Forlinear regression, we give a polynomial-time algorithm based on Celis-Dennis-Tapia optimization algorithms. For binary classification, we show how to efficiently implement itusing aproper agnostic learner (i.e., anEmpirical Risk Minimizer) for the class of interest.

We study the preference-based pure exploration problem for bandits with vector-valued rewards.

GFlowNet policies can efficiently find high-quality solutions.

In modern portfolio management, one may require anm-sparse (no more thanm active assets) portfolio to save managerial and financial costs.

Unfortunately, many real-world problems of practical interest have these characteristics.