23 citations · 35 across the 8 of their papers we have counts for
5 papers · 1 filter
Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving
Xinyu Wang, Jonas M. Kübler, Kailash Budhathoki +2
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…
A Proximal Operator for Inducing 2:4-Sparsity
Jonas M Kübler, Yu-Xiang Wang, Shoham Sabach +5
Recent hardware advancements in AI Accelerators and GPUs allow to efficiently compute sparse matrix multiplications, especially when 2 out of 4 consecutive weights are set to zero.…
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models
David Hoffmann, Kailash Budhathoki, Matthaeus Kleindessner
The evolving capabilities of large language models are accompanied by growing sizes and deployment costs, necessitating effective inference optimisation techniques. We propose a no…
Discovering Reliable Causal Rules
Kailash Budhathoki, Mario Boley, Jilles Vreeken
We study the problem of deriving policies, or rules, that when enacted on a complex system, cause a desired outcome. Absent the ability to perform controlled experiments, such rule…
Causal Inference by Stochastic Complexity
Kailash Budhathoki, Jilles Vreeken
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov co…