution .
Results show that for around 43% test samples CTGAN-LIME was able to recognize the adversarial attack while Vanilla-LIME wasn't, proving CT-GAN LIME to be more robust than vanilla LIME.

Presented a geometric view of GP-UCB based on posterior mean and variance
Introduced two clustering-based acquisition functions GPUCB_NN & GPUCB^2
Ran GPUCB^2, GPUCB_NN, GPUCB, GPUCB_MCMC, E1, EI_MCMC, PI, PI_MCMC on various standard functions such as Branin-Hoo function, Sixhumpcamel function, Eggholder function and a synthetic function
Reduced the search space of the GP-UCB acquisition function to a single best cluster chosen by these methods

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