Neural Information Processing Systems http://nips.cc/

We investigate the problem of learning a Lipschitz function from binary feedback.

In contextual dynamic pricing, a seller sequentially prices goods based on contextual information.

The prompt-based paradigm has fostered two recent human-AI interaction trends.

Interpretability has become a well-recognized goal for machine learning models.

We investigate the problem of learning a Lipschitz function from binary feedback.

In optimization under uncertainty, the desirability of a decision (e.g., inventory) depends on an

It was the work of Cournot [1897] in 1897 that firstly applied mathematics to analyze the relationship between prices and demands.

Column generation and branch-and-price are leading methods for large-scale exact optimization. Column generation iterates between solving a master problem and a pricing problem. The master problem is a linear program, which can be solved using a generic solver. The pricing problem is highly dependent on the application but is usually discrete. Due to the difficulty of discrete optimization, high-performance column generation often relies on a custom pricing algorithm built specifically to exploit the problem's structure. This bespoke nature of the pricing solver prevents the reuse of components for other applications. We show that domain-independent dynamic programming, a software package for modeling and solving arbitrary dynamic programs, can be used as a generic pricing solver. We develop basic implementations of branch-and-price with pricing by domain-independent dynamic programming and show that they outperform a world-leading solver on static mixed integer programming formulations for seven problem classes.