In this talk, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution p over a poset is monotone if, for any pair of domain elements x and y such that x \preceq y, p(x) \leq p(y). I am going to present the proof sketch of achieving a lower bound for this problem over a few posets, e.g., matchings, and hypercubes. The main idea of these lower bounds is the following: we introduce a new property called *bigness* over a finite domain, where the distribution is T-big if the minimum probability for any domain element is at least T. Relying on the framework of [Wu-Yang'15], we establish a lower bound of \Omega(n/\log n) for testing bigness of distributions on domains of size n. We then build on these lower bounds to give \Omega(n/\log{n}) lower bounds for testing monotonicity over a matching poset of size n and significantly improved lower bounds over the hypercube poset. Although finding a tight upper bound for testing monotonicity remains open, I will also discuss the steps we took towards the upper bound as well.

Joint work with: Themis Gouleakis, John Peebles, Ronitt Rubinfeld, and Anak Yodpinyanee.

Video Recording