On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

Trend · papers per month

count % of corpus

5 results for “supLinUCB”

Nearly Minimax-Optimal Regret for Linearly Parameterized Banditsstat.ML

We study the linear contextual bandit problem with finite action sets. When the problem dimension is $d$ , the time horizon is $T$ , and there are $n \leq 2^{d/2}$ candidate actions per time period, we (1) show that the minimax expected regret is $Ω(\sqrt{dT (\log T) (\log n)})$ for every algorithm, and (2) introduce a V…

2019-03-30abs ↗pdf ↗

Variance-Aware Regret Bounds for Stochastic Contextual Dueling Banditscs.LG

This paper addresses dueling bandits with contextual information, improving regret bounds by accounting for variance.

problem Minimizing cumulative regret in dueling bandits with contextual information.

method Proposes a new SupLinUCB-type algorithm for contextual dueling bandits with variance-aware regret bound.

result Achieves a variance-aware regret bound of

ilde O\big(d\sqrt{\sum_{t=1}^Tσ_t^2} + d\big)

dueling bandits contextual regret bounds variance supLinUCB

2023-10-02abs ↗pdf ↗

Federated Linear Bandits with Finite Adversarial Actionscs.LG

A federated learning algorithm tackles linear bandits with adversarial actions, achieving optimal regret bounds.

problem Federated linear bandits with finite adversarial action sets.

method FedSupLinUCB algorithm, extending SupLinUCB and OFUL principles.

result Achieves a total regret of

ilde{O}(\sqrt{d T})

, matching minimax lower bound and being order-optimal.

federated learning linear bandits adversarial actions supLinUCB OFUL

2023-11-02abs ↗pdf ↗

Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selectionstat.ML

A new method for sparse linear bandits reduces exploration-exploitation tradeoff.

problem Sparse linear bandits in high-dimensional settings with finite actions.

method Best subset selection for parameter estimation and doubly growing epochs for regret minimization.

result Achieves nearly dimension-independent regret of

ilde{\mathcal{O}}(s\sqrt{T})

with high probability.

sparse linear bandits contextual bandits best subset selection regret minimization doubly growing epochs

2020-09-04abs ↗pdf ↗

On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Banditscs.LG

Algorithm reduces regret in misspecified linear contextual bandits.

problem Misspecified linear contextual bandits with bounded misspecification.

method Data selection scheme for online regression, leveraging uncertainty.

result Regret bound of

\tilde O(d^2/Δ)

when

ζ \leq \tilde O(Δ/\sqrt{d})

Linear Contextual Bandits Misspecification Regret Bound Online Learning Uncertainty

2023-03-16abs ↗pdf ↗