Paper extends FOFC algorithm to work with mixed data types.
problem Designing causal discovery algorithms for mixed data types.
method Proves tetrad constraint can be entailed for mixed data types and applies FOFC algorithm.
result FOFC algorithm can work on mixed data types.
Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of th…
Study controls error rates of binary classifiers using hypothesis testing.
problem Traditional binary classifiers have uncontrolled error rates.
method Combines binary classification with statistical hypothesis testing.
result Trained classifiers can be made to meet target error rate thresholds.
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.
Sharp bounds found on expert error in binary advice aggregation.
problem Aggregating binary advice from conditionally independent experts.
method Sharp upper and lower bounds on optimal error probability in asymmetric case.
result Sharp bounds recover and sharpen known results in symmetric case.
We compute the spectral action of SU(2)/Γ with the trivial spin structure and the round metric and find it in each case to be equal to ∣Γ∣1(Λ3f^(2)(0)−1/4Λf^(0))+O(Λ−∞). We do this by explicitly computing the spectrum of the Dirac operator for SU(2)/Γ equipped with the trivial …
Consistent selection of predictors in high-dimensional binary models with misspecified parameters.
problem Selection of predictors in high-dimensional binary models with misspecified parameters.
method Two-step selection procedure: screening and ordering predictors by Lasso, followed by model selection.
result Consistent selection of the support of the minimizer of the associated risk.
BatchNorm helps BNNs avoid exploding gradients during training.
problem Difficulty in training Binary Neural Networks (BNNs).
method Theoretical study and numerical experiments on the role of BatchNorm in BNNs.
result BatchNorm is crucial for avoiding exploding gradients in BNNs.
Extends co-clustering to mixed numerical and binary data.
problem Co-clustering of mixed data types (numerical and binary).
method Latent block models for mixed data types.
result Effectiveness of the proposed approach on simulated data.
Archetypal analysis helps understand binary data sets.
problem Explaining binary questionnaire data.
method Using archetypal analysis for binary observations.
result The approach contributes to understanding binary data sets.
Paper uses RL to optimize bit-flipping decoding for binary codes.
problem Improving bit-flipping decoding for binary linear codes.
method Mapped iterative decoding algorithms to MDPs for reinforcement learning.
result Learned BF decoders offer performance-complexity trade-offs and near-optimal performance.
This paper improves binary classification methods beyond accuracy, especially in imbalanced datasets.
problem Binary classification performance metrics often fail to reflect real-world consequences, especially in imbalanced datasets.
method Derives a generalized Bayes-optimal classifier from accuracy to any performance metric, removing assumptions and providing finite-sample statistical guarantees.
result Optimal classification performance depends on class imbalance properties, providing new insights and guarantees.
We generalize Conway's approach to integral binary quadratic forms on Q to study integral binary hermitian forms on quadratic imaginary extensions of Q. In Conway's case, an indefinite form that doesn't represent 0 determines a line ("river") in the spine T associated with SL(2,Z) in the hyperbolic plane. In our genera…
New method learns signals from binary measurements, surpassing existing techniques.
problem Learning signals from noisy, incomplete, and quantized binary measurements.
method Self-supervised learning approach (SSBM) for binary data.
result SSBM outperforms supervised learning and sparse reconstruction methods.
Binary, or one-bit, representations of data arise naturally in many applications, and are appealing in both hardware implementations and algorithm design. In this work, we study the problem of data classification from binary data and propose a framework with low computation and resource costs. We illustrate the utility…
Paper explores learning patterns in binary sequences, finding no method consistently outperforms others.
problem Learning patterns in infinite binary sequences.
method Various learning methods are compared, finding no method consistently outperforms others.
result No learning method consistently outperforms others in predicting binary sequences.
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.
Optimizes binary regression models with gradient ascent-descent methods.
problem Regression problems with binary weights in quantized learning and digital communication.
method Maximin optimization using gradient ascent-descent methods.
result The approach is optimal in linear regression with low noise and robust regression with few outliers.
Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning …
In this paper there is proposed a generalized version of the SVM for binary classification problems in the case of using an arbitrary transformation x -> y. An approach similar to the classic SVM method is used. The problem is widely explained. Various formulations of primal and dual problems are proposed. For one of t…
Privacy-preserving binary classification using locally differential private data.
problem Classifying data while protecting individual privacy.
method Locally differential private mechanism followed by a universally consistent classifier.
result Minimax rates of convergence are slower when using private data.
We formulate learning of a binary autoencoder as a biconvex optimization problem which learns from the pairwise correlations between encoded and decoded bits. Among all possible algorithms that use this information, ours finds the autoencoder that reconstructs its inputs with worst-case optimal loss. The optimal decode…
The paper analyzes network models with binary values and sub-Gamma noise, deriving asymptotic properties.
problem Analyzing network models with binary values and sub-Gamma noise.
method Derives asymptotic properties of network models with binary values and sub-Gamma noise.
result Established asymptotic consistency and normality of parameter estimators in network models.
Efficiently private regression for unbounded data.
problem Privacy constraints in regression settings with unbounded covariates.
method Differential privacy techniques on mean and covariance estimation extended to sub-gaussian regime.
result Unbiased estimate of true regression vector learned up to a scaling factor.
Binary feedback outperforms ordinal comparisons in ranking recovery.
problem Challenges the conventional wisdom that ordinal comparisons offer richer information.
method Proposes a parametric framework for modeling ordinal paired comparisons, binarizing ordinal data, and proving faster convergence rates for binary comparisons.
result Binarizing ordinal data significantly improves ranking recovery accuracy and exhibits a substantial performance gap.
Resolving a conjecture of Abbe, Bandeira and Hall, the authors have recently shown that the semidefinite programming (SDP) relaxation of the maximum likelihood estimator achieves the sharp threshold for exactly recovering the community structure under the binary stochastic block model of two equal-sized clusters. The s…
There has been much recent interest in application of the pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the probabilistic outputs of automatic pattern recognition and machine learning algorithms. Special cost functions, known as proper scoring rules form natural objective functions to judge the…
The paper analyzes binary option markets with exogenous information and price sensitivity.
problem Analyzing binary option markets with exogenous information and price sensitivity.
method Derive and analyze a continuous model of binary option markets with exogenous information, using Filippov surfaces and general assumptions on purchasing rules.
result Price always converges when exogenous information is constant, and price sensitivity affects price lag vs. information.
Paper proposes a method to estimate true positive proportion without knowing it.
problem Bias in binary classifier performance due to different positive item proportions.
method Maximum likelihood estimator for true proportion of positives.
result Method accurately estimates true positive proportion in data sets.
Proposes a new binary classification model inspired by fluid phase separation.
problem Binary classification challenges.
method Discretization of nonlinear reaction-diffusion equation coupled with ODE, inspired by fluid dynamics.
result PSBC model achieves comparable performance to traditional methods on MNIST.
We analyze MDL for binary classification, quantifying overfitting and underfitting.
problem Understanding the trade-off between underfitting and overfitting in MDL for binary classification.
method Complete characterization of the regularization curve for MDL, extending previous work to all λ. result Precise quantitative description of the worst case limiting error as a function of λ and noise level. The choice of normalization affects the coefficients in regularized regression models.
problem The impact of normalization on the coefficients of regularized regression models.
method Investigated lasso, ridge, and elastic net regression with different normalization methods for binary and mixed features.
result Normalization affects the coefficients of regularized regression models, and specific scaling methods can mitigate this effect.
New clustering methods for binary data using combinatorial optimization.
problem Clustering binary data efficiently and effectively.
method Five new combinatorial optimization heuristics (SA, TA, TS, GA, ACO) applied to binary data.
result Simulated annealing performs exceptionally well compared to classical methods.
In this paper, we study the problem of compressed sensing using binary measurement matrices and ℓ1-norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices. We establish sufficient conditi…
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.
In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based o…
Binary operations on algebras of observables are studied in the quantum as well as in the classical case. It is shown that certain natural compatibility conditions with the associative product imply the properties which usually are additionally required. In particular, it is proved that locality of a Loday bracket on s…
Regularized linear regression improves binary classification performance, especially with ridge and ℓ1 regularization.
problem Improving binary classification accuracy with noisy labels.
method Systematic study of regularization strengths on linear classifiers trained on noisy binary classification data.
result Ridge regression consistently improves classification error, while ℓ1 regularization can induce sparsity and ℓ∞ regularization can concentrate weights to two values. 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.
A novel BMC model with nonconvex regularizers and accelerated proximal algorithm for binary matrix completion.
problem Recovering a binary matrix from partial observed positive elements.
method Proposes a novel BMC model with nonconvex regularizers and accelerates proximal algorithm for solving the nonconvex optimization problem.
result The proposed model and algorithm outperform other methods in both synthetic and real-world data sets.
dtControl uses decision trees to represent controllers efficiently and explainably.
problem Representing controllers concisely and explainably.
method dtControl uses decision tree learning algorithms to represent controllers. Novel techniques for determinizing controllers are introduced.
result Novel techniques for determinizing controllers during decision tree construction are extremely efficient, yielding small decision trees.
A new weighted MCC measure improves classifier performance evaluation.
problem Lack of measures sensitive to observation weights in multiclass classification.
method Proposes weighted versions of Pearson-Matthews Correlation Coefficient (MCC) for binary and multiclass classification.
result Weighted MCC values are higher for classifiers that perform better on highly weighted observations.
Develops RF-GLS for binary geospatial data.
problem Challenges in extending RF to binary geospatial data.
method Proposes RF-GLS for binary data, embedding it in generalized mixed effects models.
result Establishes consistency of RF-GP for mean function and covariate effect estimation.
In this article, we consider a 2 factors-model for pricing defaultable bond with discrete default intensity and barrier where the 2 factors are stochastic risk free short rate process and firm value process. We assume that the default event occurs in an expected manner when the firm value reaches a given default barrie…
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.
A new scoring function improves ordinal regression by combining binary predictions.
problem Improving ordinal regression prediction accuracy.
method Introduces a cumulative sum scoring function to combine binary predictions.
result Simplified formulation and two online learning algorithms converge under rank separability condition.
We present a second iteration of a machine learning approach to static code analysis and fingerprinting for weaknesses related to security, software engineering, and others using the open-source MARF framework and the MARFCAT application based on it for the NIST's SATE IV static analysis tool exposition workshop's data…
Study minimax rates for binary classifier estimation with margin conditions.
problem Estimating binary classifiers with geometric margin conditions.
method Derive lower bounds for worst-case learning rates over various function classes.
result Identify optimal rates close to O(n−1) for different function classes.