When hearing music, it is natural for people to dance to its rhythm.
Aut...
Peer review frequently follows a process where reviewers first provide
i...
Non-governmental organizations for environmental conservation have a
sig...
Despite their potential in real-world applications, multi-agent reinforc...
We aim to understand how people assess human likeness in navigation prod...
Cross-domain recommendation has attracted increasing attention from indu...
Function approximation (FA) has been a critical component in solving lar...
Correlated Equilibrium is a solution concept that is more general than N...
Estimating causal effects has become an integral part of most applied fi...
Action advising is a knowledge transfer technique for reinforcement lear...
Curriculum Reinforcement Learning (CRL) aims to create a sequence of tas...
Optimizing strategic decisions (a.k.a. computing equilibrium) is key to ...
Multi-scenario recommendation is dedicated to retrieve relevant items fo...
As one of the largest e-commerce platforms in the world, Taobao's
recomm...
Many conferences rely on paper bidding as a key component of their revie...
In conference peer review, reviewers are often asked to provide "bids" o...
Traffic signal control (TSC) is a high-stakes domain that is growing in
...
Many recent breakthroughs in multi-agent reinforcement learning (MARL)
r...
Speakers' referential expressions often depart from communicative ideals...
Preventing poaching through ranger patrols protects endangered wildlife,...
As a challenging multi-player card game, DouDizhu has recently drawn muc...
Robust Reinforcement Learning (RL) focuses on improving performances und...
Explainable reinforcement learning (XRL) is an emerging subfield of
expl...
Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesha...
Vagueness and ambiguity in privacy policies threaten the ability of cons...
One practical requirement in solving dynamic games is to ensure that the...
Many scientific conferences employ a two-phase paper review process, whe...
Current work in explainable reinforcement learning generally produces
po...
A central problem in machine learning and statistics is to model joint
d...
Conservation efforts in green security domains to protect wildlife and
f...
The use of machine learning (ML) systems in real-world applications enta...
The E-commerce platform has become the principal battleground where peop...
We consider three important challenges in conference peer review: (i)
re...
In multi-agent games, the complexity of the environment can grow
exponen...
While game-theoretic models and algorithms have been developed to combat...
Artificial intelligence for social good (AI4SG) is a research theme that...
With the maturing of AI and multiagent systems research, we have a treme...
Self-play methods based on regret minimization have become the state of ...
As robots are increasingly endowed with social and communicative
capabil...
Most existing models of multi-agent reinforcement learning (MARL) adopt
...
In many real-world problems, a team of agents need to collaborate to max...
Transportation service providers that dispatch drivers and vehicles to r...
While Nash equilibrium in extensive-form games is well understood, very
...
Today's high-stakes adversarial interactions feature attackers who const...
With the recent advances in solving large, zero-sum extensive form games...
The Computing Community Consortium (CCC), along with the White House Off...
Cyber adversaries have increasingly leveraged social engineering attacks...
Social engineering attacks represent an increasingly important attack ve...
Strong Stackelberg equilibrium (SSE) is the standard solution concept of...
Green Security Games (GSGs) have been proposed and applied to optimize
p...