Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved scalability under heavy tails, without strong convexity   Real-world data is laden with outlying values. The challenge for machine
learning is that the learner typically has no prior knowledge of whether the
feedback it receives (losses, gradients, etc.) will be heavy-tailed or not. In
this work, we study a simple algorithmic strategy that can be leveraged when
both losses and gradients can be heavy-tailed. The core technique introduces a
simple robust validation sub-routine, which is used to boost the confidence of
inexpensive gradient-based sub-processes. Compared with recent robust gradient
descent methods from the literature, dimension dependence (both risk bounds and
cost) is substantially improved, without relying upon strong convexity or
expensive per-step robustification. Empirically, we also show that under
heavy-tailed losses, the proposed procedure cannot simply be replaced with
naive cross-validation. Taken together, we have a scalable method with
transparent guarantees, which performs well without prior knowledge of how
"convenient" the feedback it receives will be.

13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci