  Standard approaches to decision-making under uncertainty focus on sequential
exploration of the space of decisions. However, \textit{simultaneously}
proposing a batch of decisions, which leverages available resources for
parallel experimentation, has the potential to rapidly accelerate exploration.
We present a family of (parallel) contextual bandit algorithms applicable to
problems with bounded eluder dimension whose regret is nearly identical to
their perfectly sequential counterparts -- given access to the same total
number of oracle queries -- up to a lower-order ``burn-in" term. We further
show these algorithms can be specialized to the class of linear reward
functions where we introduce and analyze several new linear bandit algorithms
which explicitly introduce diversity into their action selection. Finally, we
also present an empirical evaluation of these parallel algorithms in several
domains, including materials discovery and biological sequence design problems,
to demonstrate the utility of parallelized bandits in practical settings.
