A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Chakraborty, Saptarshi, Bartlett, Peter L. |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2026)
par: Chakraborty, Saptarshi, et autres
Publié: (2026)
Low-Dimensional Adaptation of Rectified Flow: A Diffusion and Stochastic Localization Perspective
par: Roy, Saptarshi, et autres
Publié: (2026)
par: Roy, Saptarshi, et autres
Publié: (2026)
Scaling Laws in Linear Regression: Compute, Parameters, and Data
par: Lin, Licong, et autres
Publié: (2024)
par: Lin, Licong, et autres
Publié: (2024)
On the Statistical Capacity of Deep Generative Models
par: Tam, Edric, et autres
Publié: (2025)
par: Tam, Edric, et autres
Publié: (2025)
Cyclic Counterfactuals under Shift-Scale Interventions
par: Saha, Saptarshi, et autres
Publié: (2025)
par: Saha, Saptarshi, et autres
Publié: (2025)
Neural Networks Generalize on Low Complexity Data
par: Chatterjee, Sourav, et autres
Publié: (2024)
par: Chatterjee, Sourav, et autres
Publié: (2024)
Statistical inference with belief functions: A survey
par: Cuzzolin, Fabio
Publié: (2026)
par: Cuzzolin, Fabio
Publié: (2026)
Enhancing Conformal Prediction Using E-Test Statistics
par: Balinsky, A. A., et autres
Publié: (2024)
par: Balinsky, A. A., et autres
Publié: (2024)
Training Implicit Generative Models via an Invariant Statistical Loss
par: de Frutos, José Manuel, et autres
Publié: (2024)
par: de Frutos, José Manuel, et autres
Publié: (2024)
Solving a Research Problem in Mathematical Statistics with AI Assistance
par: Dobriban, Edgar
Publié: (2025)
par: Dobriban, Edgar
Publié: (2025)
Statistical and Algorithmic Foundations of Reinforcement Learning
par: Chi, Yuejie, et autres
Publié: (2025)
par: Chi, Yuejie, et autres
Publié: (2025)
Deep Ensembles for Epistemic Uncertainty: A Frequentist Perspective
par: Jain, Anchit, et autres
Publié: (2025)
par: Jain, Anchit, et autres
Publié: (2025)
A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
par: Letteri, Ivan
Publié: (2025)
par: Letteri, Ivan
Publié: (2025)
Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
par: Zhao, Qingyue, et autres
Publié: (2025)
par: Zhao, Qingyue, et autres
Publié: (2025)
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models
par: Li, Gen, et autres
Publié: (2024)
par: Li, Gen, et autres
Publié: (2024)
A Diffusion Analysis of Policy Gradient for Stochastic Bandits
par: Lattimore, Tor
Publié: (2026)
par: Lattimore, Tor
Publié: (2026)
CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
par: Ota, Hirofumi, et autres
Publié: (2026)
par: Ota, Hirofumi, et autres
Publié: (2026)
Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives
par: Balakrishnan, Sivaraman, et autres
Publié: (2025)
par: Balakrishnan, Sivaraman, et autres
Publié: (2025)
Towards Bayesian Data Selection
par: Rodemann, Julian
Publié: (2024)
par: Rodemann, Julian
Publié: (2024)
FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits
par: Chakraborty, Sunrit, et autres
Publié: (2024)
par: Chakraborty, Sunrit, et autres
Publié: (2024)
Improved Scaling Laws in Linear Regression via Data Reuse
par: Lin, Licong, et autres
Publié: (2025)
par: Lin, Licong, et autres
Publié: (2025)
Risk Analysis and Design Against Adversarial Actions
par: Campi, Marco C., et autres
Publié: (2025)
par: Campi, Marco C., et autres
Publié: (2025)
Convergence of Shallow ReLU Networks on Weakly Interacting Data
par: Dana, Léo, et autres
Publié: (2025)
par: Dana, Léo, et autres
Publié: (2025)
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
par: Rajendran, Goutham, et autres
Publié: (2024)
par: Rajendran, Goutham, et autres
Publié: (2024)
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
par: Firdoussi, Aymane El, et autres
Publié: (2024)
par: Firdoussi, Aymane El, et autres
Publié: (2024)
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
par: Hazard, Christopher J., et autres
Publié: (2025)
par: Hazard, Christopher J., et autres
Publié: (2025)
Compression, Generalization and Learning
par: Campi, Marco C., et autres
Publié: (2023)
par: Campi, Marco C., et autres
Publié: (2023)
Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
par: Dong, Zihan, et autres
Publié: (2026)
par: Dong, Zihan, et autres
Publié: (2026)
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
par: Frei, Spencer, et autres
Publié: (2022)
par: Frei, Spencer, et autres
Publié: (2022)
Online Learning with Unknown Constraints
par: Sridharan, Karthik, et autres
Publié: (2024)
par: Sridharan, Karthik, et autres
Publié: (2024)
Adaptive Sample Aggregation In Transfer Learning
par: Hanneke, Steve, et autres
Publié: (2024)
par: Hanneke, Steve, et autres
Publié: (2024)
Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
par: Wang, Kevin, et autres
Publié: (2026)
par: Wang, Kevin, et autres
Publié: (2026)
Conformal Prediction for Privacy-Preserving Machine Learning
par: Balinsky, Alexander David, et autres
Publié: (2025)
par: Balinsky, Alexander David, et autres
Publié: (2025)
Learning with Differentially Private (Sliced) Wasserstein Gradients
par: Rodríguez-Vítores, David, et autres
Publié: (2025)
par: Rodríguez-Vítores, David, et autres
Publié: (2025)
A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models
par: Kumar, Shivam, et autres
Publié: (2024)
par: Kumar, Shivam, et autres
Publié: (2024)
Provable Reward-Agnostic Preference-Based Reinforcement Learning
par: Zhan, Wenhao, et autres
Publié: (2023)
par: Zhan, Wenhao, et autres
Publié: (2023)
Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
par: Hu, Xinyang, et autres
Publié: (2024)
par: Hu, Xinyang, et autres
Publié: (2024)
Documents similaires
-
On the Statistical Properties of Generative Adversarial Models for Low Intrinsic Data Dimension
par: Chakraborty, Saptarshi, et autres
Publié: (2024) -
A Statistical Analysis for Supervised Deep Learning with Exponential Families for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2024) -
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2024) -
Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2026) -
Low-Dimensional Adaptation of Rectified Flow: A Diffusion and Stochastic Localization Perspective
par: Roy, Saptarshi, et autres
Publié: (2026)