arXiv research

Paper develops PAC-Bayes bounds for unknown linear systems.

New strategy optimally identifies best arm in unknown variance Gaussian bandits.

Improved SGD with AdaGrad stepsizes adapts to unknown parameters and unbounded gradients.

New algorithm for competing influence spread in unknown networks.

In the present paper, we studied a Dynamic Stochastic Block Model (DSBM) under the assumptions that the connection probabilities, as functions of time, are smooth and that at most $s$ nodes can switch their class memberships between two consecutive time points. We estimate the edge probability tensor by a kernel-type p…

We study the minimax optimal rates for estimating a range of Integral Probability Metrics (IPMs) between two unknown probability measures, based on $n$ independent samples from them. Curiously, we show that estimating the IPM itself between probability measures, is not significantly easier than estimating the probabili…

A method for classifying points with minimal queries using Hermite polynomials.

Optimal nonparametric regression estimator adapts to unknown smoothness.

Study minimax off-policy evaluation in multi-armed bandits with known and unknown behavior policies.

Nonparametric adaptive robust control tackles model uncertainty in stochastic processes.

New mixture models for clustering and density estimation of unknown distributions.

We give a highly efficient "semi-agnostic" algorithm for learning univariate probability distributions that are well approximated by piecewise polynomial density functions. Let $p$ be an arbitrary distribution over an interval $I$ which is $τ$-close (in total variation distance) to an unknown probability distribution $…

We consider the setting of linear regression in high dimension. We focus on the problem of constructing adaptive and honest confidence sets for the sparse parameter θ, i.e. we want to construct a confidence set for theta that contains theta with high probability, and that is as small as possible. The l_2 diameter of a …

Safety filter for unknown discrete-time systems with learned models and noise covariance.

In many applications (in particular information systems, such as pattern recognition, machine learning, cheminformatics, bioinformatics to name but a few) the assessment of uncertainty is essential - i.e., the estimation of the underlying probability distribution function. More often than not, the form of this function…

We study the minimax optimal rate for estimating the Wasserstein-$1$ metric between two unknown probability measures based on $n$ i.i.d. empirical samples from them. We show that estimating the Wasserstein metric itself between probability measures, is not significantly easier than estimating the probability measures u…

One of the central themes in the classification task is the estimation of class posterior probability at a new point $\bf{x}$. The vast majority of classifiers output a score for $\bf{x}$, which is monotonically related to the posterior probability via an unknown relationship. There are many attempts in the literature …

Algorithm identifies nearest mode in noisy data.

Method determines credit transition matrix from cumulative default probabilities.

The paper analyzes regret in online recommendation systems with constraints.

New bounds quantify estimation error in kernel-based system identification with unknown hyperparameters.

Paper presents a method to reduce prediction variance of DNNs for unknown systems.

Study optimizes sampling to avoid extreme tail risks in unknown heavy-tailed distributions.

Algorithm minimizes regret while adhering to unknown safety constraints.

Study best arm identification in restless bandits with unknown TPMs.

We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentag…

This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between di…

The problem of estimating an unknown discrete distribution from its samples is a fundamental tenet of statistical learning. Over the past decade, it attracted significant research effort and has been solved for a variety of divergence measures. Surprisingly, an equally important problem, estimating an unknown Markov ch…

Safe Bayesian optimization method using information theory.

We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate of our posterior update rule to the true probability function in…

Statistical machine learning plays an important role in modern statistics and computer science. One main goal of statistical machine learning is to provide universally consistent algorithms, i.e., the estimator converges in probability or in some stronger sense to the Bayes risk or to the Bayes decision function. Kerne…

Proposes a differentiable hypergeometric distribution for learning group importance.

MAXENT method outperforms ML in sparse data with specific prior correlations.

New algorithms estimate and test collision probability with near-optimal sample complexity.

New formulae identify discrete probability laws without needing normalization constants.

A new growth model for dynamic networks using Markovian latent points.

Paper explores stability, regularization, and gradient flows for stochastic inverse problems.

We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an upper bound on the corrupted data fraction. The method minimizes a risk function defined by a non-parametric distribution with unknown prob…

New method predicts sets under unknown covariate shift with high confidence.

New theory uses probability sets for data variability, improving machine learning.

Recently, fundamental conditions on the sampling patterns have been obtained for finite completability of low-rank matrices or tensors given the corresponding ranks. In this paper, we consider the scenario where the rank is not given and we aim to approximate the unknown rank based on the location of sampled entries an…

Paper tackles SMPC for linear systems with unknown noise distribution.

New method constructs confidence bands for ODE models with unknown regulatory effects.

Efficiently matches random graphs with inhomogeneous edge probabilities.

Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology. In this paper we present a model-free RL algorithm to synthesize control policies that maximize the probability of satisfying h…

Statistical test verifies long-term rating system calibration with overlapping time windows.

Constrained Markov Decision Processes are a class of stochastic decision problems in which the decision maker must select a policy that satisfies auxiliary cost constraints. This paper extends upper confidence reinforcement learning for settings in which the reward function and the constraints, described by cost functi…

New algorithm detects changes quickly without knowing parameters, near optimally.