:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Liu, Hanwen, Ma, Yixuan, Jin, Shi, Wang, Yuguang
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence Statistics Theory
Accesso online:	https://arxiv.org/abs/2511.06044
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Stochastic Direct Search Method for Blind Resource Allocation
di: Achddou, Juliette, et al.
Pubblicazione: (2022)

Scaling Limits of Long-Context Transformers
di: Bruno, Giuseppe, et al.
Pubblicazione: (2026)

Random Multiplexing
di: Liu, Lei, et al.
Pubblicazione: (2025)

Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
di: Yvernes, Clément, et al.
Pubblicazione: (2025)

How Particle System Theory Enhances Hypergraph Message Passing
di: Ma, Yixuan, et al.
Pubblicazione: (2025)

Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models
di: Balasubramanian, Krishnakumar
Pubblicazione: (2026)

Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
di: Firdoussi, Aymane El, et al.
Pubblicazione: (2024)

Diffusion Posterior Sampling is Computationally Intractable
di: Gupta, Shivam, et al.
Pubblicazione: (2024)

Generalization and Scaling Laws for Mixture-of-Experts Transformers
di: Mayaki, Mansour Zoubeirou a
Pubblicazione: (2026)

Reject, Resample, Repeat: Understanding Parallel Reasoning in Language Model Inference
di: Golowich, Noah, et al.
Pubblicazione: (2026)

Ordinary Least Squares is a Special Case of Transformer
di: Tan, Xiaojun, et al.
Pubblicazione: (2026)

Scaling Laws in Linear Regression: Compute, Parameters, and Data
di: Lin, Licong, et al.
Pubblicazione: (2024)

A Computational Theory for Efficient Mini Agent Evaluation with Causal Guarantees
di: Yan, Hedong
Pubblicazione: (2025)

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
di: Shen, Zhaiming, et al.
Pubblicazione: (2025)

Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions
di: He, Yixuan, et al.
Pubblicazione: (2025)

Enhancing Conformal Prediction Using E-Test Statistics
di: Balinsky, A. A., et al.
Pubblicazione: (2024)

Beyond identifiability: Learning causal representations with few environments and finite samples
di: Lee, Inbeom, et al.
Pubblicazione: (2026)

Transformers Handle Endogeneity in In-Context Linear Regression
di: Liang, Haodong, et al.
Pubblicazione: (2024)

Total Variation Rates for Riemannian Flow Matching
di: Guan, Yunrui, et al.
Pubblicazione: (2026)

LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning
di: Cao, Junyu, et al.
Pubblicazione: (2026)

Sinkhorn Based Associative Memory Retrieval Using Spherical Hellinger Kantorovich Dynamics
di: Mustafi, Aratrika, et al.
Pubblicazione: (2026)

Analysis of Corrected Graph Convolutions
di: Wang, Robert, et al.
Pubblicazione: (2024)

Cost-optimal Sequential Testing via Doubly Robust Q-learning
di: Zhou, Doudou, et al.
Pubblicazione: (2026)

Distribution-consistency Structural Causal Models
di: Gong, Heyang, et al.
Pubblicazione: (2024)

About the Cost of Central Privacy in Density Estimation
di: Lalanne, Clément, et al.
Pubblicazione: (2023)

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
di: de Lara, Lucas
Pubblicazione: (2025)

A Measure-Theoretic Axiomatisation of Causality
di: Park, Junhyung, et al.
Pubblicazione: (2023)

Identifiability of total effects from abstractions of time series causal graphs
di: Assaad, Charles K., et al.
Pubblicazione: (2023)

Note on the identification of total effect in Cluster-DAGs with cycles
di: Yvernes, Clément
Pubblicazione: (2025)

Identifiability by common backdoor in summary causal graphs of time series
di: Yvernes, Clément, et al.
Pubblicazione: (2025)

Conditional Performance Guarantee for Large Reasoning Models
di: Huang, Jianguo, et al.
Pubblicazione: (2026)

Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds
di: Pariente, Yaacov, et al.
Pubblicazione: (2026)

Statistical Inference for Misspecified Contextual Bandits
di: Guo, Yongyi, et al.
Pubblicazione: (2025)

Retraining as Approximate Bayesian Inference
di: Katz, Harrison
Pubblicazione: (2026)

Physics-informed machine learning as a kernel method
di: Doumèche, Nathan, et al.
Pubblicazione: (2024)

Random Tree Model of Meaningful Memory
di: Zhong, Weishun, et al.
Pubblicazione: (2024)

The Coverage Principle: How Pre-Training Enables Post-Training
di: Chen, Fan, et al.
Pubblicazione: (2025)

Causal Digital Twins for Cyber-Physical Security: A Framework for Robust Anomaly Detection in Industrial Control Systems
di: Homaei, Mohammadhossein, et al.
Pubblicazione: (2025)

The Geometry of Knowing: From Possibilistic Ignorance to Probabilistic Certainty -- A Measure-Theoretic Framework for Epistemic Convergence
di: Jah, Moriba Kemessia
Pubblicazione: (2026)

Transport f divergences
di: Li, Wuchen
Pubblicazione: (2025)