We first state a generic inequality based on Lemma 2. This differs from earlier approaches, which instead combine Markov’s inequality with a result similar to Lemma 2 (e.g., Tropp, 2011a, Theorem 3.6).

We derive exponential tail inequalities for sums of random matrices with no dependence on the explicit matrix dimensions. These are similar to the ma…

Most results in nonparametric regression theory are developed only for the case of additive noise. In such a setting many smoothing techniques includ…

Differential privacy provides a rigorous framework for privacy-preserving data analysis. This paper proposes the first differentially private procedu…

Most of the work on the structural nested model and g-estimation for causal inference in longitudinal data assumes a discrete-time underlying data ge…

We study the problem of two-sample comparison with categorical data when the contingency table is sparsely populated. In modern applications, the num…

Numerous statistics have been proposed for the measure of offensive ability in major league baseball. While some of these measures may offer moderate…

We propose a computationally efficient random walk on a convex body which rapidly mixes and closely tracks a time-varying log-concave distribution. W…

We present a new computational approach to approximating a large, noisy data table by a low-rank matrix with sparse singular vectors. The approximati…

Covariate balance is a conventional key diagnostic for methods used estimating causal effects from observational studies. Recently, there is an emerg…

The application of existing methods for constructing optimal dynamic treatment regimes is limited to cases where investigators are interested in opti…

We introduce a new model of competition on growing networks. This extends the preferential attachment model, with the key property that node choices …

Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the p…

We have developed a sophisticated statistical model for predicting the hitting performance of Major League baseball players. The Bayesian paradigm pr…

Discussion of "A significance test for the lasso" by Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani [arXiv:1301.7161].

"Value alignment" (VA) is considered as one of the top priorities in AI research. Much of the existing research focuses on the "A" part and not the "…

Gradient-based optimization algorithms can be studied from the perspective of limiting ordinary differential equations (ODEs). Motivated by the fact …

In this work, we have provided deterministic inequalities for a class of smooth M-estimators that unify the classical asymptotic analysis under various dependence settings. Furthermore, these inequalities readily yield tail bounds for estimation err…

Ever since the proof of asymptotic normality of maximum likelihood estimator by Cramer (1946), it has been understood that a basic technique of the T…

Understanding the relationship between change in crime over time and the geography of urban areas is an important problem for urban planning. Accurat…

We establish bounds on the KL divergence between two multivariate Gaussian distributions in terms of the Hamming distance between the edge sets of th…

Predicting historic temperatures based on tree rings, ice cores, and other natural proxies is a difficult endeavor. The relationship between proxies …

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