Score-based generative models (SGMs) have recently shown impressive results for difficult
generative tasks such as the unconditional and conditional generation of natural images …

Separate from just attacking some of the standard problems in reinforcement learning as
they are found in many books as an example, it's good to look at fields where the answers …

Synthetic data has made tremendous strides in various commercial settings including
finance, healthcare, and virtual reality. We present a broad overview of prototypical …

Market simulation is an increasingly important method for evaluating and training trading
strategies and testing" what if" scenarios. The extent to which results from these simulations …

This study proposes a novel reinforcement learning (RL) framework, professional trader RL
(Pro Trader RL), which mimics the decision-making patterns and trading philosophy of …

One of the perquisites of a talk like this is that I get to expound on broad themes. AAMAS is a
conference about agents and multiples of agents, so I probably ought to say something …

We investigate the effects of market making on market performance, focusing on allocative
efficiency as well as gains from trade accrued by background traders. We employ empirical …

We present an agent-based model of manipulating prices in financial markets through
spoofing: submitting spurious orders to mislead traders who learn from the order book. Our …

We present an agent-based model of manipulating prices in financial markets through
spoofing: submitting spurious orders to mislead other traders. Built around the standard …

We study a very general class of games—multi-dimensional aggregative games—which in
particular generalize both anonymous games and weighted congestion games. For any …