:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Blaas, Arno, Goliński, Adam, Miller, Andrew, Zappella, Luca, Jacobsen, Jörn-Henrik, Heinze-Deml, Christina
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2410.19575
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts
by: Crabbé, Jonathan, et al.
Published: (2023)

Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
by: Santilli, Andrea, et al.
Published: (2025)

SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
by: Kirchhof, Michael, et al.
Published: (2025)

GenCtrl -- A Formal Controllability Toolkit for Generative Models
by: Cheng, Emily, et al.
Published: (2026)

Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
by: Gamella, Juan L., et al.
Published: (2022)

Anti-causal domain generalization: Leveraging unlabeled data
by: Saengkyongam, Sorawit, et al.
Published: (2026)

Controlling Language and Diffusion Models by Transporting Activations
by: Rodriguez, Pau, et al.
Published: (2024)

Uncertainty Quantification for LLM Function-Calling
by: Ye, Zihuiwen, et al.
Published: (2026)

Adversarial Robustness in Distributed Quantum Machine Learning
by: Kananian, Pouya, et al.
Published: (2025)

Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters
by: Palumbo, Emanuele, et al.
Published: (2025)

Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs
by: Wang, Yinong Oliver, et al.
Published: (2025)

Adversarial Robustness of Partitioned Quantum Classifiers
by: Kananian, Pouya, et al.
Published: (2025)

Simulation-based Inference for Cardiovascular Models
by: Wehenkel, Antoine, et al.
Published: (2023)

Sparse Autoencoders are Capable LLM Jailbreak Mitigators
by: Assogba, Yannick, et al.
Published: (2026)

LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss
by: Rodriguez, Pau, et al.
Published: (2025)

An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
by: Merkel, Nikolai, et al.
Published: (2023)

Federated Computing -- Survey on Building Blocks, Extensions and Systems
by: Schwermer, René, et al.
Published: (2024)

Foundation Model's Embedded Representations May Detect Distribution Shift
by: Vargas, Max, et al.
Published: (2023)

MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System
by: Mulitze, Felix, et al.
Published: (2025)

MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees
by: Zhang, Ryan, et al.
Published: (2024)

HyperTransport: Amortized Conditioning of T2I Generative Models
by: Maiorca, Valentino, et al.
Published: (2026)

Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare
by: Miller, Elizabeth W., et al.
Published: (2026)

CoRet: Improved Retriever for Code Editing
by: Fehr, Fabio, et al.
Published: (2025)

An Analysis of Model Robustness across Concurrent Distribution Shifts
by: Jeon, Myeongho, et al.
Published: (2025)

Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference
by: Behrmann, Jens, et al.
Published: (2025)

The Design Space of Tri-Modal Masked Diffusion Models
by: Bethune, Louis, et al.
Published: (2026)

ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models
by: Danieli, Federico, et al.
Published: (2025)

Clinicians' Voice: Fundamental Considerations for XAI in Healthcare
by: Röber, T. E., et al.
Published: (2024)

Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
by: Manduchi, Laura, et al.
Published: (2024)

Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
by: Merkel, Nikolai, et al.
Published: (2024)

Bias after Prompting: Persistent Discrimination in Large Language Models
by: Sivakumar, Nivedha, et al.
Published: (2025)

Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs
by: Nakkiran, Preetum, et al.
Published: (2025)

Online Linear Regression in Dynamic Environments via Discounting
by: Jacobsen, Andrew, et al.
Published: (2024)

Do LLMs estimate uncertainty well in instruction-following?
by: Heo, Juyeon, et al.
Published: (2024)

LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
by: Chen, Chacha, et al.
Published: (2026)

Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance
by: Wang, Shiqiang, et al.
Published: (2026)

Distributionally Robust Coreset Selection under Covariate Shift
by: Tanaka, Tomonari, et al.
Published: (2025)

Testable Learning with Distribution Shift
by: Klivans, Adam R., et al.
Published: (2023)

Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts
by: Najafi, Amir, et al.
Published: (2024)

Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications
by: Flovik, Vegard
Published: (2024)