Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Sakaue, Shinsaku, Oki, Taihei |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
par: Oki, Taihei, et autres
Publié: (2026)
par: Oki, Taihei, et autres
Publié: (2026)
No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and Hardness of Adversarial Full-Information Setting
par: Oki, Taihei, et autres
Publié: (2024)
par: Oki, Taihei, et autres
Publié: (2024)
Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound
par: Sakaue, Shinsaku, et autres
Publié: (2025)
par: Sakaue, Shinsaku, et autres
Publié: (2025)
Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
par: Sakaue, Shinsaku, et autres
Publié: (2024)
par: Sakaue, Shinsaku, et autres
Publié: (2024)
Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness
par: Sakaue, Shinsaku
Publié: (2026)
par: Sakaue, Shinsaku
Publié: (2026)
From Average Sensitivity to Small-Loss Regret Bounds under Random-Order Model
par: Sakaue, Shinsaku, et autres
Publié: (2026)
par: Sakaue, Shinsaku, et autres
Publié: (2026)
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel$-$Young Loss Perspective and Gap-Dependent Regret Analysis
par: Sakaue, Shinsaku, et autres
Publié: (2025)
par: Sakaue, Shinsaku, et autres
Publié: (2025)
Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses
par: Bao, Han, et autres
Publié: (2025)
par: Bao, Han, et autres
Publié: (2025)
Non-Stationary Online Structured Prediction with Surrogate Losses
par: Sakaue, Shinsaku, et autres
Publié: (2025)
par: Sakaue, Shinsaku, et autres
Publié: (2025)
Bandit and Delayed Feedback in Online Structured Prediction
par: Shibukawa, Yuki, et autres
Publié: (2025)
par: Shibukawa, Yuki, et autres
Publié: (2025)
Structural Preprocessing Method for Nonlinear Differential-Algebraic Equations Using Linear Symbolic Matrices
par: Oki, Taihei, et autres
Publié: (2024)
par: Oki, Taihei, et autres
Publié: (2024)
Algebraic Algorithms for Fractional Linear Matroid Parity via Non-commutative Rank
par: Oki, Taihei, et autres
Publié: (2022)
par: Oki, Taihei, et autres
Publié: (2022)
Generalizing the Multiple Exchange Property for Matroid Bases
par: Oki, Taihei, et autres
Publié: (2025)
par: Oki, Taihei, et autres
Publié: (2025)
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
par: Scavuzzo, Lara, et autres
Publié: (2024)
par: Scavuzzo, Lara, et autres
Publié: (2024)
Fractional Linear Matroid Matching is in quasi-NC
par: Gurjar, Rohit, et autres
Publié: (2024)
par: Gurjar, Rohit, et autres
Publié: (2024)
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints
par: Ozdaglar, Asuman, et autres
Publié: (2022)
par: Ozdaglar, Asuman, et autres
Publié: (2022)
PDHG-Unrolled Learning-to-Optimize Method for Large-Scale Linear Programming
par: Li, Bingheng, et autres
Publié: (2024)
par: Li, Bingheng, et autres
Publié: (2024)
Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
par: Li, Xiao, et autres
Publié: (2024)
par: Li, Xiao, et autres
Publié: (2024)
Expectation Error Bounds for Transfer Learning in Linear Regression and Linear Neural Networks
par: Liu, Meitong, et autres
Publié: (2026)
par: Liu, Meitong, et autres
Publié: (2026)
Fast and Numerically Stable Implementation of Rate Constant Matrix Contraction Method
par: Hemmi, Shinichi, et autres
Publié: (2024)
par: Hemmi, Shinichi, et autres
Publié: (2024)
Ascending Auctions for Combinatorial Markets with Frictions: A Unified Framework via Discrete Convex Analysis
par: Oki, Taihei, et autres
Publié: (2026)
par: Oki, Taihei, et autres
Publié: (2026)
Steepest Descent Algorithm for M-convex Function Minimization Using Long Step Length
par: Oki, Taihei, et autres
Publié: (2025)
par: Oki, Taihei, et autres
Publié: (2025)
A Generalization Bound for Nearly-Linear Networks
par: Golikov, Eugene
Publié: (2024)
par: Golikov, Eugene
Publié: (2024)
Nearly Optimal Linear Convergence of Stochastic Primal-Dual Methods for Linear Programming
par: Lu, Haihao, et autres
Publié: (2021)
par: Lu, Haihao, et autres
Publié: (2021)
Search Strategy Generation for Branch and Bound Using Genetic Programming
par: Maudet, Gwen, et autres
Publié: (2024)
par: Maudet, Gwen, et autres
Publié: (2024)
From Privacy to Generalization: Linear Max-Information Bounds for DP-SGD
par: Lampert, Christoph H., et autres
Publié: (2026)
par: Lampert, Christoph H., et autres
Publié: (2026)
Sample Complexity Bounds for Linear Constrained MDPs with a Generative Model
par: Liu, Xingtu, et autres
Publié: (2025)
par: Liu, Xingtu, et autres
Publié: (2025)
Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks
par: Balcan, Maria-Florina, et autres
Publié: (2025)
par: Balcan, Maria-Florina, et autres
Publié: (2025)
Locally Linear Continual Learning for Time Series based on VC-Theoretical Generalization Bounds
par: Ferreira, Yan V. G., et autres
Publié: (2026)
par: Ferreira, Yan V. G., et autres
Publié: (2026)
Label Learning Method Based on Tensor Projection
par: Li, Jing, et autres
Publié: (2024)
par: Li, Jing, et autres
Publié: (2024)
PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders
par: Guo, Ruixin, et autres
Publié: (2025)
par: Guo, Ruixin, et autres
Publié: (2025)
A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming
par: Xu, Haoran, et autres
Publié: (2026)
par: Xu, Haoran, et autres
Publié: (2026)
Generalization Bounds for Equivariant Networks on Markov Data
par: Li, Hui, et autres
Publié: (2025)
par: Li, Hui, et autres
Publié: (2025)
Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
par: Le, Tung Quoc, et autres
Publié: (2026)
par: Le, Tung Quoc, et autres
Publié: (2026)
On the Generalization Bounds of Symbolic Regression with Genetic Programming
par: Nomura, Masahiro, et autres
Publié: (2026)
par: Nomura, Masahiro, et autres
Publié: (2026)
Data-driven Projection Generation for Efficiently Solving Heterogeneous Quadratic Programming Problems
par: Iwata, Tomoharu, et autres
Publié: (2025)
par: Iwata, Tomoharu, et autres
Publié: (2025)
In-Context Learning of Linear Dynamical Systems with Transformers: Approximation Bounds and Depth-Separation
par: Cole, Frank, et autres
Publié: (2025)
par: Cole, Frank, et autres
Publié: (2025)
Learning Confidence Bounds for Classification with Imbalanced Data
par: Clifford, Matt, et autres
Publié: (2024)
par: Clifford, Matt, et autres
Publié: (2024)
Data-Driven Estimation of Capacity Upper Bounds
par: Häger, Christian, et autres
Publié: (2022)
par: Häger, Christian, et autres
Publié: (2022)
MDBench: Benchmarking Data-Driven Methods for Model Discovery
par: Bideh, Amirmohammad Ziaei, et autres
Publié: (2025)
par: Bideh, Amirmohammad Ziaei, et autres
Publié: (2025)
Documents similaires
-
Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
par: Oki, Taihei, et autres
Publié: (2026) -
No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and Hardness of Adversarial Full-Information Setting
par: Oki, Taihei, et autres
Publié: (2024) -
Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound
par: Sakaue, Shinsaku, et autres
Publié: (2025) -
Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
par: Sakaue, Shinsaku, et autres
Publié: (2024) -
Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness
par: Sakaue, Shinsaku
Publié: (2026)