Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimax Value Interval for Off-Policy Evaluation and Policy Optimization   We study minimax methods for off-policy evaluation (OPE) using value
functions and marginalized importance weights. Despite that they hold promises
of overcoming the exponential variance in traditional importance sampling,
several key problems remain:
  (1) They require function approximation and are generally biased. For the
sake of trustworthy OPE, is there anyway to quantify the biases?
  (2) They are split into two styles ("weight-learning" vs "value-learning").
Can we unify them?
  In this paper we answer both questions positively. By slightly altering the
derivation of previous methods (one from each style; Uehara et al., 2020), we
unify them into a single value interval that comes with a special type of
double robustness: when either the value-function or the importance-weight
class is well specified, the interval is valid and its length quantifies the
misspecification of the other class. Our interval also provides a unified view
of and new insights to some recent methods, and we further explore the
implications of our results on exploration and exploitation in off-policy
policy optimization with insufficient data coverage.

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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
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The categories of this paper are: econ
29
The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc