We give the first agnostic, efficient, proper learning algorithm for monotone Boolean
functions. Given uniformly random examples of an unknown function, our algorithm outputs …

We give a 2^̃O(n/ε)-time algorithm for properly learning monotone Boolean functions under
the uniform distribution over {0,1\}^n. Our algorithm is robust to adversarial label noise and …

Our society increasingly relies on algorithms and data analysis to make critical decisions.
Yet, almost all work in the theory of supervised learning has long relied on the following two …

We give a $2^{\tilde {O}(\sqrt {n}/\epsilon)} $-time algorithm for properly learning monotone
Boolean functions under the uniform distribution over $\{0, 1\}^ n $. Our algorithm is robust to …

We study local filters for the Lipschitz property of real-valued functions f: V→[0, r], where the
Lipschitz property is defined with respect to an arbitrary undirected graph G=(V, E). We give …

A Complete Bibliography of ACM Transactions on Computation Theory (ToCT) Page 1 A
Complete Bibliography of ACM Transactions on Computation Theory (ToCT) Nelson HF …