Projects - Ph.D
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
The process of gaining control of user computers through different types of attacks and using these for malicious activities is known as phishing. In this project, we developed an end-to-end system using which, a person can identify if the link is of an attacker or not, just by entering the URL on our website. To pre-process the data tokenization, stemming and vectorization have been utilized so that it can be used by algorithms. We have ensembled a Quantum Machine learning algorithm with LGBM and also used four classical machine learning algorithms, one bagging based and one boosting-based algorithm.