Unsupervised ML method reveals hidden features in reactive-diffusion simulations.
problem Automating interpretation of large model outputs in reactive-diffusion simulations.
method NTFk using Non-negative Tensor Factorization (NTF) coupled with k-means clustering.
result Identifies additive features characterizing mixing behavior.
metabeta uses neural networks to speed up Bayesian mixed-effects regression.
problem Bayesian mixed-effects regression is computationally expensive.
method metabeta is a neural network model that pre-trains to estimate posterior distributions.
result metabeta achieves comparable performance to MCMC at a fraction of the time.
Machine learning models accurately predict the state and dynamics of reactive mixing.
problem Accurate prediction of reactive mixing for Earth and environmental science applications.
method Built a high-fidelity numerical model to simulate reactive mixing scenarios. Used 20 different machine learning emulators to classify mixing state and predict three QoIs.
result Ensemble methods and MLP models accurately predict the state of reactive mixing and QoIs, significantly faster than high-fidelity simulations.
Proposes a convex model for mixed logit to handle individual heterogeneity.
problem Non-convex optimization in mixed logit models for individual heterogeneity.
method Sparse and low-rank decomposition for convex formulation.
result Convex formulation avoids simulation-based approximation and unstable model interpretation.
We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like sof…
Paper tackles AI driving competition challenges with mixed simulation and real-world data.
problem AI algorithms perform poorly in real-world environments compared to simulated ones and vice versa.
method Employed imitation learning on a mixed dataset to train algorithms equally well in all environments.
result Trained algorithms performed well in both simulated and real-world environments.
New method for mixed memberships using symmetrized Laplacian inverse matrix.
problem Mixed memberships in community detection.
method Spectral clustering on symmetrized Laplacian inverse matrix.
result Mixed-SLIM methods outperform state-of-the-art methods.
This work improves mixing rates for Bayesian CART, a key component of BART.
problem Understanding and improving mixing rates for Bayesian inference with MCMC.
method Derived upper bounds on mixing times, provided sufficient conditions for polynomial mixing, and proposed Twiggy Bayesian CART.
result Twiggy Bayesian CART achieves polynomial mixing without assuming signal connectivity.
Bayesian optimization tackles mixed discrete-continuous problems with Gaussian processes.
problem Optimizing problems with both discrete and continuous variables using costly simulations.
method Relaxing discrete variables into continuous latent variables, using Bayesian optimization, and incorporating compatibility constraints with Lagrangians.
result Comparative analysis of different mixed Bayesian optimization approaches.
Global optimization algorithm finds sparse mixed membership matrix factorization's global optimum.
problem Sparse mixed membership matrix factorization problems with local optima.
method Derives a global optimization algorithm for sparse mixed membership matrix factorization.
result Guaranteed ε-global optimum across random initializations and multiple modes. Lower bound on BART's mixing time increases with data points.
problem Slow mixing time in BART's MCMC chains.
method Simplified BART with a single tree and reduced MCMC moves.
result Mixing time grows exponentially with data points.
Polynomial mixing times for simulated tempering in mixture sampling problems.
problem Sampling from mixtures of log-concave distributions with location shifts.
method Conductance decomposition applied to an auxiliary Markov chain on an augmented space.
result First polynomial-time guarantee for simulated tempering with MALA.
New method improves sampling from multi-modal distributions using Langevin diffusion and simulated tempering.
problem Sampling from multi-modal distributions is challenging and slow.
method Combining Langevin diffusion with simulated tempering.
result The new method mixes more rapidly and samples from distributions close to mixtures of gaussians.
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.
This study compares transfer learning and multi-agent learning for AI-driven traffic agents.
problem Improving traffic flow in mixed-intelligence highway scenarios.
method Online MIT DeepTraffic simulation, deep reinforcement learning, elitist evolutionary algorithm, hyperparameter search, transfer learning, multi-agent learning.
result Transfer learning and multi-agent learning yield different average speeds for AI-driven traffic agents.
FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.
problem Detecting anomalies in high-dimensional mixed data.
method kurtosis-weighted Factor Analysis of Mixed Data (FAMDAD).
result Anomalies are highly separable in the first and last few dimensions of the FAMDAD embedding.
New model clusters mixed-type data with missing values, improving air quality analysis.
problem Clustering mixed-type data with missing values and regime persistence.
method Statistical jump model incorporating regime persistence and handling missing data.
result Superior performance in inferring persistent air quality regimes compared to traditional methods.
Paper uses deep learning to improve thermal-hydraulic simulations.
problem Limited credibility of thermal-hydraulic codes in real plant conditions.
method Feature Similarity Measurement (FSM) and deep learning.
result Deep learning constructs relationships between local physical features and simulation errors.
Proposes a method to allocate time budgets in mixed criticality systems.
problem Managing execution time variability in mixed criticality systems.
method Quantifies execution time variability using statistical dispersion parameters and proposes a heuristic to allocate time budgets.
result The proposed heuristic reduces the probability of exceeding allocated budgets.
A new spectral clustering algorithm that avoids eigenvector computation.
problem Computational complexity in spectral clustering for large datasets.
method A mixing process on a graph to find a linear combination of eigenvectors without computing eigenvectors.
result Partitioning datasets achieves better accuracy than standard spectral clustering methods.
The paper proposes methods for predicting missing values in mixed data matrices.
problem Matrix completion for mixed data types (continuous, binary, ordinal).
method Generalized latent factor models for low-rank matrix estimation with entrywise consistency.
result Tight probabilistic error bounds for the proposed estimators.
In mix-game which is an extension of minority game, there are two groups of agents; group1 plays the majority game, but the group2 plays the minority game. This paper studies the change of the average winnings of agents and volatilities vs. the change of mixture of agents in mix-game model. It finds that the correlatio…
Study compares clustering methods for mixed-type data.
problem Challenges in clustering mixed-type data.
method Distance-based (k-prototypes, PDQ, convex k-means), probabilistic (KAY-means, MBNs, LCM).
result KAMILA, LCM, and k-prototypes perform best.
A novel Bayesian approach for latent variable models from mixed data with missing values.
problem Learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values.
method Proposes a novel Bayesian Gaussian copula factor (BGCF) approach that is consistent and robust to violations of certain conditions.
result BGCF substantially outperforms two state-of-the-art alternative approaches in simulations and is favorable over robust maximum likelihood (MLR) in practice.
This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of th…
A new multivariate distribution for modeling tails and dependence structures.
problem Modeling tails and dependence structures in multivariate data.
method Generalized Mixed Tempered Stable distribution, random number generation, estimation based on characteristic functions.
result Improved model fitting for multivariate data with better tail behavior and dependence structure.
A new method for community detection in networks is presented.
problem Community detection in network analysis.
method Mixed regularized spectral clustering (Mixed-RSC) based on the regularized Laplacian matrix.
result The method is asymptotically consistent under mild conditions.
LSTM models improve macroeconomic forecasting with mixed frequency data.
problem Improving accuracy of macroeconomic forecasts using mixed frequency data.
method Adapted LSTM model to mixed frequency data, using U-MIDAS scheme.
result Proposed LSTM models outperform conventional MIDAS models in out-of-sample predictive performance.
Study rare-event simulation for neural networks and random forests.
problem Safety evaluation and robustness quantification of machine learning models.
method Importance sampling scheme integrating large deviations and sequential mixed integer programming.
result Efficiency guarantees and numerical demonstrations for various neural network architectures.
New method assesses financial and cyber risks under uncertainty.
problem Uncertainty in risk assessment for financial and cyber systems.
method Combines stochastic approximation and distorted mix method to compute worst case average value at risk.
result Efficient algorithm for tail uncertainty in multivariate distributions.
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.
Efficient algorithm for mixed membership models with large p.
problem Learning mixed membership models with high p and k.
method Efficient algorithm reducing tensor decomposition to sub-tensor factorization.
result Provable guarantees and competitive empirical results.
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 Krylov subspace methods speed up mixed-effects models with crossed random effects.
problem Slow computations for high-dimensional crossed random effects in mixed-effects models.
method Krylov subspace-based methods for generalized mixed-effects models with cross effects.
result Speedups by factors of up to 10,000 in computations for mixed-effects models.
New ICA method for sources with mixed spectra.
problem Inaccurate separation of sources with temporal autocorrelations and mixed spectra.
method Estimates spectral density functions and line spectra using cubic splines and indicator functions, then maximizes the Whittle likelihood function.
result Outperforms existing ICA methods in simulations and EEG data applications.
GP-MRO discovers robust mixed strategies for unknown objectives.
problem Optimizing unknown objectives against worst-case uncertain parameters.
method Sequential learning from noisy point evaluations, combining online learning and Gaussian processes.
result GP-MRO finds robust mixed strategies that significantly improve performance over deterministic strategies.
CausalMix generates synthetic data with causal controls for mixed-type tables.
problem Synthetic data for causal inference with mixed-type and multimodal tabular data.
method CausalMix combines Gaussian latent priors with data-type-specific decoders for control over overlap, confounding, and treatment effect heterogeneity.
result CausalMix achieves state-of-the-art distributional metrics and stable causal control.
New algorithm for sampling from distributions with thin tails.
problem Sampling from distributions with thin tails and theoretical guarantees.
method Proposes a Metropolized Algorithm With Optimization Step (MAO).
result Derives upper bounds on the mixing time of MAO.
Bayesian machine learning methods improve nowcasting with mixed frequency data.
problem Handling frequency mismatches and predicting short-term economic indicators.
method Developed Gaussian process (GP) methods for MIDAS regressions.
result Gaussian process-MIDAS methods offer gains in predictive accuracy.
New method for robust matrix completion with mixed data types.
problem Recovering a structured low rank matrix with mixed data types.
method Proposes a computationally feasible statistical approach with strong recovery guarantees for mixed data types.
result Strong recovery guarantees for low rank matrix completion with mixed data types.
Study integrates reliability constraints into generation planning models.
problem Challenges in integrating reliability constraints with generation planning models.
method Leverages a weighted oblique decision tree (WODT) technique to embed reliability verification constraints.
result Demonstrates effectiveness in achieving reliable and optimal planning solutions.
Logit-Coordinate models improve representation of mixed data types.
problem Representation of mixed continuous-categorical data in generative models.
method Logit-coordinate framework combining categorical and continuous variables.
result Logit-Coordinate models outperform one-hot encoding in simulations and real data.
Bayesian Optimization framework tackles materials design challenges with mixed variables.
problem Challenges in materials design due to mixed qualitative and quantitative variables, limited data, and high simulation costs.
method Data-centric, mixed-variable Bayesian Optimization framework using Latent Variable Gaussian Process (LVGP) and Expected Improvement acquisition function.
result Locates optimal design for insulating polymer nanocomposites efficiently.
HyDaP clusters mixed-type data efficiently.
problem Clustering data with mixed types (continuous and categorical).
method Two-step hybrid approach: density-based and partition-based for continuous variables, partition-based for mixed data.
result HyDaP outperforms existing methods in clustering electronic health records.
Method improves regression models using unlabeled data.
problem Improving predictive performance of regression models with limited labeled data.
method Mixed semi-supervised generalized-linear-regression with different mixing mechanisms.
result Integrating unlabeled data consistently improves predictive performance.
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.
Spectral methods improve signal recovery in mixed GLMs with precise asymptotics.
problem Estimating multiple signals from unlabeled observations in mixed GLMs.
method Developed exact asymptotics for spectral methods in a proportional regime.
result Optimized spectral method combined with a linear estimator minimizes estimation error.
Bayesian Tweedie mixed models are improved with adversarial variational inference.
problem Intractable likelihood function and hierarchical structure of mixed effects.
method Adversarial variational inference with reparameterization and flexible hyper prior.
result Proposed method reduces estimation bias and achieves state-of-the-art predictive performance.