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Lukas Cavigelli

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4 papers

1 author row

NeurIPS Conference 2025 Conference Paper

AC-LoRA: (Almost) Training-Free Access Control Aware Multi-Modal LLMs

Lara Magdalena Lazier
Aritra Dhar
Vasilije Stambolic
Lukas Cavigelli

Corporate LLMs are gaining traction for efficient knowledge dissemination and management within organizations. However, as current LLMs are vulnerable to leaking sensitive information, it has proven difficult to apply them in settings where strict access control is necessary. To this end, we design AC-LoRA, an end-to-end system for access control-aware corporate LLM chatbots that maintains a strong information isolation guarantee. AC-LoRA maintains separate LoRA adapters for permissioned datasets, along with the document embedding they are finetuned on. AC-LoRA retrieves a precise set of LoRA adapters based on the similarity score with the user query and their permission. This similarity score is later used to merge the responses if more than one LoRA is retrieved, without requiring any additional training for LoRA routing. We provide an end-to-end prototype of AC-LoRA, evaluate it on two datasets, and show that AC-LoRA matches or even exceeds the performance of state-of-the-art LoRA mixing techniques while providing strong isolation guarantees. Furthermore, we show that AC-LoRA design can be directly applied to different modalities.

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TMLR Journal 2023 Journal Article

Efficient Inference With Model Cascades

Luzian Lebovitz
Lukas Cavigelli
Michele Magno
Lorenz K Muller

State-of-the-art deep learning models are becoming ever larger. However, many practical applications are constrained by the cost of inference. Cascades of pretrained models with conditional execution address these requirements based on the intuition that some inputs are easy enough that they can be processed correctly by a smaller model allowing for an early exit. If the smaller model is not sufficiently confident in its prediction, the input is passed on to a larger model. The selection of the confidence threshold allows to trade off computational cost against accuracy. In this work we explore the effective design of model cascades, thoroughly evaluate the impact on the accuracy-efficiency trade-off, and provide a reproducible state-of-the-art baseline that is currently missing for related research. We demonstrate that model cascades dominate the ImageNet Pareto front already with 2-model cascades, achieving an average reduction in compute effort at equal accuracy of almost 3.1x above 86% and more than 1.9x between 80% and 86% top-1 accuracy, while 3-model cascades achieve 4.4x above 87% accuracy. We confirm wider applicability and effectiveness of the method on the GLUE benchmark. We release the code to reproduce our experiments in the supplementary material and use only publicly available pretrained models and datasets.

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NeurIPS Conference 2023 Conference Paper

RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing

Antoine Scardigli
Lukas Cavigelli
Lorenz K. Müller

Monte-Carlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts, limiting its use in real-time applications. To address this, we propose a framework with end-to-end training of a sampling importance network, a latent space encoder network, and a denoiser network. Our approach uses reinforcement learning to optimize the sampling importance network, thus avoiding explicit numerically approximated gradients. Our method does not aggregate the sampled values per pixel by averaging but keeps all sampled values which are then fed into the latent space encoder. The encoder replaces handcrafted spatiotemporal heuristics by learned representations in a latent space. Finally, a neural denoiser is trained to refine the output image. Our approach increases visual quality on several challenging datasets and reduces rendering times for equal quality by a factor of 1. 6x compared to the previous state-of-the-art, making it a promising solution for real-time applications.

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NeurIPS Conference 2017 Conference Paper

Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations

Eirikur Agustsson
Fabian Mentzer
Michael Tschannen
Lukas Cavigelli
Radu Timofte
Luca Benini
Luc Gool

We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image compression and neural network compression. While these tasks have typically been approached with different methods, our soft-to-hard quantization approach gives results competitive with the state-of-the-art for both.

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