A new model estimates mixed memberships for categorical data with weighted responses.
problem Limited applicability of existing GoM model to weighted categorical data.
method Proposes Weighted Grade of Membership (WGoM) model, relaxing distribution constraints.
result WGoM can describe any response matrix with finite distinct elements.
Bayesian method predicts runtime metrics for fog manufacturing.
problem Accurate prediction of runtime performance metrics in fog manufacturing.
method Bayesian sparse regression for multivariate mixed responses.
result Enhanced prediction and statistical inferences of runtime metrics.
Mixed-integer optimization improves fairness and transparency in machine learning models.
problem Ensuring fairness and transparency in machine learning models deployed in sensitive areas.
method Embedding responsible ML considerations directly into the learning process using mixed-integer optimization.
result MIO enables the learning of inherently transparent models that can incorporate fairness or other constraints.
The study examines mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression.
problem Investigating convergence properties of data-augmentation samplers for Bayesian probit regression.
method Using recent results on Gibbs samplers for log-concave targets, the study provides non-asymptotic bounds on mixing times.
result Explicit non-asymptotic bounds on mixing times depend on design matrix and prior precision, holding uniformly over responses.
Neural models improve GLMMs for complex data.
problem Nonlinear relationships in grouped data.
method Replaced linear function with neural networks.
result Improved performance on synthetic and real-world data.
New model handles complex output dependence in large datasets.
problem Complex output dependence in large datasets.
method Orthogonal Stochastic Linear Mixing Model (OSLMM) with Markov chain Monte Carlo inference.
result OSLMM reduces prediction error compared to state-of-the-art methods.
Deployment-complete benchmarking assesses if evidence leads to consistent deployment actions.
problem Lack of clear evidence leading to consistent deployment actions.
method Introduces deployment-complete benchmarking to test if benchmark evidence determines deployment actions.
result Benchmark evidence must be complete for a claim to lead to a consistent deployment action.
A new method for Gaussian Processes handles mixed continuous and categorical inputs.
problem Modeling cross-correlations between continuous and categorical data.
method Low-Rank Correlation (LRC) method for Gaussian Processes with flexible rank approximation.
result LRC outperforms existing methods in estimating cross-correlations and predicting response surfaces.
GBMixed boosts mixed models for clustered data, estimating mean and variance flexibly.
problem Flexible estimation of mean and variance components in clustered data.
method Gradient Boosting framework for linear mixed models with likelihood-based gradients.
result GBMixed accurately recovers complex nonlinear fixed effects and covariances.
This paper extends stable blanket theory to models with hidden variables and causal cycles.
problem Identifying stable predictors in models with hidden variables and causal cycles.
method Use acyclic directed mixed graphs (ADMGs) and directed graphs (DGs) with m-separation and σ-separation to characterize and construct intervention-stable predictor sets. result Graphical characterizations of Markov blankets, stable frontiers, and stable blankets in models with hidden variables and cycles.
OMERF extends random forest for hierarchical data and ordinal responses.
problem Analyzing hierarchical data and ordinal responses using tree-based methods.
method Ordinal Mixed-Effects Random Forest (OMERF) that preserves flexibility and hierarchical structure.
result OMERF identifies discriminating student characteristics and estimates school effects.
MMM model clusters mixed-type longitudinal data efficiently.
problem Challenges in clustering multivariate longitudinal mixed-type data.
method MMM model reorganizes data into a three-way structure, using a mixture of matrix-variate normal distributions.
result MMM model handles various data types (continuous, ordinal, binary, nominal, count) and temporal dependence.
Mixed likelihood GPs improve model performance in human-in-the-loop experiments.
problem Lack of auxiliary information in traditional GPs for human responses.
method Propose mixed likelihood variational GPs to leverage auxiliary information.
result Modeling performance improvements across diverse applications.
We prove that the ordinary least-squares (OLS) estimator attains nearly minimax optimal performance for the identification of linear dynamical systems from a single observed trajectory. Our upper bound relies on a generalization of Mendelson's small-ball method to dependent data, eschewing the use of standard mixing-ti…
A new method combines machine learning with mixed-effects models for better repeated measurement analysis.
problem Inference of linear coefficients in partially linear mixed-effects models with complex interactions and high-dimensional variables.
method Double machine learning approach to estimate nonparametrically nonlinear variables, then use standard linear mixed-effects techniques to estimate the linear coefficient.
result The estimated fixed effects coefficient converges at the parametric rate and is semiparametrically efficient.
New optimization algorithm for mixed-variable problems improves efficiency.
problem Optimizing functions with both continuous and categorical variables.
method Combines radial basis function and metric stochastic response surface methods with modifications for categorical variables and parallel processing.
result Numerical experiments show the effectiveness of the proposed modifications.
Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest…
Holistic GLMs add constraints for better model quality.
problem Improving classical linear regression models.
method Sparsity-inducing, sign-coherence, and linear constraints.
result Holistic GLMs reliably solve GLMs for various responses.
Generative Adversarial Networks (GAN) have become one of the most successful frameworks for unsupervised generative modeling. As GANs are difficult to train much research has focused on this. However, very little of this research has directly exploited game-theoretic techniques. We introduce Generative Adversarial Netw…
In this paper we present a nonparametric method for extending functional regression methodology to the situation where more than one functional covariate is used to predict a functional response. Borrowing the idea from Kadri et al. (2010a), the method, which support mixed discrete and continuous explanatory variables,…
Paper explores limits and possibilities of aligning LLMs with human preferences.
problem Aligning LLMs with diverse human preferences to ensure fairness and informed outcomes.
method Analysis of probabilistic representation of human preferences and preservation of diverse preferences.
result LLMs can't fully align with human preferences using reward-based approaches due to Condorcet cycles, but mixed strategies are statistically possible.
Study best-response learning dynamics in zero-sum polymatrix games under full and minimal information settings.
problem Learning dynamics in zero-sum polymatrix games under different information settings.
method Two-timescale learning dynamics combining smoothed best-response updates and TD-learning for estimating local payoff functions.
result Polynomial-time finite-sample guarantees for convergence to an ε-Nash equilibrium in the minimal information case.
New Bayesian method for sparse multidimensional item response theory.
problem Sparse interpretable explanations for questionnaire data.
method Bayesian EM algorithm for sparse factor loadings.
result Reliable recovery of factor dimensionality and latent structure.
MOMENT selects and estimates mixed-effects models using moment identities.
problem Selecting and estimating random-effects covariance matrix and fixed-effects coefficients in multiresponse linear mixed-effects models.
method MOMENT is a stage-wise moment-based framework that reduces the random-effects selection problem to a smooth constrained convex optimization problem.
result MOMENT performs competitively and can outperform separate univariate analyses for correlated responses.
DeepCausalMMM models marketing impacts using deep learning and causal inference.
problem Traditional MMM approaches struggle with non-linear dynamics and temporal patterns.
method Combines deep learning, causal inference, and marketing science. Uses GRUs for temporal patterns and DAG structure for channel dependencies.
result Captures non-linear dynamics and temporal patterns in marketing impacts.
Study on recovering sparse linear classifiers from mixed binary responses.
problem Learning a mixture of sparse linear classifiers from binary responses.
method Query-based approach to identify all sparse vectors from a set.
result Upper bounds on the number of queries required for recovery.
This paper uses MIO to select features for kernel SVM classification.
problem Feature selection for kernel SVM classification.
method Mixed-integer optimization (MIO) for feature subset selection.
result The MIO approach can often outperform linear-SVM-based methods in prediction performance.
Unified multitask learning framework for mixed-type outcomes.
problem Difficulty in formulating a unified objective for tasks with different outcomes.
method Multitask transformation framework with shared sparsity, using deep neural networks and rank-based optimization.
result Improved prediction and variable selection across continuous, binary, and mixed outcomes.
Enhances topology optimization with multiclass microstructures using latent variable Gaussian process.
problem Lack of an inherent ordering or distance measure between different classes of microstructures.
method Extended latent-variable Gaussian process (LVGP) models to multi-response LVGP (MR-LVGP) models for metamaterials.
result Improved performance through consistent load-transfer paths for micro- and macro-structures.
Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal r…
Proposes integrating random effects into deep neural networks for better predictive performance.
problem Correlated data in real-life applications are not handled well by traditional deep neural networks.
method Uses mixed models with random effects to handle correlations in deep neural networks, minimizing Gaussian negative log-likelihood with SGD.
result Improves predictive performance over natural competitors in various correlation scenarios.
Develops conformal Bayes for two-sided censored Gaussian regression under label shift.
problem Prediction under label shift with censored responses.
method Combines posterior predictive tilting with weighted conformal calibration.
result Restores marginal coverage with smaller prediction sets.
GLMM trees identify subgroups with different growth patterns in longitudinal data.
problem Identifying subgroups with distinct growth trajectories in longitudinal studies.
method Extended GLMM trees for longitudinal data.
result Extended GLMM trees outperform other methods in accuracy and speed.
Optimum in Convex Hulls (OCH) generalizes clinical trial results to broader populations.
problem Clinical trials exclude confounding but limit recruitment; observational data are more inclusive but suffer from confounding.
method OCH uses convex hulls of conditional expectations or densities to approximate the true treatment effect from both observational and trial data.
result OCH estimates the treatment effect with state-of-the-art accuracy in terms of both expectations and densities.
Automated method finds meaningful directions in neural network activations.
problem Mixed selectivity in neurons makes interpretation challenging.
method Automated quantification of interpretability and discovery of meaningful directions.
result Meaningful directions in neural network activations are more interpretable than individual neurons.
MixCIT tests conditional independence for mixed data types efficiently and reliably.
problem Testing conditional independence for mixed data types, especially when at least one is continuous.
method Graph-based test statistic comparing kernel similarities, debiased local-polynomial approach for continuous variables.
result Unified, efficient, and statistically guaranteed solution across heterogeneous data types.
We analyze mixing times of three DA algorithms for regression models.
problem Analyzing mixing times of data augmentation algorithms for regression models.
method Modified conductance-based method to study mixing times of Gibbs samplers.
result Prove non-asymptotic polynomial upper bounds on mixing times for DA algorithms.
Multi-task learning models using Gaussian processes (GP) have been developed and successfully applied in various applications. The main difficulty with this approach is the computational cost of inference using the union of examples from all tasks. Therefore sparse solutions, that avoid using the entire data directly a…
Propose an XMSE-aware mixed estimator for EB that interpolates between ML and EB shrinkage.
problem Kernel-based EB estimation may be worse than ML when the kernel is poorly aligned with the true parameter.
method An XMSE-aware mixed estimator that interpolates between ML and EB shrinkage.
result Fixed-weight XMSE is a scalar quadratic, yielding a closed-form oracle mixing weight that is no worse than both ML and the base EB estimator at the XMSE scale.
Study finds no significant short-term impact on liquidity supply after protocol fees were reduced.
problem Liquidity provider welfare is affected by protocol fees, but the impact on liquidity supply is unclear.
method Used a matched-overlap event-study difference-in-differences design to estimate the liquidity-supply response to take-rate cuts.
result No significant short-term impact on active liquidity or local depth; no change in LP participation or composition.
DSPM models control noise volatility, improving financial data analysis.
problem Financial returns exhibit volatility clustering, challenging traditional models.
method DSPM uses a tempered-stable subordinator to control noise volatility, preserving kurtosis and autocorrelation.
result DSPM models accurately capture volatility clustering and noise mechanisms.
Hybrid Policy Optimization tackles reinforcement learning in hybrid spaces, improving performance over PPO.
problem Credit assignment issues and biased gradients in hybrid discrete-continuous action spaces.
method Mixed gradient estimator combining pathwise and score-function gradients, reformulating problems in hybrid form.
result HPO substantially outperforms PPO on inventory control and switched systems, with performance gaps increasing with continuous action dimension.
New algorithm for solving minimax problems over distributions converges to Nash equilibrium.
problem Solving minimax problems over probability distributions.
method Symmetric Mean-field Langevin Dynamics (MFL-AG and MFL-ABR) with weighted averaging and best response dynamics.
result Converges to mixed Nash equilibrium with average-iterate and last-iterate convergence.
A new model integrates covariates with grade of membership analysis for better latent structure recovery.
problem Improving latent structure recovery in multivariate categorical data analysis.
method Covariate-assisted grade of membership model exploiting shared low-rank simplex geometry.
result Auxiliary covariates can provably improve latent structure recovery, leading to faster convergence rates.
New method for sparse kernel selection improves prediction accuracy.
problem Sparse Multiple Kernel Learning for binary classification.
method Alternating best response algorithm with semidefinite relaxations.
result Method outperforms state-of-the-art MKL approaches in prediction accuracy.
In this paper the problem of optimal derivative design, profit maximization and risk minimization under adverse selection when multiple agencies compete for the business of a continuum of heterogenous agents is studied. The presence of ties in the agents' best-response correspondences yields discontinuous payoff functi…
This paper introduces a novel recalibration method for multivariate forecasts.
problem Multivariate calibration for potentially misspecified models.
method Local mappings between marginal probability integral transform values and observed space, using K-nearest neighbors or normalizing flows.
result Demonstrated effectiveness on currency exchange rate and childhood malnutrition data.
sEM uses optimal transport to improve EM algorithm for better convergence and avoiding local optima.
problem Improving the EM algorithm for better convergence and avoiding local optima.
method sEM uses entropic optimal transport to compute responsibilities in the expectation step, leading to better global convergence guarantees and avoiding local optima.
result sEM learns cell labels significantly better than other approaches, improving convergence and avoiding local optima.