The workshop will explore the impact of problem geometry on algorithmic performance in decision-making processes. Example topics include resource-constrained decision-making settings, the interaction between state-space geometry and algorithmic guarantees (e.g., downward-closed and matroid settings, discrete vs. continuous, convexity, network structure), the structure of transitions (stationary vs. non-stationary, exogenous randomness, correlation), and more complex induced structures (Lipschitz/convexity of value functions, induced problem dimensions). In addition, the workshop will also explore constraints arising from more practical concerns (simplicity, distributed control), as well as from incentives in dynamic mechanisms.

Invited Participants

Shipra Agrawal (Columbia University), Siddhartha Banerjee (Cornell University), Hamsa Bastani (Upenn), Zaiwei Chen (Caltech), Ching-An Cheng (Microsoft Research), Nikhil Devanur (Amazon), Gabriele Farina (CMU), Negin Golrezaei (Massachusetts Institute of Technology), Yashodhan Kanoria (Columbia University), Haihao Lu (Chicago Booth School of Business), Siva Theja Maguluri (Georgia Institute of Technology), Rad Niazadeh (The University of Chicago Booth School of Business), Sanjay Shakkottai (The University of Texas at Austin), Balasubramanian Sivan (Google Research), Csaba Szepesvari (University of Alberta), Weina Wang (Carnegie Mellon University), Adam Wierman (Caltech)