# Exact Learning of Juntas from Membership Queries

## TL;DR

This paper advances the understanding of learning Juntas exactly through membership queries by establishing new bounds and algorithms, narrowing the gap between theoretical limits and practical methods.

## Contribution

It introduces novel techniques for bounding and algorithms, improving the efficiency of exact learning of Juntas from membership queries.

## Key findings

- New bounds for adaptive and non-adaptive learning of Juntas.
- Deterministic and randomized algorithms with reduced query and time complexities.
- Some bounds are tight, indicating fundamental limits or requiring breakthrough techniques.

## Abstract

In this paper, we study adaptive and non-adaptive exact learning of Juntas from membership queries. We use new techniques to find new bounds, narrow some of the gaps between the lower bounds and upper bounds and find new deterministic and randomized algorithms with small query and time complexities. Some of the bounds are tight in the sense that finding better ones either gives a breakthrough result in some long-standing combinatorial open problem or needs a new technique that is beyond the existing ones.