… the immediate (1-step) cost of taking action a in state x; y is the new state where we end up
after taking a in x; n is the number of times we have tried a in x, and p is the action taken in y. …

The Schrödinger bridge problem (SBP) is gaining increasing attention in generative modeling
and showing promising potential even in comparison with the score-based generative …

Addressing the ongoing examination of high-frequency trading practices in financial markets,
we report the results of an extensive empirical study estimating the maximum possible …

Over the past years, foundation models have caused a paradigm shift in machine learning
due to their unprecedented capabilities for zero-shot and few-shot generalization. However, …

In this chapter, we overview the uses of machine learning for high frequency trading and
market microstructure data and problems. Machine learning is a vibrant subfield of computer …

Temporal data such as time series can be viewed as discretized measurements of the
underlying function. To build a generative model for such data we have to model the stochastic …

Dark pools are a recent type of stock exchange in which information about outstanding orders
is deliberately hidden in order to minimize the market impact of large-volume trades. The …

… Without loss of generality, consider a regression task in the feature space X (eg, Rd) with
response Y taking values in Y … ) ∼D, and allows for the general form of heteroscedasticity of Y . …

Detecting out-of-distribution (OOD) samples is a problem of practical importance for a reliable
use of deep neural networks (DNNs) in production settings. The corollary to this problem is …

Temporal data like time series are often observed at irregular intervals which is a challenging
setting for the existing machine learning methods. To tackle this problem, we view such …