Copyright 2017 by the author(s). We introduce PseudoNet, a new pseudolikelihood-based estimator of the inverse covariance matrix, that has a number of useful statistical and computational properties. We show, through detailed experiments with synthetic as well as real-world finance and wind power data, that PseudoNet outperforms related methods in terms of estimation error and support recovery, making it well-suited for use in a downstream application, where obtaining low estimation error can be important. We also show, under regularity conditions, that PseudoNet is consistent. Our proof assumes the existence of accurate estimates of the diagonal entries of the underlying inverse covariance matrix; we additionally provide a two-step method to obtain these estimates, even in a high-dimensional setting, going beyond the proofs for related methods. Unlike other pseudolikelihood-based methods, we also show that PseudoNet does not saturate, i.e., in high dimensions, there is no hard limit on the number of nonzero entries in the PseudoNet estimate. We present a fast algorithm as well as screening rules that make computing the PseudoNet estimate over a range of tuning parameters tractable.

In STEM domains, students are expected to acquire domainknowledge from visual representations that they may not yetbe able to interpret. Such learning requires perceptual flu-ency, or the ability to intuitively and rapidly see the underlyingconcepts in visuals and to translate between them. Perceptualfluency is acquired via nonverbal, implicit learning processes.Thus far, we have lacked a principled approach for identify-ing a sequence of perceptual fluency problems that promoterobust learning. Here, we describe how a novel machine learn-ing technique can generate an optimal sequence of perceptualfluency problems. In a human experiment, we show that amachine-generated sequence outperforms both a random se-quence and a sequence generated by a human domain expert.Interestingly, the machine-generated sequence resulted in sig-nificantly lower accuracy during training, but higher posttestaccuracy. This suggests that the machine-generated sequenceinduced desirable difficulties. To our knowledge, our study isthe first to show that machine learning can yield desirable dif-ficulties for perceptual learning

In a categorization task involving both labeled and unlabeled data, it has been shown that humans make use of the underlying distribution of the unlabeled examples. It has also been shown that humans are sensitive to shifts in this distribution, and will change predicted classifications based on these shifts. It is not immediately obvious what causes these shifts – what specific properties of these distributions humans are sensitive to. Assuming a parametric model of human categorization learning, we can ask which parameters or sets of parameters humans fix after exposure to labeled data and which are adjustable to fit subsequent unlabeled data. We formulate models to describe different parameter sets which humans may be sensitive to and a dataset which optimally discriminates among these models. Experimental results indicate that humans are sensitive to all parameters, with the closest model fit being an unconstrained version of semi-supervised learning using expectation maximization.