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.
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.
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.
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.
New copula-based models for binary outcomes capture complex interactions.
problem Complex interaction effects in binary outcome models.
method Additive models with copula-based components, no discretization required.
result Better or comparable predictive performance compared to other methods.
Interactive tool helps choose and understand classification metrics.
problem Common metrics for binary classification have limitations.
method Graphical application to visualize and explore evaluation metrics.
result Promotes careful attention to interpretation of metrics.
Efficient algorithms learn non-binary concepts from random counter-examples.
problem Learning non-binary concepts from random counter-examples efficiently.
method Two simple LRC algorithms: deterministic and randomized.
result Both algorithms achieve optimal average learning time of O(log|H|).
Proposes a credit scoring system for Aave accounts.
problem Scoring the creditworthiness of DeFi accounts.
method Tree-based binary classifier for predicting delinquency.
result Validated the credit scoring system for Aave accounts.
BELIEF framework interprets GLMs using binary linear models.
problem Understanding and interpreting generalized linear models (GLMs) with binary outcomes.
method Developed a framework called binary expansion linear effect (BELIEF) to interpret GLMs through transparent linear models.
result BELIEF framework reveals perfect predictors in complete separation scenarios.
We investigate a class of binary choice models with social interactions. We propose a unifying perspective that integrates economic models using a utility function and psychological models using an impact function. A general approach for analyzing the equilibrium structure of these models within mean-field approximatio…
Method finds significant attribute interactions in medical data.
problem Identify significant attribute interactions in medical datasets.
method Statistical significance testing and ASTRID method.
result Identifies interactions useful for feature selection and data anonymization.
Generative model learns to compose images of objects from different distributions.
problem Capturing complex interactions between objects in scenes.
method Composition-by-Decomposition (CoDe) network.
result Model generates realistic composite images capturing interactions between input objects.
Enhances preference learning by incorporating response times into binary choices.
problem Limited information from binary choices about preference strength.
method Combines choices and response times using the EZ diffusion model.
result Response times improve utility estimation for strong preferences.
Proposes a method to learn sparse and low-rank interactions in Ising models with latent variables.
problem Learning sparse interactions in Ising models with latent variables.
method Sparse + low-rank decomposition of Ising model parameters using convex regularized likelihood problem.
result Consistency properties in high-dimensional settings with growing number of variables and samples.
New method accurately estimates Ising model parameters and structure.
problem Reconstructing structure and parameters of Ising models from binary samples.
method Interaction Screening method using local optimization problems.
result Proves perfect graph structure recovery with optimal number of samples.
New algorithm finds significant interactions between continuous features.
problem Finding statistically significant interactions between continuous features is challenging due to combinatorial explosion.
method Proposes an algorithm that derives a lower bound on p-values for each interaction, pruning non-significant interactions.
result Efficiently detects all significant interactions in synthetic and real-world datasets.
We investigate the effect of tax evasion on the income distribution and the inequality index of a society through a kinetic model described by a set of nonlinear ordinary differential equations. The model allows to compute the global outcome of binary and multiple microscopic interactions between individuals. When evas…
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. Interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them and receive responses. Pool-based algorithms can select elements at any order, while stream-based algo…
Modeling and estimating dynamic graphs from binary pattern sequences.
problem Extracting dominant correlation structures from time-dependent binary patterns.
method State-space model of an Ising-type network composed of multiple undirected graphs, sequential Bayes algorithm.
result The method outperforms traditional methods in uncovering overlapping graphs and estimating dynamics of weights.
A model studies deep neural networks with binary synapses under connection removal.
problem Understanding the mechanism of deep learning from a theoretical perspective.
method Random active path model with diluted binary synapses under removal perturbation.
result A critical value of perturbation separates spin glass and paramagnetic phases, with the latter having poor generalization performance.
WHInter solves high-dimensional sparse interaction models efficiently.
problem Learning sparse models with two-way interactions in high-dimensional data.
method Working set algorithm for l1-regularised problems with quadratic interactions.
result Two orders of magnitude faster than state-of-the-art methods.
To date, testing interactions in high dimensions has been a challenging task. Existing methods often have issues with sensitivity to modeling assumptions and heavily asymptotic nominal p-values. To help alleviate these issues, we propose a permutation-based method for testing marginal interactions with a binary respons…
Finding statistically significant interactions between binary variables is computationally and statistically challenging in high-dimensional settings, due to the combinatorial explosion in the number of hypotheses. Terada et al. recently showed how to elegantly address this multiple testing problem by excluding non-tes…
In order to use the advanced inference techniques available for Ising models, we transform complex data (real vectors) into binary strings, by local averaging and thresholding. This transformation introduces parameters, which must be varied to characterize the behaviour of the system. The approach is illustrated on fin…
Learning compact and interpretable representations is a very natural task, which has not been solved satisfactorily even for simple binary datasets. In this paper, we review various ways of composing experts for binary data and argue that competitive forms of interaction are best suited to learn low-dimensional represe…
A new binary classification algorithm using hypergraphs reduces preprocessing needs.
problem Binary classification in unstructured spaces with missing data.
method Hypergraph representation, seminorm learning, partitioning elements based on training set interactions.
result Empirical validation shows high performance on various datasets.
Unified theory for training neural networks with binary synapses.
problem Discrete nature of synapses and complex interactions in neural networks.
method Variational mean-field theory decomposing learning into maximization and expectation steps.
result Unified framework for unsupervised learning in neural networks.
A new test method improves goodness-of-fit tests for copulas.
problem Developing robust tests for copula goodness-of-fit.
method Binary Expansion Approximation of UniformiTY (BEAUTY) and Binary Expansion Adaptive Symmetry Test (BEAST).
result The BEAST method improves empirical power against various alternatives.
We introduce BAR processes for modeling binary interactions and show they mix rapidly.
problem Modeling binary interactions in various graphical structures.
method Introduce Bernoulli Autoregressive Processes (BAR) with autoregressive dynamics and efficient structure learning.
result BAR processes mix rapidly with a mixing time of O(logp). Finding interactions between variables in large and high-dimensional datasets is often a serious computational challenge. Most approaches build up interaction sets incrementally, adding variables in a greedy fashion. The drawback is that potentially informative high-order interactions may be overlooked. Here, we propos…
Paper tackles interactive learning with noisy labels and comparisons, showing how comparison oracles can improve learning efficiency.
problem Interactive learning with noisy labels and comparisons.
method Characterizes comparison oracle as threshold function learning problem, presents algorithm with query complexity analysis.
result Lower bounds show almost optimal label and total query complexity.
Model detects patterns in noisy binary data, explaining neuron activity in terms of cell assemblies.
problem Detecting structure in noisy or approximate repeats of patterns in sparse binary data.
method Probabilistic binary latent variable model based on Noisy-OR model, inferring sparse activity in latent variables.
result Model successfully extracts and explains latent structure in spiking neural data.
Develops a fast BMF approach for binary matrices.
problem Finding patterns in binary matrices for various applications.
method MEBF (Median Expansion for Boolean Factorization) using geometric segmentation and heuristic submatrix identification.
result Superior performance in reconstruction error and computational efficiency compared to existing methods.
Robo-advisors estimate clients' risk aversion using interactive questionnaires.
problem Estimating risk aversion of non-expert clients using adaptive questionnaires.
method Model risk aversion with cost functions and spectral risk measures. Use inverse reinforcement learning to design questions maximizing distinguishing power.
result Designing questions by maximizing distinguishing power achieves satisfactory accuracy in learning risk aversion with fewer than 50 questions.
Genetic algorithms are a well-known method for tackling the problem of variable selection. As they are non-parametric and can use a large variety of fitness functions, they are well-suited as a variable selection wrapper that can be applied to many different models. In almost all cases, the chromosome formulation used …
BaGGLS models biological interactions using Bayesian shrinkage for interpretability.
problem Interpreting complex interactions in high-dimensional biological data.
method Bayesian group global-local shrinkage prior with variational approximation.
result BaGGLS outperforms other methods in interaction detection and scalability.
Collaborative Trees model analyzes feature interactions and additive effects.
problem Analyzing complex statistical associations between features and response variables.
method Proposes a novel tree model and its bagging version to decompose mean decrease in impurity and visualize feature contributions.
result Demonstrates the superior capability of the tree model in estimating additive effects and interaction effects.
New method provides fine-grained feedback on interactive student programs.
problem Time-consuming manual grading of interactive student programs.
method Meta-exploration approach using reinforcement learning.
result 94.3% accuracy in providing fine-grained feedback.
Proposes a new model to capture interaction modalities in networks.
problem Limited information in binary network representations of interactions.
method Topic Blockmodel using a count-based topic model.
result Better extraction of information from interaction networks.
New method bypasses assumptions for unbiased estimation of complex system interactions.
problem Inferring pair-wise and higher-order interactions from observational data.
method Cross-disciplinary approach using Targeted Learning for unbiased estimation.
result Universal estimator of all-order symmetric interactions without parametric assumptions.
This study uncovers how neural architectures and weights interact in classification tasks.
problem Understanding the role of neural architecture and weights in classification performance.
method Developed a novel method to find optimal task-specific architectures as binary networks with {0, 1}-valued weights, using approximate gradient descent.
result Well-trained architectures may not require fine-tuning of weights, highlighting the importance of structure over weights.
HYVINT generates hypergraphs with intensity-driven incidence formation and variational learning.
problem Challenges in generating hypergraphs with mechanistic interpretation and limited latent space.
method HYVINT uses intensity-driven incidence formation and a lower-bound variational estimator for latent representations.
result HYVINT achieves strong fidelity and novelty on synthetic and real-world hypergraphs.
XTNet estimates complex cross-treatment effects in multi-category, multi-valued settings.
problem Challenges in estimating causal effects for multi-category, multi-valued treatments.
method Dynamic Neural Masking for capturing treatment interactions without restrictive assumptions.
result XTNet consistently outperforms state-of-the-art baselines in multi-category, multi-valued treatment effect estimation.
An active learner is given a class of models, a large set of unlabeled examples, and the ability to interactively query labels of a subset of these examples; the goal of the learner is to learn a model in the class that fits the data well. Previous theoretical work has rigorously characterized label complexity of activ…
Ridge regression shows different behaviors in binary classification with noisy labels.
problem Binary classification with noisy labels and anisotropic cluster distributions.
method Investigation of ridge regression behavior in overparameterized settings with label noise.
result Ridge regression exhibits qualitatively different behavior based on the scale of cluster mean vectors and covariance matrices.
PADME predicts drug-target interaction strengths using deep learning.
problem Challenges in drug-target interaction prediction, especially for cold-target problems.
method PADME uses deep neural networks to predict real-valued interaction strengths between compounds and proteins, handling cold-target problems.
result PADME consistently outperforms baseline methods on multiple datasets, including the ToxCast dataset.
A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation…
Estimates binary labels from dependent data using Markov Random Fields.
problem Statistical estimation from dependent data across spatial, temporal, and social domains.
method Modeling dependencies as Markov Random Fields and providing efficient estimation algorithms.
result Statistically efficient estimation rates for Ising models from a single sample.