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Jonas M. Kübler

6 accepted papers

2026

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

Jonas M. Kübler, Kailash Budhathoki, Matthäus Kleindessner, Xiong Zhou, Junming Yin, Ashish Khetan +1
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 0Source ↗code ↗Scholar ↗
2025

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

Xinyu Wang, Jonas M. Kübler, Kailash Budhathoki, Yida Wang, Matthäus Kleindessner
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 0Source ↗Scholar ↗
2025

Wanda++: Pruning Large Language Models via Regional Gradients

Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan, Goeric Huybrechts, Markus Müller +8
ACL 2025finding

Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy degradation without full-model sparsity-aware fine-tuning. This paper presents Wanda++, a novel pruning framework that out…

Cited by 0Source ↗PDF ↗Scholar ↗
2022

A Witness Two-Sample Test

Jonas M. Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet
AISTATS 2022poster

The Maximum Mean Discrepancy (MMD) has been the state-of-the-art nonparametric test for tackling the two-sample problem. Its statistic is given by the difference in expectations of the witness function, a real-valued function defined as a weighted sum of kernel evaluations on a set of basis points.…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗
2022

AutoML Two-Sample Test

Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf
NeurIPS 2022accept

Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of many sophisticated test procedures going beyond the standard supervised learning frameworks, whose usage can require spec…

Cited by 26Source ↗PDF ↗Scholar ↗