Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

Large Language Models (LLMs) have shown impressive performance as general purpose
agents, but their abilities remain highly dependent on prompts which are hand written with …

Simultaneously optimizing many design parameters in time-consuming experiments causes
bottlenecks in a broad range of scientific and engineering disciplines,. One such example is …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

What is reinforcement learning? How is reinforcement learning different from stochastic
optimization? And finally, can it be used for applications to quantitative finance for my current …

Motivated by the task of hyperparameter optimization, we introduce the\em non-stochastic
best-arm identification problem. We identify an attractive algorithm for this setting that makes …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …