High-arity Sample Compression
Fuente:
arXiv
Saved in:
| Main Authors: | Coregliano, Leonardo N., Opich, William |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
High-arity PAC learning via exchangeability
by: Coregliano, Leonardo N., et al.
Published: (2024)
by: Coregliano, Leonardo N., et al.
Published: (2024)
A packing lemma for VCN${}_k$-dimension and learning high-dimensional data
by: Coregliano, Leonardo N., et al.
Published: (2025)
by: Coregliano, Leonardo N., et al.
Published: (2025)
Sample completion, structured correlation, and Netflix problems
by: Coregliano, Leonardo N., et al.
Published: (2025)
by: Coregliano, Leonardo N., et al.
Published: (2025)
Learning Decentralized Swarms Using Rotation Equivariant Graph Neural Networks
by: Transue, Taos, et al.
Published: (2025)
by: Transue, Taos, et al.
Published: (2025)
Teaching and Learning under Deductive Errors
by: Telle, Jan Arne, et al.
Published: (2026)
by: Telle, Jan Arne, et al.
Published: (2026)
A Special Case of Quadratic Extrapolation Under the Neural Tangent Kernel
by: Kim, Abiel
Published: (2025)
by: Kim, Abiel
Published: (2025)
Conformal e-prediction
by: Vovk, Vladimir
Published: (2020)
by: Vovk, Vladimir
Published: (2020)
Golden Handcuffs make safer AI agents
by: Ebtekar, Aram, et al.
Published: (2026)
by: Ebtekar, Aram, et al.
Published: (2026)
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
by: Dramko, Evan, et al.
Published: (2025)
by: Dramko, Evan, et al.
Published: (2025)
Inductive randomness predictors: beyond conformal
by: Vovk, Vladimir
Published: (2025)
by: Vovk, Vladimir
Published: (2025)
The Role of Randomness in Stability
by: Hopkins, Max, et al.
Published: (2025)
by: Hopkins, Max, et al.
Published: (2025)
Do PAC-Learners Learn the Marginal Distribution?
by: Hopkins, Max, et al.
Published: (2023)
by: Hopkins, Max, et al.
Published: (2023)
Realizable Learning is All You Need
by: Hopkins, Max, et al.
Published: (2021)
by: Hopkins, Max, et al.
Published: (2021)
On Reductions and Representations of Learning Problems in Euclidean Spaces
by: Chornomaz, Bogdan, et al.
Published: (2024)
by: Chornomaz, Bogdan, et al.
Published: (2024)
Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs
by: Khavari, Behnoush, et al.
Published: (2025)
by: Khavari, Behnoush, et al.
Published: (2025)
Randomness, exchangeability, and conformal prediction
by: Vovk, Vladimir
Published: (2025)
by: Vovk, Vladimir
Published: (2025)
The Price of Robustness: Stable Classifiers Need Overparameterization
by: von Berg, Jonas, et al.
Published: (2026)
by: von Berg, Jonas, et al.
Published: (2026)
Bounds on the Generalization Error in Active Learning
by: Menden, Vincent, et al.
Published: (2024)
by: Menden, Vincent, et al.
Published: (2024)
Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration
by: Gopalan, Parikshit, et al.
Published: (2023)
by: Gopalan, Parikshit, et al.
Published: (2023)
Conformal e-prediction in the presence of confounding
by: Vovk, Vladimir, et al.
Published: (2026)
by: Vovk, Vladimir, et al.
Published: (2026)
Universality of conformal prediction under the assumption of randomness
by: Vovk, Vladimir
Published: (2025)
by: Vovk, Vladimir
Published: (2025)
Neural Network Approximation: A View from Polytope Decomposition
by: Li, ZeYu, et al.
Published: (2026)
by: Li, ZeYu, et al.
Published: (2026)
The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
by: Azizian, Waïss, et al.
Published: (2022)
by: Azizian, Waïss, et al.
Published: (2022)
Inductive Venn-Abers and related regressors
by: Petej, Ivan, et al.
Published: (2026)
by: Petej, Ivan, et al.
Published: (2026)
Aggregation in conformal e-classification
by: Vovk, Vladimir
Published: (2026)
by: Vovk, Vladimir
Published: (2026)
Recursively Enumerably Representable Classes and Computable Versions of the Fundamental Theorem of Statistical Learning
by: Kattermann, David, et al.
Published: (2025)
by: Kattermann, David, et al.
Published: (2025)
Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning
by: Chavooshi, Malihehsadat, et al.
Published: (2025)
by: Chavooshi, Malihehsadat, et al.
Published: (2025)
Asymptotic Optimism of Random-Design Linear and Kernel Regression Models
by: Luo, Hengrui, et al.
Published: (2025)
by: Luo, Hengrui, et al.
Published: (2025)
Adaptive learning of density ratios in RKHS
by: Zellinger, Werner, et al.
Published: (2023)
by: Zellinger, Werner, et al.
Published: (2023)
Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study
by: Keraghel, Imed, et al.
Published: (2024)
by: Keraghel, Imed, et al.
Published: (2024)
Grouped Sequential Optimization Strategy -- the Application of Hyperparameter Importance Assessment in Deep Learning
by: Wang, Ruinan, et al.
Published: (2025)
by: Wang, Ruinan, et al.
Published: (2025)
Discrete Diffusion Models for Language Generation
by: Weligalle, Ashen
Published: (2025)
by: Weligalle, Ashen
Published: (2025)
Approximation and generalization properties of the random projection classification method
by: Boutin, Mireille, et al.
Published: (2021)
by: Boutin, Mireille, et al.
Published: (2021)
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model Compression
by: Ilin, Ivan, et al.
Published: (2025)
by: Ilin, Ivan, et al.
Published: (2025)
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
by: Hurault, Samuel, et al.
Published: (2025)
by: Hurault, Samuel, et al.
Published: (2025)
Make Optimization Once and for All with Fine-grained Guidance
by: Shi, Mingjia, et al.
Published: (2025)
by: Shi, Mingjia, et al.
Published: (2025)
Domain Generalization by Functional Regression
by: Holzleitner, Markus, et al.
Published: (2023)
by: Holzleitner, Markus, et al.
Published: (2023)
Boosting Test Performance with Importance Sampling--a Subpopulation Perspective
by: Shen, Hongyu, et al.
Published: (2024)
by: Shen, Hongyu, et al.
Published: (2024)
Random feature-based double Vovk-Azoury-Warmuth algorithm for online multi-kernel learning
by: Rokhlin, Dmitry B., et al.
Published: (2025)
by: Rokhlin, Dmitry B., et al.
Published: (2025)
A hierarchical Vovk-Azoury-Warmuth forecaster with discounting for online regression in RKHS
by: Rokhlin, Dmitry B.
Published: (2025)
by: Rokhlin, Dmitry B.
Published: (2025)
Similar Items
-
High-arity PAC learning via exchangeability
by: Coregliano, Leonardo N., et al.
Published: (2024) -
A packing lemma for VCN${}_k$-dimension and learning high-dimensional data
by: Coregliano, Leonardo N., et al.
Published: (2025) -
Sample completion, structured correlation, and Netflix problems
by: Coregliano, Leonardo N., et al.
Published: (2025) -
Learning Decentralized Swarms Using Rotation Equivariant Graph Neural Networks
by: Transue, Taos, et al.
Published: (2025) -
Teaching and Learning under Deductive Errors
by: Telle, Jan Arne, et al.
Published: (2026)