Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Learning and Decision-Making under Generalized Linear Model with
  High-Dimensional Data   We propose a minimax concave penalized multi-armed bandit algorithm under
generalized linear model (G-MCP-Bandit) for a decision-maker facing
high-dimensional data in an online learning and decision-making process. We
demonstrate that the G-MCP-Bandit algorithm asymptotically achieves the optimal
cumulative regret in the sample size dimension T , O(log T), and further
attains a tight bound in the covariate dimension d, O(log d). In addition, we
develop a linear approximation method, the 2-step weighted Lasso procedure, to
identify the MCP estimator for the G-MCP-Bandit algorithm under non-iid
samples. Under this procedure, the MCP estimator matches the oracle estimator
with high probability and converges to the true parameters with the optimal
convergence rate. Finally, through experiments based on synthetic data and two
real datasets (warfarin dosing dataset and Tencent search advertising dataset),
we show that the G-MCP-Bandit algorithm outperforms other benchmark algorithms,
especially when there is a high level of data sparsity or the decision set is
large.

30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
9
The categories of this paper are: nlin