  Online advertising channels have commonly focused on maximizing total
advertiser value (or welfare) to enhance long-run retention and channel
healthiness. Previous literature has studied auction design by incorporating
machine learning predictions on advertiser values (also known as
machine-learned advice) through various forms to improve total welfare. Yet,
such improvements could come at the cost of individual bidders' welfare and do
not shed light on how particular advertiser bidding strategies impact welfare.
  Motivated by this, we present an analysis on an individual bidder's welfare
loss in the autobidding world for auctions with and without machine-learned
advice, and also uncover how advertiser strategies relate to such losses. In
particular, we demonstrate how ad platforms can utilize ML advice to improve
welfare guarantee on the aggregate and individual bidder level by setting ML
advice as personalized reserve prices when the platform consists of autobidders
who maximize value while respecting a return-on-ad spent (ROAS) constraint.
Under parallel VCG auctions with such ML advice-based reserves, we present a
worst-case welfare lower-bound guarantee for an individual autobidder, and show
that the lower-bound guarantee is positively correlated with ML advice quality
as well the scale of bids induced by the autobidder's bidding strategies.
Further, we prove an impossibility result showing that no truthful, and
possibly randomized mechanism with anonymous allocations can achieve
universally better individual welfare guarantees than VCG, in presence of
personalized reserves based on ML-advice of equal quality. Moreover, we extend
our individual welfare guarantee results to generalized first price (GFP) and
generalized second price (GSP) auctions.
