Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Online Learning for Resource Allocation -- Beyond Euclidean
  Projection and Dynamic Fit   Online-learning literature has focused on designing algorithms that ensure
sub-linear growth of the cumulative long-term constraint violations. The
drawback of this guarantee is that strictly feasible actions may cancel out
constraint violations on other time slots. For this reason, we introduce a new
performance measure called $\hCFit$, whose particular instance is the
cumulative positive part of the constraint violations. We propose a class of
non-causal algorithms for online-decision making, which guarantees, in slowly
changing environments, sub-linear growth of this quantity despite noisy
first-order feedback. Furthermore, we demonstrate by numerical experiments the
performance gain of our method relative to the state of art.

8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph