In digital online advertising, advertisers procure ad impressions simultaneously on multiple
platforms, or so-called channels, such as Google Ads, Meta Ads Manager, etc., each of …

We study online auto-bidding algorithms for a single advertiser maximizing value under the
Return-on-Spend (RoS) constraint, quantifying performance in terms of regret relative to the …

We study online learning problems in which a decision maker has to make a sequence of
costly decisions, with the goal of maximizing their expected reward while adhering to budget …

Today's online advertisers procure digital ad impressions through interacting with
autobidding platforms: advertisers convey high level procurement goals via setting levers …

The bandits with knapsack (BwK) framework models online decision-making problems in
which an agent makes a sequence of decisions subject to resource consumption …

We study online learning in episodic constrained Markov decision processes (CMDPs),
where the goal of the learner is to collect as much reward as possible over the episodes …

This monograph provides an overview of distributed online optimization in multi-agent
systems. Online optimization approaches planning and decision problems from a robust …

Fairness is a major concern in contemporary decision problems. In these situations, the
objective is to maximize fairness while preserving the efficacy of the underlying decision …

The effectiveness of advertising in e-commerce largely depends on the ability of merchants
to bid on and win impressions for their targeted users. The bidding procedure is highly …

In many applications, learning systems are required to process continuous non-stationary
data streams. We study this problem in an online learning framework and propose an …