Starting from the seminal works of Tukey (1960) and Huber (1964), the field of robust statistics asks: Are there estimators that provable work in the presence of noise? The trouble is that all known provably robust estimators are also hard to compute in high-dimensions. Here, we study a basic problem in robust statistics, posed in various forms in the above works. Given corrupted samples from a high-dimensional Gaussian, are there efficient algorithms to accurately estimate its parameters? We give the…

Abstract: It is common to collect high-dimensional data that are structured as a multiway array or tensor; examples include multivariate categorical data that are organized as a contingency table, sequential data on nucleotides or animal vocalizations, and neuroscience data on brain networks. In each of these cases, there is interest in doing inference on the joint probability distribution of the data and on interpretable functionals of this probability distribution. The goal is to avoid restrictive parametric assumptions, enable both statistical and…

Abstract: (i) Change point detection for networks: We consider the preferential attachment model. We formulate and study the regime where the network transitions from one evolutionary scheme to another. In the large network limit we derive asymptotics for various functionals of the network including degree distribution and maximal degree. We study functional central limit theorems for the evolution of the degree distribution which feed into proving consistency of a proposed estimator of the change point. (ii) Power of choice and network…

 Abstract: We propose an efficient meta-algorithm for Bayesian inference problems based on low-degree polynomials, semidefinite programming, and tensor decomposition. The algorithm is inspired by recent lower bound constructions for sum-of-squares and related to the method of moments. Our focus is on sample complexity bounds that are as tight as possible (up to additive lower-order terms) and often achieve statistical thresholds or conjectured computational thresholds. Our algorithm recovers the best known bounds for partial recovery in the stochastic block model, a…

Abstract: Active learning is a framework for supervised learning that explicitly models, and permits one to control and optimize, the costs of labeling data. The hope is that by carefully selecting which examples to label in an adaptive manner, the number of labels required to learn an accurate classifier is substantially reduced. However, in many learning settings (e.g., when some classes are rare), it is difficult to identify which examples are most informative to label, and existing active learning algorithms are prone to labeling uninformative examples. Based…

Abstract: We describe an emerging research direction regarding the complexity of testing global properties of discrete distributions, when given access to only a few samples from the distribution. Such properties might include testing if two distributions have small statistical distance, testing various independence properties, testing whether a distribution has a specific shape (such as monotone decreasing, k-modal, k-histogram, monotone hazard rate,...), and approximating the entropy. We describe bounds for such testing problems whose sample complexities are sublinear in the size…

Abstract: Spin systems on trees have found applications ranging from the reconstruction of phylogenies to the analysis of networks with community structure. A key feature of such processes is the interplay between the growth of the tree and the decay of correlations along it. How the resulting threshold phenomena impact estimation depends on the problem considered. I will illustrate this on two recent results: 1) the critical threshold of ancestral sequence reconstruction by maximum parsimony on general phylogenies and 2)…

Abstract: Why are we interested in the causal structure of a process? In classical prediction tasks, for example, it seems that no causal knowledge is required. In many situations, however, we are interested in a system's behavior after parts of this system have been changed. Here, causal models become important because they are usually considered invariant under those changes. A causal prediction (which uses only direct causes of the target variable as predictors) remains valid even if we intervene on…