The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

Abstract Colombian drug lord Pablo Escobar, while on the run, purportedly burned two
million dollars in banknotes to keep his daughter warm. A stark reminder that, in life …

Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …

Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

We study the problem of generating diverse candidates in the context of Multi-Objective
Optimization. In many applications of machine learning such as drug discovery and material …

Expensive multi-objective optimization problems can be found in many real-world
applications, where their objective function evaluations involve expensive computations or …

Many real-world control problems involve conflicting objectives where we desire a dense
and high-quality set of control policies that are optimal for different objective preferences …

While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language
Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning …

Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …

The issue of fairness in recommendation is becoming increasingly essential as
Recommender Systems (RS) touch and influence more and more people in their daily lives …