Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Implicit Parameter-free Online Learning with Truncated Linear Models   Parameter-free algorithms are online learning algorithms that do not require
setting learning rates. They achieve optimal regret with respect to the
distance between the initial point and any competitor. Yet, parameter-free
algorithms do not take into account the geometry of the losses. Recently, in
the stochastic optimization literature, it has been proposed to instead use
truncated linear lower bounds, which produce better performance by more closely
modeling the losses. In particular, truncated linear models greatly reduce the
problem of overshooting the minimum of the loss function. Unfortunately,
truncated linear models cannot be used with parameter-free algorithms because
the updates become very expensive to compute. In this paper, we propose new
parameter-free algorithms that can take advantage of truncated linear models
through a new update that has an "implicit" flavor. Based on a novel
decomposition of the regret, the new update is efficient, requires only one
gradient at each step, never overshoots the minimum of the truncated model, and
retains the favorable parameter-free properties. We also conduct an empirical
study demonstrating the practical utility of our algorithms.

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The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
18
The categories of this paper are: econ
2
The categories of this paper are: math