Cover Image

Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rauba, Paulius, van der Schaar, Mihaela
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Tiny Autoregressive Recursive Models
by: Rauba, Paulius, et al.
Published: (2026)

Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models
by: Rauba, Paulius, et al.
Published: (2024)

Visualizing token importance for black-box language models
by: Rauba, Paulius, et al.
Published: (2025)

No More, No Less: Least-Privilege Language Models
by: Rauba, Paulius, et al.
Published: (2026)

Quantifying perturbation impacts for large language models
by: Rauba, Paulius, et al.
Published: (2024)

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
by: Rauba, Paulius, et al.
Published: (2024)

Redefining Digital Health Interfaces with Large Language Models
by: Imrie, Fergus, et al.
Published: (2023)

Statistical Hypothesis Testing for Auditing Robustness in Language Models
by: Rauba, Paulius, et al.
Published: (2025)

Multi-Agent Systems Should be Treated as Principal-Agent Problems
by: Rauba, Paulius, et al.
Published: (2026)

Matchmaker: Self-Improving Large Language Model Programs for Schema Matching
by: Seedat, Nabeel, et al.
Published: (2024)

Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning
by: Sun, Hao, et al.
Published: (2024)

Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
by: Sun, Hao, et al.
Published: (2025)

Not All Explanations for Deep Learning Phenomena Are Equally Valuable
by: Jeffares, Alan, et al.
Published: (2025)

Language Bottleneck Models for Qualitative Knowledge State Modeling
by: Berthon, Antonin, et al.
Published: (2025)

Simulating Viva Voce Examinations to Evaluate Clinical Reasoning in Large Language Models
by: Chiu, Christopher, et al.
Published: (2025)

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs
by: Kacprzyk, Krzysztof, et al.
Published: (2025)

Large Language Models to Enhance Bayesian Optimization
by: Liu, Tennison, et al.
Published: (2024)

Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
by: Jeffares, Alan, et al.
Published: (2024)

Why Tabular Foundation Models Should Be a Research Priority
by: van Breugel, Boris, et al.
Published: (2024)

Preference Learning for AI Alignment: a Causal Perspective
by: Kobalczyk, Katarzyna, et al.
Published: (2025)

Active Timepoint Selection for Learning Measure-Valued Trajectories
by: Huynh, Nicolas, et al.
Published: (2026)

Causal Deep Learning
by: Berrevoets, Jeroen, et al.
Published: (2023)

Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations
by: Kacprzyk, Krzysztof, et al.
Published: (2024)

Towards Automated Knowledge Integration From Human-Interpretable Representations
by: Kobalczyk, Katarzyna, et al.
Published: (2024)

On Error Propagation of Diffusion Models
by: Li, Yangming, et al.
Published: (2023)

LaTable: Towards Large Tabular Models
by: van Breugel, Boris, et al.
Published: (2024)

Automatically Learning Hybrid Digital Twins of Dynamical Systems
by: Holt, Samuel, et al.
Published: (2024)

Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport
by: Amad, Harry, et al.
Published: (2026)

Discovery of Hidden Miscalibration Regimes
by: Kobalczyk, Katarzyna, et al.
Published: (2026)

Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs
by: Sun, Hao, et al.
Published: (2025)

Deep Generative Symbolic Regression
by: Holt, Samuel, et al.
Published: (2023)

Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples
by: Li, Yangming, et al.
Published: (2024)

Decision Tree Induction Through LLMs via Semantically-Aware Evolution
by: Liu, Tennison, et al.
Published: (2025)

What's the next frontier for Data-centric AI? Data Savvy Agents
by: Seedat, Nabeel, et al.
Published: (2025)

A Study of Posterior Stability for Time-Series Latent Diffusion
by: Li, Yangming, et al.
Published: (2024)

Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers
by: Curth, Alicia, et al.
Published: (2024)

Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
by: Seedat, Nabeel, et al.
Published: (2024)

Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
by: Melnychuk, Valentyn, et al.
Published: (2024)

Adaptive Experiment Design with Synthetic Controls
by: Hüyük, Alihan, et al.
Published: (2024)

Position: All Current Generative Fidelity and Diversity Metrics are Flawed
by: Räisä, Ossi, et al.
Published: (2025)