Titlebook: Algorithmic Learning Theory; 26th International C Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles Conference proceedings 2015 Springer Int

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Non-adaptive Learning of a Hidden Hypergraph either run in exponential time or have non-optimal query complexity. We give the first polynomial time non-adaptive learning algorithm for learning hypergraph that asks an almost optimal number of queries.

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Efficient Matrix Sensing Using Rank-1 Gaussian Measurements. Existing methods for the problem either rely on measurement operators such as random element-wise sampling which cannot recover arbitrary low-rank matrices or require the measurement operator to satisfy the Restricted Isometry Property (RIP). However, RIP based linear operators are generally full

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cognizant

Exact Learning of Multivalued Dependenciesultivalued dependencies. In this work, we study the learnability of multivalued dependency formulas (MVDF), which correspond to the logical theory behind multivalued dependencies. As we explain, MVDF lies between propositional Horn and 2-Quasi-Horn. We prove that MVDF is polynomially learnable in An

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Non-adaptive Learning of a Hidden Hypergraph either run in exponential time or have non-optimal query complexity. We give the first polynomial time non-adaptive learning algorithm for learning hypergraph that asks an almost optimal number of queries.

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Learning a Random DFA from Uniform Strings and State Informationm of learning a DFA from given input data is a classic topic in computational learning theory. In this paper we study the learnability of a random DFA and propose a computationally efficient algorithm for learning and recovering a random DFA from uniform input strings and state information in the st