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In the problem of online portfolio selection as formulated by Cover (1991), the trader repeatedly distributes her capital over d assets in each of T>1 rounds, with the goal of maximizing the total return. Cover proposed an algorithm, termed Universal Portfolios, that performs nearly as well as the best (in hindsight) static assignment of a portfolio, with an O(dlog(T)) regret in terms of the logarithmic return. Without imposing any restrictions on the market this guarantee is known to be worstcase optimal, and no other algorithm attaining it has been discovered so far. Unfortunately, Cover's algorithm crucially relies on computing certain ddimensional integral which must be approximated in any implementation; this results in a prohibitive O(d^4(T+d)^14) perround runtime for the fastest known implementation due to Kalai and Vempala (2002). We propose an algorithm for online portfolio selection that admits essentially the same regret guarantee as Universal Portfolios  up to a constant factor and replacement of log(T) with log(T+d)  yet has a drastically reduced runtime of O(d^(T+d)) per round. The selected portfolio minimizes the current logarithmic loss regularized by the logdeterminant of its Hessian  equivalently, the hybrid logarithmicvolumetric barrier of the polytope specified by the asset return vectors. Asmore »Free, publiclyaccessible full text available October 1, 2023

We consider the highdimensional linear regression model and assume that a fraction of the responses are contaminated by an adversary with complete knowledge of the data and the underlying distribution. We are interested in the situation when the dense additive noise can be heavytailed but the predictors have subGaussian distribution. We establish minimax lower bounds that depend on the the fraction of the contaminated data and the tails of the additive noise. Moreover, we design a modification of the square root Slope estimator with several desirable features: (a) it is provably robust to adversarial contamination, with the performance guarantees that take the form of subGaussian deviation inequalities and match the lower error bounds up to logfactors; (b) it is fully adaptive with respect to the unknown sparsity level and the variance of the noise, and (c) it is computationally tractable as a solution of a convex optimization problem. To analyze the performance of the proposed estimator, we prove several properties of matrices with subGaussian rows that could be of independent interest.Free, publiclyaccessible full text available October 1, 2023

This paper addresses the following question: given a sample of i.i.d. random variables with finite variance, can one construct an estimator of the unknown mean that performs nearly as well as if the data were normally distributed? One of the most popular examples achieving this goal is the median of means estimator. However, it is inefficient in a sense that the constants in the resulting bounds are suboptimal. We show that a permutationinvariant modification of the median of means estimator admits deviation guarantees that are sharp up to 1+o(1) factor if the underlying distribution possesses 3+p moments for some p>0 and is absolutely continuous with respect to the Lebesgue measure. This result yields potential improvements for a variety of algorithms that rely on the median of means estimator as a building block. At the core of our argument is a new deviation inequality for the Ustatistics of order that is allowed to grow with the sample size, a result that could be of independent interest. Finally, we demonstrate that a hybrid of the median of means and Catoni's estimator is capable of achieving subGaussian deviation guarantees with nearly optimal constants assuming just the existence of the second moment.Free, publiclyaccessible full text available July 1, 2023

The topic of robustness is experiencing a resurgence of interest in the statistical and machine learning communities. In particular, robust algorithms making use of the socalled median of means estimator were shown to satisfy strong performance guarantees for many problems, including estimation of the mean, covariance structure as well as linear regression. In this work, we propose an extension of the median of means principle to the Bayesian framework, leading to the notion of the robust posterior distribution. In particular, we (a) quantify robustness of this posterior to outliers, (b) show that it satisfies a version of the Bernsteinvon Mises theorem that connects Bayesian credible sets to the traditional confidence intervals, and (c) demonstrate that our approach performs well in applications.Free, publiclyaccessible full text available July 1, 2023

We study the supervised clustering problem under the twocomponent anisotropic Gaussian mixture model in high dimensions in the nonasymptotic setting. We first derive a lower and a matching upper bound for the minimax risk of clustering in this framework. We also show that in the highdimensional regime, the linear discriminant analysis (LDA) classifier turns out to be suboptimal in a minimax sense. Next, we characterize precisely the risk of regularized supervised least squares classifiers under $\ell_2$ regularization. We deduce the fact that the interpolating solution (0 training error solution) may outperform the regularized classifier, under mild assumptions on the covariance structure of the noise. Our analysis also shows that interpolation can be robust to corruption in the covariance of the noise when the signal is aligned with the ``clean'' part of the covariance, for the properly defined notion of alignment. To the best of our knowledge, this peculiar phenomenon has not yet been investigated in the rapidly growing literature related to interpolation. We conclude that interpolation is not only benign but can also be optimal and in some cases robust.

Abstract: We consider the problem of estimating the covariance structure of a random vector $Y\in \mathbb R^d$ from a sample $Y_1,\ldots,Y_n$. We are interested in the situation when d is large compared to n but the covariance matrix $\Sigma$ of interest has (exactly or approximately) low rank. We assume that the given sample is (a) $\epsilon$adversarially corrupted, meaning that $\epsilon$ fraction of the observations could have been replaced by arbitrary vectors, or that (b) the sample is i.i.d. but the underlying distribution is heavytailed, meaning that the norm of Y possesses only 4 finite moments. We propose an estimator that is adaptive to the potential lowrank structure of the covariance matrix as well as to the proportion of contaminated data, and admits tight deviation guarantees despite rather weak assumptions on the underlying distribution. Finally, we discuss the algorithms that allow to approximate the proposed estimator in a numerically efficient way.

The question of fast convergence in the classical problem of high dimensional linear regression has been extensively studied. Arguably, one of the fastest procedures in practice is Iterative Hard Thresholding (IHT). Still, IHT relies strongly on the knowledge of the true sparsity parameter s. In this paper, we present a novel fast procedure for estimation in the high dimensional linear regression. Taking advantage of the interplay between estimation, support recovery and optimization we achieve both optimal statistical accuracy and fast convergence. The main advantage of our procedure is that it is fully adaptive, making it more practical than state of the art IHT methods. Our procedure achieves optimal statistical accuracy faster than, for instance, classical algorithms for the Lasso. Moreover, we establish sharp optimal results for both estimation and support recovery. As a consequence, we present a new iterative hard thresholding algorithm for high dimensional linear regression that is scaled minimax optimal (achieves the estimation error of the oracle that knows the sparsity pattern if possible), fast and adaptive.

This paper investigates asymptotic properties of a class of algorithms that can be viewed as robust analogues of the classical empirical risk minimization. These strategies are based on replacing the usual empirical average by a robust proxy of the mean, such as the (version of) the medianofmeans estimator. It is well known by now that the excess risk of resulting estimators often converges to 0 at optimal rates under much weaker assumptions than those required by their “classical” counterparts. However, much less is known about the asymptotic properties of the estimators themselves, for instance, whether robust analogues of the maximum likelihood estimators are asymptotically efficient. We make a step towards answering these questions and show that for a wide class of parametric problems, minimizers of the appropriately defined robust proxy of the risk converge to the minimizers of the true risk at the same rate, and often have the same asymptotic variance, as the estimators obtained by minimizing the usual empirical risk. Moreover, our results show that robust algorithms based on the socalled “minmax” type procedures in many cases provably outperform, is the asymptotic sense, algorithms based on direct risk minimization.

This paper investigates robust versions of the general empirical risk minimization algorithm, one of the core techniques underlying modern statistical methods. Success of the empirical risk minimization is based on the fact that for a ``wellbehaved'' stochastic process $\{ f(X), \ f\in \m F \}$ indexed by a class of functions $f\in \mathcal F$, averages $\frac{1}{N}\sum_{j=1}^N f(X_j)$ evaluated over a sample $X_1, \ldots, X_N$ of i.i.d. copies of $X$ provide good approximation to the expectations $\mathbb E f(X)$, uniformly over large classes $f\in \mathcal F$. However, this might no longer be true if the marginal distributions of the process are heavytailed or if the sample contains outliers. We propose a version of empirical risk minimization based on the idea of replacing sample averages by robust proxies of the expectations, and obtain highconfidence bounds for the excess risk of resulting estimators. In particular, we show that the excess risk of robust estimators can converge to $0$ at fast rates with respect to the sample size $N$, referring to the rates faster than $N^{1/2}$. We discuss implications of the main results to the linear and logistic regression problems, and evaluate the numerical performance of proposed methods on simulated and real data.

Let X be a random variable with unknown mean and finite variance. We present a new estimator of the mean of X that is robust with respect to the possible presence of outliers in the sample, provides tight subGaussian deviation guarantees without any additional assumptions on the shape or tails of the distribution, and moreover is asymptotically efficient. This is the first estimator that provably combines all these qualities in one package. Our construction is inspired by robustness properties possessed by the selfnormalized sums. Finally, theoretical findings are supplemented by numerical simulations highlighting the strong performance of the proposed estimator in comparison with previously known techniques.