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Found 15 papers, 0 papers with code
On Detecting Some Defective Items in Group Testing
We develop upper and lower bounds on the number of tests required to detect $\ell$ defective items in both the adaptive and non-adaptive settings while considering scenarios where no prior knowledge of $d$ is available, and situations where an estimate of $d$ or at least some non-trivial upper bound on $d$ is available.
Almost Optimal Proper Learning and Testing Polynomials
For $s$-sparse polynomial over $n$ variables and $\epsilon=1/s^\beta$, $\beta>1$, our algorithm makes $$q_U=\left(\frac{s}{\epsilon}\right)^{\frac{\log \beta}{\beta}+O(\frac{1}{\beta})}+ \tilde O\left(s\right)\left(\log\frac{1}{\epsilon}\right)\log n$$ queries.
On Learning and Testing Decision Tree
In this paper, we study learning and testing decision tree of size and depth that are significantly smaller than the number of attributes $n$.
Bounds for the Number of Tests in Non-Adaptive Randomized Algorithms for Group Testing
In this paper, we study the measures $$c_{\cal M}(d)=\lim_{n\to \infty} \frac{m_{\cal M}(n, d)}{\ln n} \mbox{ and } c_{\cal M}=\lim_{d\to \infty} \frac{c_{\cal M}(d)}{d}.$$ In the literature, the analyses of such models only give upper bounds for $c_{\cal M}(d)$ and $c_{\cal M}$, and for some of them, the bounds are not tight.
Adaptive Exact Learning of Decision Trees from Membership Queries
In this paper we study the adaptive learnability of decision trees of depth at most $d$ from membership queries.
On Learning Graphs with Edge-Detecting Queries
We then give two two-round Monte Carlo algorithms, the first asks $O(m^{4/3}\log n)$ queries for any $n$ and $m$, and the second asks $O(m\log n)$ queries when $n>2^m$.
On Polynomial time Constructions of Minimum Height Decision Tree
The second result is a polynomial time $(\ln 2) DEN(A)$-approximation (and therefore $(\ln 2) ETD(A)$-approximation) algorithm for the depth of the decision tree of $A$.
Non-Adaptive Randomized Algorithm for Group Testing
We show that there is a linear time decoding for such test and for $d\to \infty$ the number of tests converges to the number of tests with the separability property and is therefore optimal (in the RID model).
The Maximum Cosine Framework for Deriving Perceptron Based Linear Classifiers
Moreover, we construct a cosine bound from which we build the Maximum Cosine Perceptron algorithm or, for short, the MCP algorithm.
Exact Learning of Juntas from Membership Queries
In this paper, we study adaptive and non-adaptive exact learning of Juntas from membership queries.
Learning Disjunctions of Predicates
Our algorithm asks at most $|F| \cdot OPT(F_\vee)$ membership queries where $OPT(F_\vee)$ is the minimum worst case number of membership queries for learning $F_\vee$.
Exact Learning from an Honest Teacher That Answers Membership Queries
Given a teacher that holds a function $f:X\to R$ from some class of functions $C$.
Non-Adaptive Learning a Hidden Hipergraph
We give a new deterministic algorithm that non-adaptively learns a hidden hypergraph from edge-detecting queries.
Learning Boolean Halfspaces with Small Weights from Membership Queries
We also give a non-adaptive proper learning algorithm that asks $n^{O(t^3)}$ membership queries.
On Exact Learning Monotone DNF from Membership Queries
In this paper, we study the problem of learning a monotone DNF with at most $s$ terms of size (number of variables in each term) at most $r$ ($s$ term $r$-MDNF) from membership queries.
