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Kailash Budhathoki

5 accepted papers

2026

When LLMs get significantly worse: A statistical approach to detect model degradations

ICLR 2026poster

Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoretically lossless methods and others without accuracy guarantees like quantization. In all of these cases it is crucial to ensure that the model quality has not…

Cited by 0SourcecodeScholar
2025

Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving

NeurIPS 2025poster

When serving a single base LLM with several different LoRA adapters simultaneously, the adapters cannot simply be merged with the base model’s weights as the adapter swapping would create overhead and requests using different adapters could not be batched. Rather, the LoRA computations have to be se…

Cited by 0SourceScholar
2024

Quantifying intrinsic causal contributions via structure preserving interventions

AISTATS 2024poster

We propose a notion of causal influence that describes the ‘intrinsic’ part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function of the upstream noise terms, we separate the intrinsic information added by each node from the one obtained from its anc…

Cited by 11SourcePDFScholar
2022

Causal structure-based root cause analysis of outliers

ICML 2022spotlight

Current techniques for explaining outliers cannot tell what caused the outliers. We present a formal method to identify "root causes" of outliers, amongst variables. The method requires a causal graph of the variables along with the functional causal model. It quantifies the contribution of each var…