Tharrmashastha SAPV

Efficient Quantum Agnostic Improper Learning of Decision Trees

The agnostic setting is the hardest generalization of the PAC model since it is akin to learning with adversarial noise. In this paper, we give a poly $(n, t, 1/\epsilon)$ quantum algorithm for learning size $t$ decision trees over $n$-bit inputs with uniform marginal over instances, in the agnostic…