Binary BPS improves sampling for easy mixtures.
problem Sampling from binary distributions efficiently.
method Generalized Bouncy Particle Sampler for binary variables.
result Binary BPS outperforms binary HMC for easy mixtures.
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,181 papers · 148 categories
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48 results for “Binary distributions”
Paper constructs unfaithful probability distributions in binary causal graphs.
problem Unfaithful probability distributions in binary causal graphs.
method Constructs unfaithful probability distributions in binary causal graphs.
result Examples of unfaithful probability distributions in binary causal graphs.
Derives pricing formulae for power binary and normal distribution standard options.
problem Developing pricing models for binary and standard options.
method Incorporates Buchen's formulae into power binary options and derives a formula for normal distribution standard options.
result Derives pricing formulae for power binary and normal distribution standard options.
The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraintscs.IT
Paper resolves open problems on sample complexity in binary hypothesis testing.
problem Open problems in distributed simple binary hypothesis testing under information constraints.
method One-shot lower bound on Bayes error, streamlined sample complexity formula, reverse data-processing inequality.
result Optimally tight sample complexity bounds for communication-constrained simple binary hypothesis testing.
BIND removes background noise from binary matrices, improving detection accuracy and fairness.
problem Real data often violates the i.i.d assumption for binary matrix entries, leading to inaccurate detection.
method BIND optimizes detection by estimating row- and column-wise mixture distributions and eliminating background noise.
result BIND effectively removes background noise and increases detection accuracy and fairness.
Efficiently estimates binary product distributions with privacy.
problem Estimating means of binary product distributions privately and accurately.
method Polynomial time, pure differential privacy approach.
result Optimal sample complexity with polylogarithmic factors.
BinaryGAN uses binary neurons for GAN training.
problem Training GANs with binary outputs.
method End-to-end backpropagation with sigmoid-adjusted straight-through estimators.
result BinaryGAN can generate binary-valued predictions.
A new method combines simple binary classifiers to build complex multiclass classifiers, achieving performance limits in a Gaussian setting.
problem Building a sophisticated multiclass classifier from simple binary decisions.
method Combining simple binary classifiers to form a -class classifier.
result Explicit performance bounds across various decoding and dimensional regimes for a stylized Gaussian setting.
Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraintsmath.ST
Study binary hypothesis testing with privacy and communication constraints.
problem Binary hypothesis testing under local differential privacy and communication constraints.
method Qualifies results as minimax or instance optimal, develops instance-optimal algorithms.
result Achieves minimum possible sample complexity under both privacy and communication constraints.
Distribution-free binary classification: prediction sets, confidence intervals and calibrationstat.ML
Study three types of uncertainty quantification for binary classification without distributional assumptions.
problem Uncertainty quantification for binary classification in a distribution-free setting.
method Established theorems connecting calibration, confidence intervals, and prediction sets for score-based classifiers.
result Distribution-free calibration is only possible using scoring functions that partition feature space into countably many sets.
Modified Metropolis algorithm ensures convergence for multivariate binary distributions with fixed-order updates.
problem Infeasibility of standard Metropolis algorithm for multivariate binary distributions with fixed-order updates.
method Proposed a modified Metropolis transition operator ensuring irreducibility and convergence.
result Ensures convergence to the limiting distribution in multivariate binary case with fixed-order updates.
Simple model forecasts asset return distributions precisely.
problem Forecasting dynamic return distributions of assets.
method Ordered binary choice regression with sign predictability constraints.
result Precise forecasts of asset return distributions using past indicators and volatility proxies.
New binary AA methods improve on existing techniques.
problem Binary data limitations in AA methods.
method Proposed two optimization frameworks for binary AA.
result Superior performance on synthetic and real binary data.
New tests for binary classification regression functions without distribution assumptions.
problem Testing regression functions in binary classification without distributional assumptions.
method Conditional kernel mean embeddings and resampling-based framework.
result Distribution-free hypothesis tests with exact type I error control.
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence. Recently, some techniques to discover a unique causal model have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limit…
RBMs model binary interactions with hidden node activation effects.
problem Understanding how RBM hidden node activation affects binary variable distributions.
method Investigated RBM marginal distributions with different hidden node activation functions.
result Found exact expressions for RBM marginals as interacting binary variables.
The study examines the discrepancies between binary forecasts and real-world outcomes, revealing their often misleading nature.
problem The confusion between binary forecasts and real-world payoffs in decision-making and prediction.
method Comparative analysis of binary forecasts, bets, and real-world continuous payoffs under different tail conditions.
result Binary forecasting abilities do not translate to better real-world performance, and vice versa, especially under nonlinearities.
New algorithm separates audio sources better using alpha-stable distributions.
problem Improving audio source separation using complex distributions.
method Estimating mixtures of alpha-stable distributions using characteristic function matching.
result Better separation performance than Gaussian-based methods.
diproperm tests differences in HDLSS data with binary classifiers.
problem Testing differences in HDLSS data with binary classifiers.
method DiProPerm test for binary linear classifiers.
result Validates the DiProPerm test on real-world data.
Alternative to convolutions using decision trees for neural networks.
problem Replacing complex convolutions with simpler decision-based layers.
method Binary decisions as indices to conditional distributions, trained using backpropagation.
result Performance similar to conventional neural networks, with runtime improvements.
A novel method for feature selection using a reparameterized logitNormal distribution.
problem Feature selection for reconstruction in high-dimensional data.
method Introducing a reparameterization of the logitNormal distribution to address differentiability and covariance issues.
result The method provides an effective exploration scheme and efficient feature selection for reconstruction.
Binary testing for softmax models requires many samples, similar to leverage score models.
problem Binary hypothesis testing for softmax models and leverage score models.
method Analyzing sample complexity and drawing analogies between models.
result Sample complexity is asymptotically \(O(ε^{-2})\), where \(ε\) is the distance between model parameters.
Quantum circuits represent binary classification trees with binary features.
problem Classifying data using binary classification trees with binary features.
method Quantum circuits and probabilistic approach for traversing decision trees.
result First realization of a decision tree classifier on a quantum device.
New insights into identifying mixtures of product distributions using Hadamard extensions.
problem Identifying mixtures of product distributions on binary variables.
method Analysis of Hadamard extensions of matrix products.
result Conditions for full column rank of Hadamard extensions.
The paper analyzes recalibration methods for binary classifiers under distribution shift.
problem Recalibrating binary classifiers to match a target prior probability.
method Analysis of distribution shift assumptions and proposal of new recalibration methods.
result QMM methods provide conservative results for risk weights functions.
Paper analyzes impact of PRM on binary random variables and distribution shifts.
problem Impact of performative risk minimization on binary random variables and distribution shifts.
method Formulated two measures of impact, derived explicit formulas for full information, and provided estimators for partial information.
result PRM can have amplified side effects compared to methods that do not model data shift.
A new method for binary ICA using non-stationary sources.
problem Independent component analysis of binary data.
method Linear mixing model in latent space, followed by binary observation model with non-stationary sources.
result Proves non-identifiability with few observed variables but identifies with more variables.
The paper develops efficient estimators for semi-parametric binary models in distributed computing.
problem Estimation and inference challenges in large-scale data under non-smooth objective functions.
method Proposes one-shot and multi-round divide-and-conquer estimators with adaptive kernel smoothing to relax constraints and achieve superlinear optimization error.
result Establishes quadratic convergence up to optimal statistical error rate and handles dataset heterogeneity and high-dimensional sparse parameters.
Continuous Sweep improves binary quantifier performance.
problem Estimating class prevalence in datasets.
method Parametric binary quantifier inspired by Median Sweep, using parametric class distributions and mean of Adjusted Count estimates.
result Continuous Sweep outperforms other quantifiers in simulations and empirical data analysis.
New test for binary treatment effects using kernel methods.
problem Testing distributional effects of binary treatments.
method Kernel-based doubly-robust test, avoiding permutations.
result Valid type-I error with computational efficiency.
Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulationstat.ML
New method recovers predictions from unobservable source subpopulation in binary classification.
problem Challenging binary classification with unobservable subpopulation in source domain.
method Distribution matching method to estimate subpopulation proportions, rigorous derivation of prediction models.
result Our method outperforms naive benchmarks in synthetic and real-world datasets.
ENTED efficiently decomposes binary and count tensors using nonparametric Gaussian processes.
problem Handling high-dimensional and sparse binary and count data with traditional tensor decompositions.
method ENTED uses nonparametric Gaussian processes and sparse orthogonal variational inference to handle binary and count tensors.
result ENTED outperforms traditional methods in binary and count tensor completion tasks.
Ising distribution as a latent variable modelcond-mat.dis-nn
This study suggests replacing Ising distribution with Cox distribution.
problem Handling correlated binary data efficiently.
method Exploring conditions for replacing Ising distribution with Cox distribution as a latent variable model.
result The Ising distribution can be treated as a latent variable model with a quasi-normal distribution.
Efficient computation of predictive probabilities in probit models via expectation propagationstat.CO
Binary classification models get more efficient predictive probabilities.
problem Computing predictive probabilities in Bayesian probit models is computationally challenging.
method Use of expectation propagation (EP) to find a closed-form expression for predictive probabilities.
result Closed-form predictive probabilities improve over existing methods.
Unbiased wealth exchanges always lead to inequality.
problem Understanding wealth distribution in unbiased binary exchange systems.
method Analytical demonstration of unbiased binary exchanges leading to perfect inequality.
result Any system driven by unbiased binary exchanges will reach perfect inequality and zero mobility.
An analysis of binary isotonic regression: degrees of freedom and implications for calibrationstat.ML
Sharp analysis of isotonic regression for binary data, improving calibration bounds.
problem Improving the calibration of probabilistic predictors using isotonic regression.
method Sharp finite-sample characterization of isotonic regression's degrees of freedom using analytic number theory.
result First nontrivial distribution-free guarantee on Expected Calibration Error (ECE) of isotonic regression.
Two models of binary variables are shown to represent the same distributions.
problem Comparing two models of binary variables.
method Semi-algebraic description and maximum likelihood estimates.
result Two models represent the same set of distributions.
The paper proposes using theoretical ROC curves to categorize classifier responses.
problem The lack of explicit probability distributions for classifier responses in machine learning.
method Fit beta distributions to classifier responses and use them to categorize responses into different classes.
result Established a categorization of classifier responses into classes with different ROC curve extremal behaviors.
New algorithm optimizes margin distribution in binary classifiers.
problem Optimizing margin distribution in binary classifiers.
method Proposes an algorithm that searches the hypothesis space to ensure a pre-set margin level is a robust estimator of the margin location.
result Empirical tests show the method is effective and promising for classification.
BEGIN network models binary data without parametric assumptions.
problem Conditional independence in non-parametric families of binary data.
method BEGIN network models binary data using sparse linear representations and block factorizations.
result BEGIN network captures conditional independence for arbitrary binary and multinomial variables.
Optimal gradient quantization reduces communication costs in distributed deep learning.
problem High communication costs in distributed training of deep neural networks.
method Deduced optimal gradient quantization conditions for binary and multi-level quantization, developed novel schemes for dynamic quantization levels.
result Demonstrated superior performance of proposed quantization schemes on CIFAR and ImageNet datasets.
Chi-Square Test Neural Network: A New Binary Classifier based on Backpropagation Neural Networkcs.LG
A new neural network using chi-square test for binary classification.
problem Improving binary classification accuracy.
method Backpropagation neural network with chi-square test redefined cost and error functions.
result Significantly improved classification accuracy compared to related approaches.
Bayesian learning rule trains binary neural networks effectively.
problem Training binary neural networks is challenging due to discrete optimization.
method Proposes the Bayesian learning rule to estimate Bernoulli weights.
result Obtains state-of-the-art performance and enables uncertainty estimation.
Sparse binary compression reduces communication costs in distributed deep learning.
problem Limited communication bandwidth in distributed deep learning.
method Combines gradient sparsification, binarization, and optimal weight update encoding.
result Reduces upstream communication by more than four orders of magnitude.
Stochastic binary synapses in neural networks lead to robust solutions.
problem Training low-precision neural networks with stochastic weights.
method Gradient descent on a probability distribution of binary synapses.
result Binary solutions are robust and generalize well, while typical solutions are isolated.
We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…
Improved optimization methods for discrete distributions reduce bias in gradient estimation.
problem Estimating gradients for discrete distribution parameters is challenging.
method Analyzed and proposed methods to reduce bias in gradient estimation, including Gumbel-Softmax and piece-wise linear continuous relaxation.
result Reduced bias leads to better performance in variational inference and binary optimization tasks.
Paper develops distributed inference for SVM binary classification.
problem Challenges in modern data size for existing statistical inference.
method Proposes MDL estimator for linear SVM, computationally efficient.
result MDL estimator achieves optimal statistical efficiency.