Motivated by, eg, sensitivity analysis and end-to-end learning, the demand for differentiable
optimization algorithms has been increasing. This paper presents a theoretically guaranteed …

Classification approaches that allow to extract logical rules such as decision trees are often
considered to be more interpretable than neural networks. Also, logical rules are …

Submodular functions and variants, through their ability to characterize diversity and
coverage, have emerged as a key tool for data selection and summarization. Many recent …

Many sequence prediction tasks admit multiple correct outputs and so, it is often useful to
decode a set of outputs that maximize some task-specific set-level metric. However …

Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed …

Motivated by, eg, sensitivity analysis and end-to-end learning, the demand for differentiable
optimization algorithms has been significantly increasing. In this paper, we establish a …

Imposing appropriate structure or constraints onto optimization problems is often the key to
deriving guarantees or improving generalization of performance aspects like generalization …