Research
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

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0111 · Jun 201719922001200920172026
4 results for “online-to-confidence-set”
Low-Rank Generalized Linear Bandit Problemsstat.ML

An algorithm tackles low-rank linear bandit problems with improved regret bounds.

problem Low-rank linear bandit problems where rewards are inner products with an unknown low-rank matrix.
method Combines online-to-confidence-set conversion and exponentially weighted average forecaster with a covering of low-rank matrices.
result Achieves O~((d1+d2)3/2rT)\widetilde{O}((d_1+d_2)^{3/2}\sqrt{rT}) regret, improving over standard bounds when r≪min⁡{d1,d2}r \ll \min\{d_1,d_2\}.
bandit problemslow-rank matricesregret boundsonline-to-confidence-setexponentially weighted average
2020-06-04abs ↗pdf ↗
Sparsity-Agnostic Linear Bandits with Adaptive Adversariescs.LG

New method for linear bandits with unknown sparsity, improving sparse regret bounds.

problem Sparse regret bounds for unknown sparsity and adversarial action sets.
method Combines online to confidence set conversions with randomized model selection over nested confidence sets.
result First sparse regret bounds for unknown sparsity and adversarial action sets.
linear banditssparse regretadaptive adversariesconfidence setsrandomized model selection
2024-06-03abs ↗pdf ↗
Scalable Generalized Linear Bandits: Online Computation and Hashingstat.ML

Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice. This paper proposes new, scalable solutions to the GLB probl…

2017-06-01abs ↗pdf ↗