We revisit the classical online portfolio selection problem. It is widely assumed that a trade-
off between computational complexity and regret is unavoidable, with Cover's Universal …

Consider an online convex optimization problem where the loss functions are self-
concordant barriers, smooth relative to a convex function $ h $, and possibly non-Lipschitz …

This work introduces the first small-loss and gradual-variation regret bounds for online
portfolio selection, marking the first instances of data-dependent bounds for online convex …

This paper presents new projection-free algorithms for Online Convex Optimization (OCO)
over a convex domain $\mathcal {K}\subset\mathbb {R}^ d $. Classical OCO algorithms …

In the problem of online portfolio selection as formulated by Cover (1991), the trader
repeatedly distributes her capital over $ d $ assets in each of $ T> 1$ rounds, with the goal …

The aim of this paper is to design computationally-efficient and optimal algorithms for the
online and stochastic exp-concave optimization settings. Typical algorithms for these …

Practical online learning tasks are often naturally defined on unconstrained domains, where
optimal algorithms for general convex losses are characterized by the notion of comparator …

Effective online portfolio selection necessitates seamless integration of three key properties:
diversity, sparsity, and risk control. However, existing algorithms often prioritize one property …

We explore whether quantum advantages can be found for the zeroth-order feedback online
exp-concave optimization problem, which is also known as bandit exp-concave optimization …

In this paper, we introduce a new projection-free algorithm for Online Convex Optimization
(OCO) with a state-of-the-art regret guarantee among separation-based algorithms. Existing …