no code implementations • 9 Jul 2023 • Caleb Koch, Carmen Strassle, Li-Yang Tan
We prove that it is NP-hard to properly PAC learn decision trees with queries, resolving a longstanding open problem in learning theory (Bshouty 1993; Guijarro-Lavin-Raghavan 1999; Mehta-Raghavan 2002; Feldman 2016).
no code implementations • 12 Oct 2022 • Caleb Koch, Carmen Strassle, Li-Yang Tan
We establish new hardness results for decision tree optimization problems, adding to a line of work that dates back to Hyafil and Rivest in 1976.
no code implementations • 14 Jul 2022 • Guy Blanc, Caleb Koch, Jane Lange, Li-Yang Tan
Here $S(f)$ is the sensitivity of $f$, a discrete analogue of the Lipschitz constant, and $\Delta_f(x^\star)$ is the distance from $x^\star$ to its nearest counterfactuals.
no code implementations • 28 Jul 2017 • Andrew Crutcher, Caleb Koch, Kyle Coleman, Jon Patman, Flavio Esposito, Prasad Calyam
We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task.