← Optimization
Mathematics & Optimization › Optimization ›

Online Algorithms

2190 directly classified papers

Papers per year

Papers

Piecewise Stationary Bandits under Risk Criteria AISTATS 2023
Overcoming Prior Misspecification in Online Learning to Rank AISTATS 2023
BWoS: Formally Verified Block-based Work Stealing for Parallel Processing OSDI 2023
Smooth Non-stationary Bandits ICML 2023
Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk NIPS 2023
Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness NIPS 2023
Anytime Model Selection in Linear Bandits NIPS 2023
Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games NIPS 2023
Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games NIPS 2023
Asynchronous Proportional Response Dynamics: Convergence in Markets with Adversarial Scheduling NIPS 2023
Efficient Online Clustering with Moving Costs NIPS 2023
Online Convex Optimization with Unbounded Memory NIPS 2023
Multi-Fidelity Multi-Armed Bandits Revisited NIPS 2023
Nearest Neighbour with Bandit Feedback NIPS 2023
Alternation makes the adversary weaker in two-player games NIPS 2023
Fully Dynamic $k$-Clustering in $\tilde O(k)$ Update Time NIPS 2023
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization NIPS 2023
An active learning framework for multi-group mean estimation NIPS 2023
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics JMLR 2023
Online Optimization over Riemannian Manifolds JMLR 2023
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework NIPS 2023
A PDE approach for regret bounds under partial monitoring JMLR 2023
Online Constrained Meta-Learning: Provable Guarantees for Generalization NIPS 2023
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games NIPS 2023
Robust Lipschitz Bandits to Adversarial Corruptions NIPS 2023