Cover Image

Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sakaue, Shinsaku, Tsuchiya, Taira, Bao, Han, Oki, Taihei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
by: Oki, Taihei, et al.
Published: (2026)

Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
by: Sakaue, Shinsaku, et al.
Published: (2024)

Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel$-$Young Loss Perspective and Gap-Dependent Regret Analysis
by: Sakaue, Shinsaku, et al.
Published: (2025)

Generalization Bound and Learning Methods for Data-Driven Projections in Linear Programming
by: Sakaue, Shinsaku, et al.
Published: (2023)

No-Regret M${}^{\natural}$-Concave Function Maximization: Stochastic Bandit Algorithms and Hardness of Adversarial Full-Information Setting
by: Oki, Taihei, et al.
Published: (2024)

Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness
by: Sakaue, Shinsaku
Published: (2026)

Bandit and Delayed Feedback in Online Structured Prediction
by: Shibukawa, Yuki, et al.
Published: (2025)

From Average Sensitivity to Small-Loss Regret Bounds under Random-Order Model
by: Sakaue, Shinsaku, et al.
Published: (2026)

Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring
by: Tsuchiya, Taira, et al.
Published: (2024)

Non-Stationary Online Structured Prediction with Surrogate Losses
by: Sakaue, Shinsaku, et al.
Published: (2025)

Tight Regret Upper and Lower Bounds for Optimistic Hedge in Two-Player Zero-Sum Games
by: Tsuchiya, Taira
Published: (2025)

Data- and Variance-dependent Regret Bounds for Online Tabular MDPs
by: Li, Mingyi, et al.
Published: (2026)

Any-stepsize Gradient Descent for Separable Data under Fenchel-Young Losses
by: Bao, Han, et al.
Published: (2025)

A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $Θ(T^{2/3})$ and its Application to Best-of-Both-Worlds
by: Tsuchiya, Taira, et al.
Published: (2024)

Online Control of Linear Systems under Unbounded Noise
by: Ito, Kaito, et al.
Published: (2024)

Fast Rates in Stochastic Online Convex Optimization by Exploiting the Curvature of Feasible Sets
by: Tsuchiya, Taira, et al.
Published: (2024)

Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms
by: Réveillard, William, et al.
Published: (2025)

Finite-Time Logarithmic Bayes Regret Upper Bounds
by: Atsidakou, Alexia, et al.
Published: (2023)

Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret
by: Ito, Shinji, et al.
Published: (2026)

Regret Lower Bounds for Learning Linear Quadratic Gaussian Systems
by: Ziemann, Ingvar, et al.
Published: (2022)

Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms
by: Scroccaro, Pedro Zattoni, et al.
Published: (2023)

Logarithmic Regret for Online KL-Regularized Reinforcement Learning
by: Zhao, Heyang, et al.
Published: (2025)

Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
by: Kuroki, Yuko, et al.
Published: (2023)

Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel-Young Losses
by: Cao, Yuzhou, et al.
Published: (2025)

Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal
by: Ziomek, Juliusz, et al.
Published: (2024)

A Polynomial-time Algorithm for Online Sparse Linear Regression with Improved Regret Bound under Weaker Conditions
by: Li, Junfan, et al.
Published: (2025)

Threshold-Based Optimal Arm Selection in Monotonic Bandits: Regret Lower Bounds and Algorithms
by: Varude, Chanakya, et al.
Published: (2025)

Logarithmic Regret for Nonlinear Control
by: Wang, James, et al.
Published: (2025)

Regret Bounds for Robust Online Decision Making
by: Appel, Alexander, et al.
Published: (2025)

Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
by: Kim, Seok-Jin, et al.
Published: (2024)

Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback
by: Ito, Shinji, et al.
Published: (2025)

Logarithmic Regret and Polynomial Scaling in Online Multi-step-ahead Prediction
by: Qian, Jiachen, et al.
Published: (2025)

Algebraic Algorithms for Fractional Linear Matroid Parity via Non-commutative Rank
by: Oki, Taihei, et al.
Published: (2022)

Online Nonstochastic Prediction: Logarithmic Regret via Predictive Online Least Squares
by: Pai, Chih-Fan, et al.
Published: (2026)

Universal Dynamic Regret and Constraint Violation Bounds for Constrained Online Convex Optimization
by: Supantha, Subhamon, et al.
Published: (2025)

Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games
by: Gao, Minbo, et al.
Published: (2023)

Variance-Dependent Regret Lower Bounds for Contextual Bandits
by: He, Jiafan, et al.
Published: (2025)

Distributed Online Convex Optimization with Efficient Communication: Improved Algorithm and Lower bounds
by: Yang, Sifan, et al.
Published: (2026)

A Single-Sample Polylogarithmic Regret Bound for Nonstationary Online Linear Programming
by: Xu, Haoran, et al.
Published: (2026)

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
by: Zhong, Han, et al.
Published: (2023)