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Kumar Shubham

4 accepted papers

2026

Enhancing Trustworthiness of Fine-Tuned LLMs via Regularized Subset Selection

ICLR 2026poster

Supervised fine-tuning (SFT) improves large language model (LLM) perplexity but can also degrade trustworthiness—leading to the generation of untruthful, biased, or unsafe content during user interactions. These issues are often traced back to specific phrases or patterns in the training data. Howev…

Cited by 0SourceScholar
2024

Bayesian Pseudo-Coresets via Contrastive Divergence

UAI 2024poster

Bayesian methods provide an elegant framework for estimating parameter posteriors and quantification of uncertainty associated with probabilistic models. However, they often suffer from slow inference times. To address this challenge, Bayesian Pseudo-Coresets (BPC) have emerged as a promising soluti…

2024

Fusing Conditional Submodular GAN and Programmatic Weak Supervision

AAAI 2024technical

Programmatic Weak Supervision (PWS) and generative models serve as crucial tools that enable researchers to maximize the utility of existing datasets without resorting to laborious data gathering and manual annotation processes. PWS uses various weak supervision techniques to estimate the underlying…

2024

WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer

ICML 2024poster

Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers ('cell lines') is experimentally determined in laboratory…