A new method for efficient inference in probabilistic programs with mixed support.
problem Challenges in inference for programs with both continuous and discrete latent variables.
method Stochastic gradient Markov Chain Monte Carlo algorithms.
result Outperforms existing composing inference baselines and works almost as well as inference in marginalized versions.
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.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
problem Flexible and composable nonlinear mixed-effects modeling
method Macro-based modeling language and unified interface
result Substantially expand the range of nonlinear mixed-effects models
Paper develops federated GLMM algorithms for analyzing hierarchical data.
problem Analyzing hierarchical data with non-independent observations in a federated setting.
method Developed two federated GLMM algorithms using Laplace and Gaussian Hermite approximations.
result Federated GLMM can handle hierarchical data and achieve comparable or superior performance.
Paper tackles best mixed arm identification with cost constraints in bandit models.
problem Finding the best mixed arm with cost constraints in a stochastic bandit model.
method Proposes SFSR algorithm combining successive reject and score-function-based rejection criteria.
result Upper and lower bounds on mis-identification probability show exponential decay with budget.
We improve Gaussian copula models for imputing mixed data types with precise approximations.
problem Imputing missing values with mixed data types in surveys and medical applications.
method We use randomized quasi-Monte Carlo procedures for direct and arbitrarily precise approximations of model estimation and imputation.
result Our method yields lower errors for model parameters and imputed values compared to existing methods.
Solves a long-standing convex geometry problem about mixed volumes.
problem Characterizing the support of mixed area measures.
method Geometric approach to convex bodies in R^n and R^3.
result Resolved one direction of Schneider's conjecture for arbitrary convex bodies.
We study the stochastic multi-armed bandit problem in the case when the arm samples are dependent over time and generated from so-called weak $\cC$-mixing processes. We establish a $\cC-$Mix Improved UCB agorithm and provide both problem-dependent and independent regret analysis in two different scenarios. In the first…
This paper studies convergence behavior of latent mixing measures that arise in finite and infinite mixture models, using transportation distances (i.e., Wasserstein metrics). The relationship between Wasserstein distances on the space of mixing measures and f-divergence functionals such as Hellinger and Kullback-Leibl…
A new framework uses an Incremental Transformer to design geopolymer mixtures efficiently.
problem Designing geopolymer mixtures with limited data and physical constraints.
method Topology-aware surrogate framework guided by Incremental Transformer.
result The design space is redundant, with fewer effective mixture regimes.
ContextFlow++ improves generative models by conditioning on mixed-variable contexts.
problem Lack of effective methods for context conditioning in flow-based generative models.
method Proposes ContextFlow++ with additive conditioning and mixed-variable architecture.
result ContextFlow++ achieves higher performance metrics and faster training.
The paper proves a unique conformal measure for Anosov groups and shows local mixing.
problem Proving the uniqueness of conformal measures for Anosov groups.
method Analogue of Sullivan's theorem for Anosov subgroups of semisimple groups.
result Uniqueness of conformal measures and local mixing for Anosov groups.
Study on recovering supports of multiple sparse vectors from mixed linear measurements.
problem Recovering supports of multiple sparse vectors from a mixture of linear measurements.
method Developed algorithms to identify the support of all component vectors using polynomial and quasi-polynomial number of measurements.
result Polynomial and quasi-polynomial number of measurements sufficient for recovering the supports of all component vectors.
Learning color mixing is difficult for novice painters. In order to support novice painters in learning color mixing, we propose a prediction model for semitransparent pigment mixtures and use its prediction results to create a Smart Palette system. Such a system is constructed by first building a watercolor dataset wi…
DiMMSB models directed mixed membership networks, identifying distinct community structures.
problem Modeling directed mixed membership networks with distinct community structures.
method Directed Mixed Membership Stochastic Blockmodel (DiMMSB) with DiSP algorithm.
result DiSP algorithm is asymptotically consistent and outperforms competitors.
Let X be a Kähler manifold and D be a R-divisor with simple normal crossing support and coefficients between 1/2 and 1. Assuming that K_X+D is ample, we prove the existence and uniqueness of a negatively curved Kahler-Einstein metric on X\D having mixed Poincaré and cone singularities according to the coefficients of D…
Study compares neural and statistical models for Parkinson's disease progression from voice data.
problem Difficult statistical analysis of longitudinal voice biomarkers due to subject correlation, small cohorts, and varied disease trajectories.
method Evaluated Neural Mixed Effects (NME), Generalized Neural Network Mixed Models (GNMMs), and semi-parametric Generalized Additive Mixed Models (GAMMs).
result GAMMs achieve stronger predictive performance and retain interpretable smooth effects and subject-level structure.
Paper proposes methods to improve SVM classifiers in noisy data scenarios.
problem Improving SVM classifiers when training data contains label noise.
method Mixed Integer Linear and Non Linear models with relabeling and clustering.
result Effective methods improve SVM performance in noisy data scenarios.
New method combines domain changes and sparse mixing for better latent variable learning.
problem Challenges in identifying latent variables due to insufficient domain changes and violated sparsity constraints.
method Combines sufficient changes and sparse mixing constraints, using domain encoding networks and variational autoencoders.
result Identifiability of latent variables achieved with less restrictive constraints.
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.
Method completes mixed matrix from complex surveys with heterogeneous missingness.
problem Recovering a mixed dataframe matrix from complex survey sampling with different missingness patterns.
method Two-stage procedure: logistic regression for missingness modeling, and weighted log-likelihood maximization with low-rank constraint.
result The proposed method achieves sublinear convergence and shows superior performance compared to existing methods.
Algorithm learns near-optimal policies for reward-mixing MDPs with few latent contexts.
problem Episodic reinforcement learning in reward-mixing Markov decision processes with a few latent contexts.
method Sample-efficient algorithm EM^2 using higher-order method-of-moments approach.
result Provides an ε-optimal policy using O(ε^(-2) * S^d A^d * poly(H, Z)^d) episodes for arbitrary M ≥ 2.
SBMs learn manifold-like structures by mixing samples with a non-conservative field.
problem How SBMs learn data distributions on low-dimensional manifolds.
method Investigating linear approximations and subspaces of local feature vectors during diffusion.
result SBMs mix samples by a non-conservative field within the manifold, maintaining manifold-like structure.
New sampling method improves accuracy for constrained spaces.
problem Sampling from constrained convex subsets of R^d.
method Metropolis-adjusted Preconditioned Langevin Algorithm.
result High-accuracy sampling with polylogarithmic error dependence.
New insights into statistical and computational limits for mixed sparse linear regression.
problem Recovering two sparse signals from noisy linear measurements.
method Analysis of low-degree polynomials and a simple thresholding algorithm.
result Identification of a smooth information-computation tradeoff and order-optimality of the thresholding algorithm.
Low-precision DNNs have been extensively explored in order to reduce the size of DNN models for edge devices. Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision in [5..8]-bits. However, previous studies were limited to studying posit for DNN inference…
Study shows DQN's performance degrades with temporal dependence in data.
problem Temporal dependence in replayed data affects DQN's performance.
method Modelled τ-mixing data, derived risk bounds, and empirical validation. result Temporal dependence leads to a degradation in DQN's performance rate.
Study nearest-neighbor radii under dependent sampling, finding they remain informative.
problem Analyzing nearest-neighbor radii under dependent sampling.
method Consider strong mixing dependent observations, establish distribution-free almost sure convergence and sharp non-asymptotic moment bounds.
result Nearest-neighbor geometry remains informative under dependence sampling.
Paper introduces DP methods for high-dimensional variable selection.
problem Sparse variable selection in high-dimensional learning.
method Pure differentially private estimators using Integer Programming.
result Achieves state-of-the-art empirical support recovery.
Solves Christoffel-Minkowski problem and Hessian equations with radial symmetry.
problem Christoffel-Minkowski problem and Hessian equations under rotational symmetries.
method Constructing explicit convex solutions to mixed Monge-Ampère equations on \(\mathbb{R}^n\) under radial symmetry.
result Explicit representation formula for the support function of the resulting convex body.
New method uses dendrograms for better mixture model selection and clustering.
problem Selecting the correct number of components in finite mixture models.
method Hierarchical clustering tree derived from overfitted latent mixing measures.
result Consistently selects the true number of mixing components and optimal convergence rate for parameter estimation.
This paper investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by \emph{general}, we mean that many stationary stochastic processes can be included. We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a u…
Wasserstein framework solves mixed linear regression problems.
problem Mixed linear regression with multi-modal distributions.
method Wasserstein distance minimization for nonconvex-concave minimax optimization.
result WMLR achieves global convergence and generalization guarantees for two linear models.
Study on counting orbits and Poincaré series for specific hyperbolic metrics.
problem Counting orbits and analyzing Poincaré series for strongly hyperbolic metrics.
method Combining ergodic theory techniques with topological flows and symbolic dynamics.
result Obtained orbital counting results and described the domain of analyticity for Poincaré series.
Distributed representations have been used to support downstream tasks in healthcare recently. Healthcare data (e.g., electronic health records) contain multiple modalities of data from heterogeneous sources that can provide complementary information, alongside an added dimension to learning personalized patient repres…
Study improves maize yield prediction using BNs with mixed-effects models.
problem Limited causal inference in agronomic data models.
method Integrates random effects into Bayesian networks, leveraging hierarchical data structure.
result Significantly reduces maize yield prediction error from 28% to 17%.
dcFCI discovers causal relationships robustly under latent confounding and mixed data.
problem Causal discovery under latent confounding and unfaithfulness.
method dcFCI integrates a new score to assess PAG compatibility, guided by FCI search.
result Significantly outperforms state-of-the-art methods in small and heterogeneous datasets.
This work focuses on support vector machine (SVM) with feature selection. A MILP formulation is proposed for the problem. The choice of suitable features to construct the separating hyperplanes has been modelled in this formulation by including a budget constraint that sets in advance a limit on the number of features …
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.
We extend neural networks with fractional and mixed activation functions for better function approximation.
problem Limitations in approximating higher-order smooth functions in complex spaces.
method Incorporating fractional exponents in activation functions and defining new density functions.
result Improved accuracy and broader applicability of neural network approximation theory.
Improved density estimation for mixed discrete-continuous data.
problem Inconsistent density estimation for mixtures of continuous and discrete data.
method Modification of existing nonparametric density estimation methods to handle mixed discrete-continuous data.
result Improved consistency and empirical performance for mixed discrete-continuous data.
Gradient EM converges exponentially to optimal solution in agnostic mixtures.
problem Fitting k parametric functions to given data points without a generative model. method Gradient EM algorithm for agnostic mixtures of arbitrary parametric functions.
result Gradient EM converges exponentially to population loss minimizers with high probability.
Develops HDNNs for mixed geoscience data inputs.
problem Lack of multisource, multi-scale information in deep learning studies.
method Hybrid architecture combining feature and target learning.
result HDNNs achieve higher accuracy and better generalization in reservoir prediction.
Character expansion expresses extended HOMFLY polynomials through traces of products of finite dimensional R- and Racah mixing matrices. We conjecture that the mixing matrices are expressed entirely in terms of the eigenvalues of the corresponding R-matrices. Even a weaker (and, perhaps, more reliable) version of this …
Model infers functions for attributes using multi-aggregate datasets with knowledge transfer.
problem Modeling aggregate data with varying granularities and spatial supports.
method Multi-output Gaussian process (MoGP) with linear mixing of independent latent GPs, aggregation process, and prior distribution of mixing weights.
result Proposed model outperforms in refining coarse-grained aggregate data.
Gibbs sampler mixes quickly for certain smooth distributions.
problem Drawing samples from log-smooth log-concave distributions.
method Analyzes Gibbs sampler on log-smooth and strongly log-concave distributions.
result Gibbs sampler mixes in O⋆(κ2n7.5) steps. 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,…
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.