Compression, Generalization and Learning
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Campi, Marco C., Garatti, Simone |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Risk Analysis and Design Against Adversarial Actions
par: Campi, Marco C., et autres
Publié: (2025)
par: Campi, Marco C., et autres
Publié: (2025)
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)
Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
par: Dong, Zihan, et autres
Publié: (2026)
par: Dong, Zihan, et autres
Publié: (2026)
Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
par: Zhang, Bohan, et autres
Publié: (2025)
par: Zhang, Bohan, et autres
Publié: (2025)
On the Statistical Capacity of Deep Generative Models
par: Tam, Edric, et autres
Publié: (2025)
par: Tam, Edric, 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)
Counterfactual Generative Modeling with Variational Causal Inference
par: Wu, Yulun, et autres
Publié: (2024)
par: Wu, Yulun, et autres
Publié: (2024)
Neural Networks Generalize on Low Complexity Data
par: Chatterjee, Sourav, et autres
Publié: (2024)
par: Chatterjee, Sourav, et autres
Publié: (2024)
Generalization and Scaling Laws for Mixture-of-Experts Transformers
par: Mayaki, Mansour Zoubeirou a
Publié: (2026)
par: Mayaki, Mansour Zoubeirou a
Publié: (2026)
Online Learning with Unknown Constraints
par: Sridharan, Karthik, et autres
Publié: (2024)
par: Sridharan, Karthik, 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)
Adaptive Sample Aggregation In Transfer Learning
par: Hanneke, Steve, et autres
Publié: (2024)
par: Hanneke, Steve, et autres
Publié: (2024)
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)
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)
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)
Provable Reward-Agnostic Preference-Based Reinforcement Learning
par: Zhan, Wenhao, et autres
Publié: (2023)
par: Zhan, Wenhao, et autres
Publié: (2023)
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models
par: Mei, Song
Publié: (2024)
par: Mei, Song
Publié: (2024)
Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits
par: Chen, Fan, et autres
Publié: (2025)
par: Chen, Fan, et autres
Publié: (2025)
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
par: Shen, Zhaiming, et autres
Publié: (2025)
par: Shen, Zhaiming, et autres
Publié: (2025)
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
par: Fu, Hengyu, et autres
Publié: (2024)
par: Fu, Hengyu, et autres
Publié: (2024)
Chemical Reaction Networks Learn Better than Spiking Neural Networks
par: Jaffard, Sophie, et autres
Publié: (2026)
par: Jaffard, Sophie, et autres
Publié: (2026)
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
par: Foster, Dylan J., et autres
Publié: (2024)
par: Foster, Dylan J., et autres
Publié: (2024)
Beyond identifiability: Learning causal representations with few environments and finite samples
par: Lee, Inbeom, et autres
Publié: (2026)
par: Lee, Inbeom, et autres
Publié: (2026)
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)
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
par: Chakraborty, Saptarshi, et autres
Publié: (2024)
A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment
par: Yu, Hao
Publié: (2026)
par: Yu, Hao
Publié: (2026)
Path Regularization: A Near-Complete and Optimal Nonasymptotic Generalization Theory for Multilayer Neural Networks and Double Descent Phenomenon
par: Yu, Hao
Publié: (2025)
par: Yu, Hao
Publié: (2025)
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
par: Javanmard, Adel, et autres
Publié: (2024)
par: Javanmard, Adel, et autres
Publié: (2024)
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)
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
par: Chen, Fan, et autres
Publié: (2022)
par: Chen, Fan, et autres
Publié: (2022)
Characteristic Learning for Provable One Step Generation
par: Ding, Zhao, et autres
Publié: (2024)
par: Ding, Zhao, et autres
Publié: (2024)
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
par: Hellström, Fredrik, et autres
Publié: (2023)
par: Hellström, Fredrik, et autres
Publié: (2023)
Tail-Aware Information-Theoretic Generalization for RLHF and SGLD
par: Zhang, Huiming, et autres
Publié: (2026)
par: Zhang, Huiming, et autres
Publié: (2026)
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy
par: Chen, Fan, et autres
Publié: (2025)
par: Chen, Fan, et autres
Publié: (2025)
A Score-Based Density Formula, with Applications in Diffusion Generative Models
par: Li, Gen, et autres
Publié: (2024)
par: Li, Gen, et autres
Publié: (2024)
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)
Le Cam Distortion: A Decision-Theoretic Framework for Robust Transfer Learning
par: Akdemir, Deniz
Publié: (2025)
par: Akdemir, Deniz
Publié: (2025)
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
par: Lu, Miao, et autres
Publié: (2022)
par: Lu, Miao, et autres
Publié: (2022)
Optimal rates for density and mode estimation with expand-and-sparsify representations
par: Sinha, Kaushik, et autres
Publié: (2026)
par: Sinha, Kaushik, et autres
Publié: (2026)
Documents similaires
-
Risk Analysis and Design Against Adversarial Actions
par: Campi, Marco C., et autres
Publié: (2025) -
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
par: Hazard, Christopher J., et autres
Publié: (2025) -
Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
par: Dong, Zihan, et autres
Publié: (2026) -
Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
par: Zhang, Bohan, et autres
Publié: (2025) -
On the Statistical Capacity of Deep Generative Models
par: Tam, Edric, et autres
Publié: (2025)