VAEs are a neural network approach to unsupervised learning.
problem Learning complicated distributions without labeled data.
method Variational Autoencoders use neural networks and stochastic gradient descent.
result VAEs have successfully generated various complicated data types.
i-flow uses normalizing flows for high-dimensional integration and sampling.
problem High-dimensional integration in science and statistics.
method Normalizing flows for bijective mappings between distributions.
result i-flow outperforms other algorithms for high-dimensional correlated integrals.
We analyze the (unconditional) distribution of a linear predictor that is constructed after a data-driven model selection step in a linear regression model. First, we derive the exact finite-sample cumulative distribution function (cdf) of the linear predictor, and a simple approximation to this (complicated) cdf. We t…
New algorithm learns two-layer neural networks under symmetric input distributions.
problem Learning two-layer neural networks with symmetric inputs.
method Method-of-moments framework, spectral algorithms.
result Guaranteed to recover parameters of ground-truth network under certain conditions.
The problem of determining the joint probability distributions for correlated random variables with pre-specified marginals is considered. When the joint distribution satisfying all the required conditions is not unique, the "most unbiased" choice corresponds to the distribution of maximum entropy. The calculation of t…
Paper proposes a multi-task learning approach to predict multiple diabetes complications.
problem Risk prediction and profiling of diabetes complications for personalized treatment plans.
method Multi-task learning approach with coefficient shrinkage and hierarchical Bayesian framework.
result The proposed method outperforms state-of-the-art models in predicting multiple diabetes complications.
New model detects postoperative complications early after surgery.
problem Early detection of postoperative complications in patients.
method Hidden Markov Model sequence classifier analyzing postoperative temperature sequences.
result Improved classification performance compared to other machine learning classifiers.
By Federer and Fleming there exist at least one mass-minimizing normal current in every real-valued homology class of a Riemannian manifold. However the regularity of the mass-minimizing currents and their distributions may generally be quite complicated. In this paper we shall study how to construct nice metrics so th…
The most direct way to express arbitrary dependencies in datasets is to estimate the joint distribution and to apply afterwards the argmax-function to obtain the mode of the corresponding conditional distribution. This method is in practice difficult, because it requires a global optimization of a complicated function,…
Study describes severe dengue ICU patients in Brazil, 2012-2024.
problem Characterize severe dengue ICU patients and identify risk factors.
method Prospective study, descriptive statistics, logistic regression, machine learning.
result Advanced age, comorbidities, leukocytes, and platelets are significant risk factors for complications.
Deep learning models predict postoperative complications more accurately than random forests.
problem Predicting postoperative complications to inform patient care decisions.
method Multi-task deep neural networks integrating intraoperative physiological data.
result Deep learning models improved prediction accuracy and provided interpretable risk factors.
Adaptive model learns from time series data with changing distributions.
problem Predicting time series data under distribution shift.
method Formulates distribution shift as weighted empirical risk minimization. Uses a gradient-based learning method for a forgetting mechanism.
result Proposes an efficient method for adaptive time series prediction.
New method improves Bayesian cross-validation.
problem Finding good proposal distributions for importance sampling.
method Implicitly adaptive importance sampling that iteratively matches moments.
result Better than many existing parametric adaptive importance sampling methods.
Paper explores how generative models can be made more creative.
problem Limitation of generative models in diverging from original data distribution.
method Proposes a novel training objective called Bounded Adversarial Divergence (BAD) to enable creative divergence.
result Preliminary results suggest BAD can enable creative divergence in generative models.
The aim of this paper is to propose a heterogeneous agent model of stock markets that develop complicated endogenous price fluctuations. We find occurrences of non-stationary chaos, or speculative bubble, are caused by the heterogeneity of traders' strategies. Furthermore, we show that the distributions of returns gene…
New method simplifies Bayesian inference for multi-Dirichlet priors.
problem Inference for models with hierarchical Multi-Dirichlet priors is tricky.
method Auxiliary variable scheme simplifies joint distribution of model parameters.
result Efficient inference schemes derived using the auxiliary variable scheme.
Authors construct an example of a Schottky group of rank three.
problem Theoretical existence of non-classical Schottky groups in higher ranks.
method Provided a method to construct sufficiently complicated noded Schottky groups of any rank.
result Explicit construction of a sufficiently complicated noded Schottky group of rank three.
AI identifies patient clusters for diabetes case management.
problem Diabetes complications and mental health comorbidities drive high healthcare costs.
method Combined AI techniques with diverse data sources for prediction and clustering.
result 83.5% accuracy in predicting diabetes complications and meaningful patient clusters.
This study compares different types of normalizing flows for generating complex distributions.
problem Comparing different types of normalizing flows for generating complex distributions.
method Real-valued non-Volume preserving (RealNVP), masked autoregressive flow (MAF), coupling rational quadratic spline (C-RQS), and autoregressive rational quadratic spline (A-RQS) were compared using statistical tests.
result A-RQS algorithm outperforms others in terms of accuracy and training speed.
Proposes vMF distribution for skewed elliptical distributions.
problem Skewed distributions not adequately modeled by symmetric distributions.
method Introduces von-Mises-Fisher (vMF) distribution to represent skewed elliptical distributions.
result vMF distribution provides an explicit and simple probability representation of skewed elliptical distributions.
Dehn surgery on complicated fibered knots doesn't yield lens spaces.
problem Understanding when Dehn surgery on fibered knots results in lens spaces.
method Analyzing the monodromy of fibered knots and their Dehn surgeries.
result If the monodromy is sufficiently complicated, Dehn surgery on a fibered knot doesn't yield a lens space.
We construct families of manifolds that have pairs of genus g Heegaard splittings that must be stabilized roughly g times to become equivalent. We also show that when two unstabilized, boundary-unstabilized Heegaard splittings are amalgamated by a "sufficiently complicated" map, the resulting splitting is unstabili…
The paper proposes a method for interpretable mixture density estimation using a tree structure.
problem Complex probability distributions in machine learning models.
method Interpretable tree structure for mixture density estimation with fast inference.
result The method achieves both high speed and interpretability for mixture density estimation.
A new random forest method for multivariate distributions.
problem Estimating complex multivariate distributions with heterogeneity.
method A novel splitting criterion based on MMD for multivariate responses.
result Estimates full conditional distribution for arbitrary targets.
A new base distribution for normalizing flows allows modeling complex distributions without sacrificing invertibility.
problem Normalizing flows struggle with complex, non-trivial distributions.
method Learned rejection sampling for base distribution, combined with optimization of log-likelihood and Kullback-Leibler divergence.
result The method effectively models complicated distributions without sacrificing invertibility.
New method trims network data to resist adversarial contamination.
problem Adversarial contamination in network data affects statistical and algorithmic performance.
method Proposes a new trimming method operating in model space to address both block and white noise contamination.
result Demonstrates superior performance in simulations compared to direct trimming.
Unified approach for robust and heavy-tailed mean estimation in high dimensions.
problem Estimating mean in high dimensions with adversarial corruption or heavy-tailed distributions.
method Unified meta-problem and duality theorem leading to Filter algorithm and QUE scheme.
result Unified and efficient algorithms for both robust and heavy-tailed mean estimation.
Let M be a 3-manifold with torus boundary components T1 and T2. Let φ:T1→T2 be a homeomorphism, Mφ the manifold obtained from M by gluing T1 to T2 via the map φ, and T the image of T1 in Mφ. We show that if φ is "sufficiently complicated" then any incompressible or strongly …
New method trains Markov kernels for efficient sampling.
problem Efficient sampling from complex probability distributions.
method Adversarial learning of involutive Metropolis-Hastings kernels.
result Minimizes total variation distance to empirical data.
Solar improves variable selection in high-dimensional data with complicated dependence structures.
problem Variable selection in ultrahigh dimensional data with severe multicollinearity and grouping effect issues.
method Subsample-ordered least angle regression (Solar) for ultrahigh dimensional data.
result Solar yields substantial improvements in sparsity, stability, and accuracy of variable selection compared to traditional methods.
This paper introduces a spline-based method for nonparametric ADVI that handles complex posterior distributions.
problem Learning complex posterior distributions with skewness, multimodality, and bounded support.
method Develops a spline-based nonparametric approximation approach for ADVI.
result Establishes the asymptotic consistency of the derived lower bound for importance weighted autoencoder.
We solve the mean parametrization of von Mises-Fisher distribution.
problem No closed-form normalization function for mean parameters exists.
method Derived a second-order ODE for mean normalizer and provided approximations.
result Rapid evaluation of densities and natural parameters in terms of mean parameters.
Simple math predicts growth trends.
problem Complex growth projections are hard to understand.
method Direct or indirect analysis of growth rates.
result Simple assumptions lead to understandable growth predictions.
GraphVRNN generates graphs with latent variables and node attributes.
problem Generating diverse and complex graph structures.
method Probabilistic autoregressive model for graph generation.
result GraphVRNN can model complicated distributions and generate plausible structures and node attributes.
Simple model predicts trajectory probabilities.
problem Forecasting trajectories from visual data.
method Spatio-temporal convolutional neural network.
result Achieves results on par with or better than existing methods.
Fiber simplifies RL and population-based methods for distributed training.
problem Challenges in RL and population-based methods, including frequent interaction with simulations and dynamic scaling.
method Introducing Fiber, a scalable distributed computing framework.
result Significantly expands accessibility of large-scale parallel computation.
We analyze the Levy processes produced by means of two interconnected classes of non stable, infinitely divisible distribution: the Variance Gamma and the Student laws. While the Variance Gamma family is closed under convolution, the Student one is not: this makes its time evolution more complicated. We prove that -- a…
Fitting models for non-Poisson point processes is complicated by the lack of tractable models for much of the data. By using large samples of independent and identically distributed realizations and statistical learning, it is possible to identify absence of fit through finding a classification rule that can efficientl…
New method learns from unreliable sources effectively.
problem Learning from untrusted, distributed, or private data sources.
method Statistical learning theory approach to suppress irrelevant or corrupted data.
result Significant improvements over alternative approaches in robust learning.
Paper proposes a method to efficiently test rare vehicle failures using kernel methods.
problem Efficiently test rarely occurring but critical vehicle failures.
method Uses kernel methods to approximate and construct sampling distributions for rare event sets.
result Proposed method robustly identifies rare vehicle failures and significantly reduces evaluation time.
A new MCMC method tackles doubly intractable posterior problems.
problem Sampling from complicated distributions with doubly intractable posterior.
method Multi-armed Bandit MCMC (MABMC) algorithm.
result MABMC achieves higher average acceptance probability than existing methods.
A quick gamma approximation speeds up Bayesian inference.
problem Inconvenient gamma shape parameter conjugate priors in Bayesian models.
method Introduced an easy algorithm to approximate gamma shape parameter full conditional by another gamma distribution.
result The approximation is accurate and fast, even for small sample sizes.
CQNPs enhance predictive performance and distribution modeling using quantile regression.
problem Limited predictive likelihood of Gaussian models for complex distributions.
method Introducing Conditional Quantile Neural Processes (CQNPs) that focus on estimating informative quantiles.
result Significant improvements in predictive performance and better modeling of multimodal distributions.
Paper addresses unbalanced data in common shock models for loss reserving.
problem Complications in capturing structural dependence with unbalanced data.
method Introduces a common shock Tweedie approach for unbalanced data.
result Better balance of common shock proportions and parsimonious solution.
A simple geometrical proof shows that any target function can be found in a random network's neighborhood.
problem Finding any target function in a random network's neighborhood.
method Geometrical proof using a simple model of a high-dimensional sphere projected onto a low-dimensional subspace.
result High-dimensional geometry ensures that a uniform distribution over a sphere reduces to a Gaussian distribution with negligible covariances, enabling the presence of any target function in a random network's neighborhood.
AIS algorithm improves heavy-tailed distribution estimation.
problem Inconsistent estimators and slow convergence in AIS for heavy-tailed distributions.
method Adapts Student-t proposal distributions by matching escort moments and minimizing α-divergence.
result Improves estimation accuracy for heavy-tailed distributions.
The paper explores non-classical Schottky groups and their properties.
problem Characterizing and understanding non-classical Schottky groups.
method Theoretical construction and analysis of infinite collections of Schottky groups.
result Construction of non-classical Schottky groups and examples.
A new test for volatility in clustered time series data, robust to distributional assumptions.
problem Volatility issues in clustered multiple time series data, especially in stock market indicators.
method Bootstrap method for multiple time series, accounting for contagion effect.
result The test is correctly sized and powerful, especially for stationary mean and contained volatility in fewer clusters.