We study an online allocation problem with sequentially arriving items a...
Coordinate descent methods are popular in machine learning and optimizat...
We study variance reduction methods for extragradient (EG) algorithms fo...
Regret Matching+ (RM+) and its variants are important algorithms for sol...
We consider the problem of large-scale Fisher market equilibrium computa...
Notifications are important for the user experience in mobile apps and c...
Online advertising platforms typically use auction mechanisms to allocat...
We initiate the study of statistical inference and A/B testing for
first...
Fisher markets are those where buyers with budgets compete for scarce it...
Statistical inference under market equilibrium effects has attracted
inc...
We consider the problem of allocating a distribution of items to n
recip...
The Stackelberg game model, where a leader commits to a strategy and the...
A recent paper by Piliouras et al. [2021, 2022] introduces an uncoupled
...
We study stochastic online resource allocation: a decision maker needs t...
A recent line of work has established uncoupled learning dynamics such t...
Algorithms designed for single-agent reinforcement learning (RL) general...
In this paper we establish efficient and uncoupled learning dynamics
so ...
We study online learning problems in which a decision maker wants to max...
We introduce the Conic Blackwell Algorithm^+ (CBA^+) regret minimizer, a...
We consider the problem of fairly allocating items to a set of individua...
Single-leg revenue management is a foundational problem of revenue manag...
A recent emerging trend in the literature on learning in games has been
...
In today's digital world, interaction with online platforms is ubiquitou...
While extensive-form games (EFGs) can be converted into normal-form game...
Global demand for donated blood far exceeds supply, and unmet need is
gr...
Throttling is a popular method of budget management for online ad auctio...
Regret-based algorithms are highly efficient at finding approximate Nash...
Budget-management systems are one of the key components of modern auctio...
We develop new parameter and scale-free algorithms for solving convex-co...
We study the application of iterative first-order methods to the problem...
Budget constraints are ubiquitous in online advertisement auctions. To m...
Computing market equilibria is a problem of both theoretical and applied...
The internet advertising market is a multi-billion dollar industry, in w...
Linear Fisher markets are a fundamental economic model with applications...
Markov Decision Processes (MDPs) are known to be sensitive to parameter
...
Blackwell approachability is a framework for reasoning about repeated ga...
Inspired by the recent COVID-19 pandemic, we study a generalization of t...
Can we predict how well a team of individuals will perform together? How...
Market equilibrium is a solution concept with many applications such as
...
Markov Decision Processes (MDP) are a widely used model for dynamic
deci...
Monte-Carlo counterfactual regret minimization (MCCFR) is the
state-of-t...
The problem of allocating scarce items to individuals is an important
pr...
We study the performance of optimistic regret-minimization algorithms fo...
Allocating multiple scarce items across a set of individuals is an impor...
We consider the problem of dividing items between individuals in a way t...
We consider the problem of using logged data to make predictions about w...
First-order methods are known to be among the fastest algorithms for sol...
Limited lookahead has been studied for decades in complete-information g...
The CFR framework has been a powerful tool for solving large-scale
exten...
Computing market equilibria is an important practical problem for market...