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Found 8 papers, 1 papers with code
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence.
Revisiting Smoothed Online Learning
Moreover, when the hitting cost is both convex and $\lambda$-quadratic growth, we reduce the competitive ratio to $1 + \frac{2}{\sqrt{\lambda}}$ by minimizing the weighted sum of the hitting cost and the switching cost.
Minimizing Dynamic Regret and Adaptive Regret Simultaneously
To address this limitation, new performance measures, including dynamic regret and adaptive regret have been proposed to guide the design of online algorithms.
Adaptive and Efficient Algorithms for Tracking the Best Expert
The first algorithm achieves a second-order tracking regret bound, which improves existing first-order bounds.
Multi-Objective Generalized Linear Bandits
In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives.
Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization
In this paper, we study adaptive online convex optimization, and aim to design a universal algorithm that achieves optimal regret bounds for multiple common types of loss functions.
SAdam: A Variant of Adam for Strongly Convex Functions
In this paper, we give an affirmative answer by developing a variant of Adam (referred to as SAdam) which achieves a data-dependant $O(\log T)$ regret bound for strongly convex functions.
Adaptive Online Learning in Dynamic Environments
In this paper, we study online convex optimization in dynamic environments, and aim to bound the dynamic regret with respect to any sequence of comparators.
