Abstract The Internet of Things (IoT) allows communication between devices, things, and
any digital assets that send and receive data over a network without requiring interaction …

Federated learning utilizes various resources provided by participants to collaboratively train
a global model, which potentially address the data privacy issue of machine learning. In …

Problems of cooperation--in which agents seek ways to jointly improve their welfare--are
ubiquitous and important. They can be found at scales ranging from our daily routines--such …

Artificial intelligence (AI) and reinforcement learning (RL) have improved many areas but are
not yet widely adopted in economic policy design, mechanism design, or economics at …

In traditional machine learning, the central server first collects the data owners' private data
together and then trains the model. However, people's concerns about data privacy …

Conventional machine learning approaches aggregate all training data in a central server,
which causes massive communication overhead of data transmission and is also vulnerable …

Tackling real-world socio-economic challenges requires designing and testing economic
policies. However, this is hard in practice, due to a lack of appropriate (micro-level) …

In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes
not from declarative representation, but is derived by interrogation of a procedural …

Abstract Machine Learning (ML) can help solve combinatorial optimization (CO) problems
better. A popular approach is to use a neural net to compute on the parameters of a given …

Automated auction design aims to find empirically high-revenue mechanisms through
machine learning. Existing works on multi item auction scenarios can be roughly divided into …