Image de couverture de livre

Note on Martingale Theory and Applications

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Zou, Xiandong
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence Probability
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Unified Theory of $θ$-Expectations
par: Qi, Qian
Publié: (2025)

A Mean-Field Theory of $Θ$-Expectations
par: Qi, Qian
Publié: (2025)

A Score-Based Density Formula, with Applications in Diffusion Generative Models
par: Li, Gen, et autres
Publié: (2024)

$α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
par: Schnoor, Ekkehard, et autres
Publié: (2026)

Soft-to-Hard Routing in Sparse Mixture-of-Experts Models
par: Rastegar, Reza
Publié: (2026)

On the Equivalence of Random Network Distillation, Deep Ensembles, and Bayesian Inference
par: Zanger, Moritz A., et autres
Publié: (2026)

Neural Network Parameter-optimization of Gaussian pmDAGs
par: Saremi, Mehrzad
Publié: (2023)

Deep Conditional Measure Quantization
par: Turinici, Gabriel
Publié: (2023)

Feature Learning Dynamics in Infinite-Depth Neural Networks
par: Yao, Zihan, et autres
Publié: (2025)

Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
par: Kivva, Yaroslav, et autres
Publié: (2025)

Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
par: de Frutos, José Manuel, et autres
Publié: (2025)

Nonparametric Identification of Latent Concepts
par: Zheng, Yujia, et autres
Publié: (2025)

Representative Arm Identification: A fixed confidence approach to identify cluster representatives
par: Gharat, Sarvesh, et autres
Publié: (2024)

Greedy Selection under Independent Increments: A Toy Model Analysis
par: Yang, Huitao
Publié: (2025)

Neural Expectation Operators
par: Qi, Qian
Publié: (2025)

Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI
par: Zhang, Jianyi
Publié: (2025)

Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
par: Tankala, Chandan, et autres
Publié: (2025)

Efficient Training of Neural SDEs Using Stochastic Optimal Control
par: Daems, Rembert, et autres
Publié: (2025)

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

Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning
par: Kim, Seyeon, et autres
Publié: (2024)

Quantitative CLTs in Deep Neural Networks
par: Favaro, Stefano, et autres
Publié: (2023)

Neural Laplace for learning Stochastic Differential Equations
par: Carrel, Adrien
Publié: (2024)

IBNorm: Information-Bottleneck Inspired Normalization for Representation Learning
par: Zou, Xiandong, et autres
Publié: (2025)

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
par: Jentzen, Arnulf, et autres
Publié: (2023)

Bayesian Inference with Deep Weakly Nonlinear Networks
par: Hanin, Boris, et autres
Publié: (2024)

Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing
par: Ansah-Narh, T., et autres
Publié: (2026)

Selective Reviews of Bandit Problems in AI via a Statistical View
par: Zhou, Pengjie, et autres
Publié: (2024)

TowerDebias: A Novel Unfairness Removal Method Based on the Tower Property
par: Matloff, Norman, et autres
Publié: (2024)

Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
par: Nguyen, Hai-Vy, et autres
Publié: (2024)

Will More Expressive Graph Neural Networks do Better on Generative Tasks?
par: Zou, Xiandong, et autres
Publié: (2023)

On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note
par: Zou, Guangyi, et autres
Publié: (2026)

Graphical Models for Decision-Making: Integrating Causality and Game Theory
par: Vonk, Maarten C., et autres
Publié: (2025)

Martingale-Consistent Self-Supervised Learning
par: Gögl, Moritz, et autres
Publié: (2026)

Scaling Limits of Long-Context Transformers
par: Bruno, Giuseppe, et autres
Publié: (2026)

Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models
par: Aueawatthanaphisut, Aueaphum, et autres
Publié: (2026)

A UCB Bandit Algorithm for General ML-Based Estimators
par: Liu, Yajing, et autres
Publié: (2026)

Tail-Aware Information-Theoretic Generalization for RLHF and SGLD
par: Zhang, Huiming, et autres
Publié: (2026)

Neural Brownian Motion
par: Qi, Qian
Publié: (2025)

Feature-aligned N-BEATS with Sinkhorn divergence
par: Lee, Joonhun, et autres
Publié: (2023)

Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
par: Neufeld, Ariel, et autres
Publié: (2022)