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Found 6 papers, 0 papers with code
When are Local Queries Useful for Robust Learning?
We finish by giving robust learning algorithms for halfspaces on $\{0, 1\}^n$ and then obtaining robustness guarantees for halfspaces in $\mathbb{R}^n$ against precision-bounded adversaries.
Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks
A fundamental problem in adversarial machine learning is to quantify how much training data is needed in the presence of evasion attacks.
On the Hardness of Robust Classification
However if the adversary is restricted to perturbing $O(\log n)$ bits, then the class of monotone conjunctions can be robustly learned with respect to a general class of distributions (that includes the uniform distribution).
On Restricted Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative $n \times m$ matrix $M$ into a product of a nonnegative $n \times d$ matrix $W$ and a nonnegative $d \times m$ matrix $H$.
Nonnegative Matrix Factorization Requires Irrationality
Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative $n \times m$ matrix $M$ into a product of a nonnegative $n \times d$ matrix $W$ and a nonnegative $d \times m$ matrix $H$.
Complexity of Equivalence and Learning for Multiplicity Tree Automata
Habrard and Oncina (2006) give an exact learning algorithm for multiplicity tree automata, in which the number of queries is proportional to the size of the target automaton and the size of a largest counterexample, represented as a tree, that is returned by the Teacher.
