arXiv research

Upper bound on expected supremum of Bernoulli process.

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, …

Bayesian optimization for binomial outputs with multifidelity.

A new method samples DPPs without eigendecomposition, making it faster.

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

Develops methods to construct exchangeable sequences of random multisets.

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 …

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

Estimates parameters of high-dimensional Bernoulli autoregressive process with long-range dependence.

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…

A new method trains discrete EBMs without sampling.

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

This paper solves mapping problems with a novel Gibbs sampling method.

New acquisition functions improve Bernoulli LSE.

Physics-informed model predicts beam stiffness and monitors structural health.

The paper finds unique equilibrium states for geodesic flows on certain surfaces.

Spectral method speeds fitting of binary time series models.

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

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

Study the limits of discrete DPPs to continuous DPPs as set size grows.

Data processing inequalities link Fisher information to local differential privacy constraints.

Improves interpretability of anomaly scores in GBRBM-based detection.

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

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

Sharp bounds found for various risk measures using generalized FGM copulas.

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

If a variational problem comes with no boundary conditions prescribed beforehand, and yet these arise as a consequence of the variation process itself, we speak of a free boundary values variational problem. Such is, for instance, the problem of finding the shortest curve whose endpoints can slide along two prescribed …

Characterizes symmetric Bernoulli distributions with minimal convex sums.

Simple method improves exploration in various decision problems.

Method computes pattern time statistics from random sequences.

New algorithms for sampling DPPs reduce computational cost.

This work tackles community recovery in hypergraphs with measurements of varying sizes.

New binary AA methods improve on existing techniques.

We describe the combinatorial stochastic process underlying a sequence of conditionally independent Bernoulli processes with a shared beta process hazard measure. As shown by Thibaux and Jordan [TJ07], in the special case when the underlying beta process has a constant concentration function and a finite and nonatomic …

New adaptive methods improve dynamic Bernoulli bandit performance.

Simple algorithm approximates rare event frequencies.

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.

New model for multivariate discrete event data with flexible interactions.

A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multi-scoop" generalization of the beta-Bernoulli process. The BNB process is augmented into a beta-gamma-gamma-Poisson hierarchical structure, and applied as a nonparametric Bayesian prior for an infi…

Algorithm learns smooth probability functions from Bernoulli tests with guarantees.

Study optimal stopping for diffusion processes with unknown primitives, applying RL and martingale methods.

We study in detail and explicitly solve the version of Kyle's model introduced in a specific case in \cite{BB}, where the trading horizon is given by an exponentially distributed random time. The first part of the paper is devoted to the analysis of time-homogeneous equilibria using tools from the theory of one-dimensi…

Proves a principle for one-phase Bernoulli problem minimizers.

SMH kernel speeds up Bayesian inference for large datasets.

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

Estimates network structure from incomplete event data.

Extends Kelly Criterion to more complex betting scenarios.