arXiv research

Algorithm learns smooth probability functions from Bernoulli tests with guarantees.

Unified framework for structure learning via conditional independence testing.

Study phase transitions in identifying infected individuals using group testing.

Rule-based classifiers quantify uncertainty using Bernoulli random variables.

The paper analyzes how employers can efficiently screen candidates using multiple tests, considering both skill estimation and fairness.

BeMF improves recommendation reliability in recommender systems.

Introduces t-CCS for flexible tensor sampling.

Characterizes symmetric Bernoulli distributions with minimal convex sums.

Upper bound on expected supremum of Bernoulli process.

Paper shows attackers can steal model weights with just noise inputs.

A novel method for estimating Bayesian network (BN) parameters from data is presented which provides improved performance on test data. Previous research has shown the value of representing conditional probability distributions (CPDs) via neural networks(Neal 1992), noisy-OR gates (Neal 1992, Diez 1993)and decision tre…

No algorithm outperforms uniform sampling in A/B testing.

Simple algorithm approximates rare event frequencies.

A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to …

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.

Proves a principle for one-phase Bernoulli problem minimizers.

A drone-based MOT algorithm tracks vehicles using neural network detections and TPMBM filter.

A new distribution fixes a common error in VAEs, improving image quality.

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, …

GLR-klUCB detects change-points in non-stationary bandits efficiently.

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.

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.

The structure of a Bayesian network encodes most of the information about the probability distribution of the data, which is uniquely identified given some general distributional assumptions. Therefore it's important to study the variability of its network structure, which can be used to compare the performance of diff…

Proposes a new method for feature selection in non-linear functions.

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…

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

A new algorithm resamples Bernoulli race particle filters using true weights.

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…

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.

Estimates network structure from incomplete event data.

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.