Copertina

When does Subagging Work?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Revelas, Christos, Boldea, Otilia, Werker, Bas J. M.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Consistency of Selection Strategies for Fraud Detection
di: Revelas, Christos, et al.
Pubblicazione: (2025)

When do Random Forests work?
di: Revelas, C., et al.
Pubblicazione: (2025)

Exact Finite-Sample Variance Decomposition of Subagging: A Spectral Filtering Perspective
di: Su, Ye, et al.
Pubblicazione: (2026)

FUSE : Failure-aware Usage of Subagent Evidence for MultiModal Search and Recommendation
di: Vatsa, Tushar, et al.
Pubblicazione: (2025)

When does a bridge become an aeroplane?
di: Dardeno, Tina A., et al.
Pubblicazione: (2024)

Targeted Local Projections
di: Nemtyrev, Aleksei, et al.
Pubblicazione: (2026)

When does a predictor know its own loss?
di: Gollakota, Aravind, et al.
Pubblicazione: (2025)

When does Chain-of-Thought Help: A Markovian Perspective
di: Wang, Zihan, et al.
Pubblicazione: (2026)

Why Loss Re-weighting Works If You Stop Early: Training Dynamics of Unconstrained Features
di: Zhao, Yize, et al.
Pubblicazione: (2026)

When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
di: Voelcker, Claas, et al.
Pubblicazione: (2024)

Testing for multiple change-points in macroeconometrics: an empirical guide and recent developments
di: Boldea, Otilia, et al.
Pubblicazione: (2025)

When does compositional structure yield compositional generalization? A kernel theory
di: Lippl, Samuel, et al.
Pubblicazione: (2024)

Semantics at an Angle: When Cosine Similarity Works Until It Doesn't
di: You, Kisung
Pubblicazione: (2025)

When Molecular Similarity Works: Property Cliffs Reveal Hidden Errors
di: Hu, Di, et al.
Pubblicazione: (2026)

Assessing Gender Bias in LLMs: Comparing LLM Outputs with Human Perceptions and Official Statistics
di: Bas, Tetiana
Pubblicazione: (2024)

When does Gaussian equivalence fail and how to fix it: Non-universal behavior of random features with quadratic scaling
di: Wen, Garrett G., et al.
Pubblicazione: (2025)

Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
di: Korkmaz, Buse Sibel, et al.
Pubblicazione: (2025)

Efficient two-sample instrumental variable estimators with change points and near-weak identification
di: Antoine, Bertille, et al.
Pubblicazione: (2024)

Demystifying When Pruning Works via Representation Hierarchies
di: He, Shwai, et al.
Pubblicazione: (2026)

Predicting integers from continuous parameters
di: Maat, Bas, et al.
Pubblicazione: (2026)

When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
di: Muller, Christophe, et al.
Pubblicazione: (2025)

Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
di: Nagaswetha, Kumbha, et al.
Pubblicazione: (2026)

When does Metropolized Hamiltonian Monte Carlo provably outperform Metropolis-adjusted Langevin algorithm?
di: Chen, Yuansi, et al.
Pubblicazione: (2023)

When does the mean network capture the topology of a sample of networks?
di: Meyer, François G
Pubblicazione: (2024)

Maximal Ancillarity, Semiparametric Efficiency, and the Elimination of Nuisances
di: Hallin, Marc, et al.
Pubblicazione: (2026)

Text Classification: Neural Networks VS Machine Learning Models VS Pre-trained Models
di: Petridis, Christos
Pubblicazione: (2024)

Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Features
di: Mountzouris, Christos
Pubblicazione: (2025)

Pando: Do Interpretability Methods Work When Models Won't Explain Themselves?
di: Zhong, Ziqian, et al.
Pubblicazione: (2026)

When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
di: Petrov, Aleksandar, et al.
Pubblicazione: (2023)

How does over-squashing affect the power of GNNs?
di: Di Giovanni, Francesco, et al.
Pubblicazione: (2023)

Small Batch Size Training for Language Models: When Vanilla SGD Works, and Why Gradient Accumulation Is Wasteful
di: Marek, Martin, et al.
Pubblicazione: (2025)

When Simple Model Just Works: Is Network Traffic Classification in Crisis?
di: Jerabek, Kamil, et al.
Pubblicazione: (2025)

How does Your RL Agent Explore? An Optimal Transport Analysis of Occupancy Measure Trajectories
di: Nkhumise, Reabetswe M., et al.
Pubblicazione: (2024)

Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training
di: Matsutani, Kohsei, et al.
Pubblicazione: (2026)

Why does Knowledge Distillation Work? Rethink its Attention and Fidelity Mechanism
di: Guo, Chenqi, et al.
Pubblicazione: (2024)

When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework
di: Xu, Zhen, et al.
Pubblicazione: (2025)

Prototypical Contrastive Learning For Improved Few-Shot Audio Classification
di: Sgouropoulos, Christos, et al.
Pubblicazione: (2025)

All that structure matches does not glitter
di: Martirossyan, Maya M., et al.
Pubblicazione: (2025)

Batch normalization does not improve initialization
di: Dannemann, Joris, et al.
Pubblicazione: (2025)

What model does MuZero learn?
di: He, Jinke, et al.
Pubblicazione: (2023)