Cover Image

On Information-Theoretic Measures of Predictive Uncertainty

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Schweighofer, Kajetan, Aichberger, Lukas, Ielanskyi, Mykyta, Hochreiter, Sepp
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving Uncertainty Estimation through Semantically Diverse Language Generation
by: Aichberger, Lukas, et al.
Published: (2024)

Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
by: Ielanskyi, Mykyta, et al.
Published: (2025)

Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure
by: Aichberger, Lukas, et al.
Published: (2024)

The Disparate Benefits of Deep Ensembles
by: Schweighofer, Kajetan, et al.
Published: (2024)

Unlocking the Working Memory of Large Language Models for Latent Reasoning
by: Aichberger, Lukas, et al.
Published: (2026)

Uncertainty Quantification for Regression using Proper Scoring Rules
by: Fishkov, Alexander, et al.
Published: (2025)

xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
by: Beck, Maximilian, et al.
Published: (2025)

MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs
by: Bartmann, Christoph, et al.
Published: (2026)

Overcoming Forgetting in LLM Fine-Tuning with Evolution Strategies
by: Schweighofer, Kajetan, et al.
Published: (2026)

FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware
by: Pöppel, Korbinian, et al.
Published: (2024)

Efficient Pre-Training of LLMs through Truncated SVD Layers
by: Kamali, Kaivan, et al.
Published: (2026)

Rethinking Losses for Diffusion Bridge Samplers
by: Sanokowski, Sebastian, et al.
Published: (2025)

Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
by: Schweighofer, Kajetan, et al.
Published: (2025)

A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
by: Sanokowski, Sebastian, et al.
Published: (2024)

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
by: Alkin, Benedikt, et al.
Published: (2024)

Contrastive Abstraction for Reinforcement Learning
by: Patil, Vihang, et al.
Published: (2024)

Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels
by: Beck, Maximilian, et al.
Published: (2025)

Parameter Efficient Fine-tuning via Explained Variance Adaptation
by: Paischer, Fabian, et al.
Published: (2024)

Retrieval-Augmented Decision Transformer: External Memory for In-context RL
by: Schmied, Thomas, et al.
Published: (2024)

VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification
by: Sestak, Florian, et al.
Published: (2024)

Vision-LSTM: xLSTM as Generic Vision Backbone
by: Alkin, Benedikt, et al.
Published: (2024)

Loss Given Default Prediction Under Measurement-Induced Mixture Distributions: An Information-Theoretic Approach
by: Marín, Javier
Published: (2025)

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
by: Schmied, Thomas, et al.
Published: (2024)

xLSTM: Extended Long Short-Term Memory
by: Beck, Maximilian, et al.
Published: (2024)

Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents
by: Kiyani, Shayan, et al.
Published: (2025)

SymbolicAI: A framework for logic-based approaches combining generative models and solvers
by: Dinu, Marius-Constantin, et al.
Published: (2024)

Large Language Models Can Self-Improve At Web Agent Tasks
by: Patel, Ajay, et al.
Published: (2024)

Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach
by: Makonnen, Mikael, et al.
Published: (2025)

Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
by: Sanokowski, Sebastian, et al.
Published: (2025)

Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective
by: Simeone, Osvaldo, et al.
Published: (2025)

xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
by: Beck, Maximilian, et al.
Published: (2025)

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
by: Schmidinger, Niklas, et al.
Published: (2024)

From Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty
by: Jenane, Azza, et al.
Published: (2026)

Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
by: Kruse, Maya, et al.
Published: (2025)

A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
by: Hazard, Christopher J., et al.
Published: (2025)

Generalisation of Total Uncertainty in AI: A Theoretical Study
by: Shariatmadar, Keivan
Published: (2024)

CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning
by: Narava, Rahul, et al.
Published: (2026)

Probing the Information Theoretical Roots of Spatial Dependence Measures
by: Wang, Zhangyu, et al.
Published: (2024)

On the Theoretical Limitations of Embedding-based Link Prediction
by: Badreddine, Samy, et al.
Published: (2025)

Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies
by: Sahoo, Biswajeet, et al.
Published: (2026)