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Properly Learning Decision Trees with Queries Is NP-Hard
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).
Superpolynomial Lower Bounds for Decision Tree Learning and Testing
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.
A Query-Optimal Algorithm for Finding Counterfactuals
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.
Hyperprofile-based Computation Offloading for Mobile Edge Networks
We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task.
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