  In online ad markets, a rising number of advertisers are employing bidding
agencies to participate in ad auctions. These agencies are specialized in
designing online algorithms and bidding on behalf of their clients. Typically,
an agency usually has information on multiple advertisers, so she can
potentially coordinate bids to help her clients achieve higher utilities than
those under independent bidding.
  In this paper, we study coordinated online bidding algorithms in repeated
second-price auctions with budgets. We propose algorithms that guarantee every
client a higher utility than the best she can get under independent bidding. We
show that these algorithms achieve maximal coalition welfare and discuss
bidders' incentives to misreport their budgets, in symmetric cases. Our proofs
combine the techniques of online learning and equilibrium analysis, overcoming
the difficulty of competing with a multi-dimensional benchmark. The performance
of our algorithms is further evaluated by experiments on both synthetic and
real data. To the best of our knowledge, we are the first to consider bidder
coordination in online repeated auctions with constraints.
