Nathalie Rauschmayr

According to our database, Nathalie Rauschmayr authored at least 15 papers between 2012 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models.

Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr, Federico Tombari, Bernt Schiele

CoRR, April, 2026

R-CoV: Region-Aware Chain-of-Verification for Alleviating Object Hallucinations in LVLMs.

Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr, Federico Tombari, Bernt Schiele

CoRR, April, 2026

2025

Reading Between the Lines: Abstaining from VLM-Generated OCR Errors via Latent Representation Probes.

Jihan Yao, Achin Kulshrestha, Nathalie Rauschmayr, Reed Roberts, Banghua Zhu, Yulia Tsvetkov, Federico Tombari

CoRR, November, 2025

I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning.

Stephan Rabanser, Nathalie Rauschmayr, Achin Kulshrestha, Petra Poklukar, Wittawat Jitkrittum, Sean Augenstein, Congchao Wang, Federico Tombari

CoRR, February, 2025

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence.

Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich, Achin Kulshrestha, Nathalie Rauschmayr, Yejin Choi, Yulia Tsvetkov, Chen-Yu Lee, Tomas Pfister

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Test-Time Visual In-Context Tuning.

Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr, Federico Tombari, Bernt Schiele

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

2024

Extracting Training Data From Document-Based VQA Models.

Francesco Pinto, Nathalie Rauschmayr, Florian Tramèr, Philip Torr, Federico Tombari

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2022

Profiling Deep Learning Workloads at Scale using Amazon SageMaker.

Nathalie Rauschmayr, Sami Kama, Muhyun Kim, Miyoung Choi, Krishnaram Kenthapadi

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Generating Distributional Adversarial Examples to Evade Statistical Detectors.

Yigitcan Kaya, Muhammad Bilal Zafar, Sergül Aydöre, Nathalie Rauschmayr, Krishnaram Kenthapadi

Proceedings of the International Conference on Machine Learning, 2022

2021

Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy.

Dylan Slack, Nathalie Rauschmayr, Krishnaram Kenthapadi

CoRR, 2021

Amazon SageMaker Debugger: A System for Real-Time Insights into Machine Learning Model Training.

Nathalie Rauschmayr, Vikas Kumar, Rahul Huilgol, Andrea Olgiati, Satadal Bhattacharjee, Nihal Harish, Vandana Kannan, Amol Lele, Anirudh Acharya, Jared Nielsen, Lakshmi Ramakrishnan, Ishan Bhatt, Kohen Chia, Neelesh Dodda, Zhihan Li, Jiacheng Gu, Miyoung Choi, Balajee Nagarajan, Jeffrey Geevarghese, Denis Davydenko, Sifei Li, Lu Huang, Edward Kim, Tyler Hill, Krishnaram Kenthapadi

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

2014

Optimisation of LHCb Applications for Multi- and Manycore Job Submission.

Nathalie Rauschmayr

PhD thesis, 2014

2013

Evaluating Moldability of LHCb Jobs for Multicore Job Submission.

Nathalie Rauschmayr, Achim Streit

Proceedings of the 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2013

Evaluation of x32-ABI in the Context of LHC Applications.

Nathalie Rauschmayr, Achim Streit

Proceedings of the International Conference on Computational Science, 2013

2012

Reducing the Memory Footprint of Parallel Applications with KSM.

Nathalie Rauschmayr, Achim Streit

Proceedings of the Facing the Multicore-Challenge, 2012