arXiv research

Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.

Paper corrects GIRP algorithm to ensure isotonic models.

We consider the estimation of binary election outcomes as martingales and propose an arbitrage pricing when one continuously updates estimates. We argue that the estimator needs to be priced as a binary option as the arbitrage valuation minimizes the conventionally used Brier score for tracking the accuracy of probabil…

Probabilistic learning for binary classification with categorical variables.

New approach improves classification guarantees by focusing on direction rather than regression risk.

Paper proposes a QUBO formulation that reduces binary variables in Bayesian network learning.

We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via…

To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on the original input matrix, and then performs regularized linear/logistic regressio…

Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved…

Estimates causal effects using machine learning for binary treatment and mediator.

Paper extends FOFC algorithm to work with mixed data types.

ARMS improves gradient estimation for binary variables using antithetic samples.

Proposes MELODIC family for simultaneous binary logistic regression.

Model detects patterns in noisy binary data, explaining neuron activity in terms of cell assemblies.

Given a classical channel---a stochastic map from inputs to outputs---the input can often be transformed to an intermediate variable that is informationally smaller than the input. The new channel accurately simulates the original but at a smaller transmission rate. Here, we examine this procedure when the intermediate…

RBMs model binary interactions with hidden node activation effects.

The mesoscopic organization of complex systems, from financial markets to the brain, is an intermediate between the microscopic dynamics of individual units (stocks or neurons, in the mentioned cases), and the macroscopic dynamics of the system as a whole. The organization is determined by "communities" of units whose …

A new method for binary ICA using non-stationary sources.

The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables. We generalize the algorithm to piecewise differentiable distributions and apply it to generic binary distributions using a piecewise differentiable augmentation. We illust…

We characterize and study variable importance (VIMP) and pairwise variable associations in binary regression trees. A key component involves the node mean squared error for a quantity we refer to as a maximal subtree. The theory naturally extends from single trees to ensembles of trees and applies to methods like rando…

DisARM improves gradient estimation for binary latent variables.

BEGIN network models binary data without parametric assumptions.

Proposes a gradient-based variable selection method for binary classification in RKHS.

According to [8] if the stationary Schroedinger equation on n-dim. Riemann space admits R-separation of variables (i.e. separation of variables with a factor R), then the underlying metric is necessarily isothermic. An important sub-class of isothermic metrics are the so called binary metrics. In this paper we study co…

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…

Bayesian method models binary response and covariates for two groups, estimating causal relationships.

We summarize our recent findings, where we proposed a framework for learning a Kolmogorov model, for a collection of binary random variables. More specifically, we derive conditions that link outcomes of specific random variables, and extract valuable relations from the data. We also propose an algorithm for computing …

Study shows how adjusting for a binary proxy can bound causal effects.

Paper analyzes impact of PRM on binary random variables and distribution shifts.

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

Efficiently identifies important variables in binary outcomes using variational Bayes.

New method simplifies Bayesian analysis for categorical data.

We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furtherm…

BELIEF framework interprets GLMs using binary linear models.

The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate representations for quantization with minimal accuracy loss while significantly reducing the memory footprint and compute intensity of the DN…

This paper applies deep learning to ordinal regression, modeling it as a binary search.

Algorithm BGLM-OFU minimizes regret in combinatorial causal bandits with binary models.

Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal assessment and category…

We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribution function per variable. The target application is a large scale partially observed system, like e.g. a traffic network, where a small pr…

We study the geometry of multidimensional scalar $2^{nd}$ order PDEs (i.e. PDEs with $n$ independent variables) with one unknown function, viewed as hypersurfaces $\mathcal{E}$ in the Lagrangian Grassmann bundle $M^{(1)}$ over a $(2n+1)$-dimensional contact manifold $(M,\mathcal{C})$. We develop the theory of character…

New algorithms improve binary neural network configurations.

New insights into identifying mixtures of product distributions using Hadamard extensions.

We propose a mixture of latent trait models with common slope parameters (MCLT) for model-based clustering of high-dimensional binary data, a data type for which few established methods exist. Recent work on clustering of binary data, based on a $d$-dimensional Gaussian latent variable, is extended by incorporating com…

Enhances quantum machine learning models using Fock states.

fastHDMI improves neuroimaging variable selection in high-dimensional data.

A new method reduces variance in training discrete latent variable models.

The muti-layer information bottleneck (IB) problem, where information is propagated (or successively refined) from layer to layer, is considered. Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variab…

MIP-GNN uses graph neural networks to predict variable biases for MIP solvers.