arXiv research

Optimizes ranking from click feedback in a bandit setting.

The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.

In this work, we propose learnable Bernoulli dropout (LBD), a new model-agnostic dropout scheme that considers the dropout rates as parameters jointly optimized with other model parameters. By probabilistic modeling of Bernoulli dropout, our method enables more robust prediction and uncertainty quantification in deep m…

The multi-armed bandit (MAB) problem is a classical learning task that exemplifies the exploration-exploitation tradeoff. However, standard formulations do not take into account {\em risk}. In online decision making systems, risk is a primary concern. In this regard, the mean-variance risk measure is one of the most co…

A new gradient estimator reduces variance near boundaries for binary latent variables.

The paper develops sampling methods for ocean phenomena based on temperature and salinity measurements.

In this paper, we are concerned with obtaining distribution-free concentration inequalities for mixture of independent Bernoulli variables that incorporate a notion of variance. Missing mass is the total probability mass associated to the outcomes that have not been seen in a given sample which is an important quantity…

Characterizes symmetric Bernoulli distributions with minimal convex sums.

The key idea of Bayesian optimization is replacing an expensive target function with a cheap surrogate model. By selection of an acquisition function for Bayesian optimization, we trade off between exploration and exploitation. The acquisition function typically depends on the mean and the variance of the surrogate mod…

Upper bound on expected supremum of Bernoulli process.

When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed. To circumvent this problem state-of-the-art algorithms replace the true weights with non-negative unbiased estimates. This algorithm is still valid but at the cost of higher variance of the resulting fi…

In this paper, we consider the multivariate Bernoulli distribution as a model to estimate the structure of graphs with binary nodes. This distribution is discussed in the framework of the exponential family, and its statistical properties regarding independence of the nodes are demonstrated. Importantly the model can e…

Finite index solutions to Bernoulli problem are always axially symmetric.

In this paper, we derive a useful lower bound for the Kullback-Leibler divergence (KL-divergence) based on the Hammersley-Chapman-Robbins bound (HCRB). The HCRB states that the variance of an estimator is bounded from below by the Chi-square divergence and the expectation value of the estimator. By using the relation b…

Proves a principle for one-phase Bernoulli problem minimizers.

A very simple event frequency approximation algorithm that is sensitive to event timeliness is suggested. The algorithm iteratively updates categorical click-distribution, producing (path of) a random walk on a standard $n$-dimensional simplex. Under certain conditions, this random walk is self-similar and corresponds …

A/B testing refers to the task of determining the best option among two alternatives that yield random outcomes. We provide distribution-dependent lower bounds for the performance of A/B testing that improve over the results currently available both in the fixed-confidence (or delta-PAC) and fixed-budget settings. When…

Bayesian autoencoders improve OOD detection by addressing Bernoulli likelihood issues.

This paper proposed a new regression model called $l_1$-regularized outlier isolation and regression (LOIRE) and a fast algorithm based on block coordinate descent to solve this model. Besides, assuming outliers are gross errors following a Bernoulli process, this paper also presented a Bernoulli estimate model which, …

New hashing method improves document retrieval precision.

We solve Euler equations on graph manifolds, classifying steady flows with Morse-Bott Bernoulli functions.

The paper cleans label noise in supervised classification using Bernoulli sampling.

A cornerstone of human statistical learning is the ability to extract temporal regularities / patterns from random sequences. Here we present a method of computing pattern time statistics with generating functions for first-order Markov trials and independent Bernoulli trials. We show that the pattern time statistics c…

This paper tackles open problem of tight bounds for KBs with Bernoulli rewards.

Dasgupta and Shulman showed that a two-round variant of the EM algorithm can learn mixture of Gaussian distributions with near optimal precision with high probability if the Gaussian distributions are well separated and if the dimension is sufficiently high. In this paper, we generalize their theory to learning mixture…

Spectral method speeds fitting of binary time series models.

A new method for efficient nonlinear process monitoring using random Bernoulli features.

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…

A new method uses Mean Field Games to optimize mixture models of Bernoulli and categorical distributions.

Variational autoencoders (VAE) have quickly become a central tool in machine learning, applicable to a broad range of data types and latent variable models. By far the most common first step, taken by seminal papers and by core software libraries alike, is to model MNIST data using a deep network parameterizing a Berno…

First order invariants of generic immersions of manifolds of dimension nm-1 into manifolds of dimension n(m+1)-1, m,n>1 are constructed using the geometry of self-intersections. The range of one of these invariants is related to Bernoulli numbers. As by-products some geometrically defined invariants of regular homotopy…

Paper explores exact recovery of communities in weighted graphs using Gaussian and exponential distributions.

New $L$-functions for 3-manifolds connect to Witten invariants and relate to generalized Bernoulli polynomials.

Improved regret bounds for DP-KLUCB and DP-IMED in Bernoulli bandits.

Paper compares credit portfolio risks using robust Bernoulli mixture models.

New acquisition functions improve Bernoulli LSE.

A new framework predicts links in time-dependent networks using Bernoulli autoregression.

Let $\{T^t\}$ be a smooth flow with positive speed and positive topological entropy on a compact smooth three dimensional manifold, and let $μ$ be an ergodic measure of maximal entropy. We show that either $\{T^t\}$ is Bernoulli, or $\{T^t\}$ is isomorphic to the product of a Bernoulli flow and a rotational flow. Appli…

The paper proves ML estimators are strongly consistent for identifying edge weights in BAR models.

BeMF improves recommendation reliability in recommender systems.

Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in high-dimensional non-linear function estimation problems. The new procedure is…

Study analyzes symmetric two-armed Bernoulli bandit problem with zero mean gap.

Exact simulation of correlated binary outcomes using PMF constraints and linear programming.

A new sampling method balances multi-label datasets by preserving category frequency order.

A new RBM model handles both linear and log-amplitude spectrograms.

We introduce a novel multivariate random process producing Bernoulli outputs per dimension, that can possibly formalize binary interactions in various graphical structures and can be used to model opinion dynamics, epidemics, financial and biological time series data, etc. We call this a Bernoulli Autoregressive Proces…

Proposes a non-parametric method for deep discrete latent variable models.

The beta-Bernoulli process provides a Bayesian nonparametric prior for models involving collections of binary-valued features. A draw from the beta process yields an infinite collection of probabilities in the unit interval, and a draw from the Bernoulli process turns these into binary-valued features. Recent work has …