Generators found for a complex hyperbolic braid group.
problem Finding generators for a specific complex hyperbolic braid group.
method Removing a hyperplane arrangement, taking a quotient, and finding generators for the orbifold fundamental group.
result Generators have a natural form corresponding to nearest hyperplanes.
New insights into a complex hyperbolic braid group quotient.
problem Understanding a complex hyperbolic braid group quotient.
method Analyzing the moduli space of 12-tuples in CP1 and identifying loops.
result Identifying loops in the 9-ball quotient corresponding to standard braid generators.
New universal automorphic functions capture monstrous moonshine.
problem Developing a universal framework for automorphic functions.
method Reformulating old results, constructing new coordinates, and defining central extensions.
result New invariant 1-forms and representations for universal Teichmüller space.
Meta-learning improves MCMC proposal efficiency.
problem Manual construction of model-specific MCMC proposals is time-consuming and model-specific.
method Parametrize MCMC proposals as neural networks and use meta-learning to generalize across models.
result Learned neural proposals accelerate inference on unseen models without model-specific training.
New MCMC methods improve sampling efficiency and mixing.
problem Improving sampling efficiency and mixing in MCMC methods.
method Sequential proposals in MCMC methods, including HMC and bouncy particle sampler.
result New methods improve numerical efficiency and mixing in multimodal distributions.
Improved sampling for complex distributions using quasi-Newton proposals.
problem Sampling from complex, high-dimensional target distributions efficiently.
method Extended pseudo-marginal Metropolis-Hastings with quasi-Newton proposals.
result Quasi-Newton proposals outperform standard random-walk and Hessian-based proposals.
Enhances RJMCMC efficiency with non-linear transport-based proposals.
problem Designing efficient RJMCMC proposals for complex models.
method Applies non-linear transport-based approach to construct efficient transdimensional jumps.
result Acceptance probability depends only on model probabilities when exact transports are used.
Optimizes noisy IS with better proposal densities.
problem Improving IS estimators with noisy data.
method Derives optimal proposal densities considering noise variance.
result Optimal proposals enhance IS estimators by focusing on noisy regions.
Neural network MCMC sampler maximizes proposal entropy for efficient sampling.
problem Inefficient sampling from complex probability distributions.
method Proposes a neural network MCMC sampler that maximizes proposal entropy.
result Significantly higher efficiency in various sampling tasks.
Convex norms improve coupled matrix and tensor completion.
problem Efficiently complete coupled matrices and tensors with shared information.
method Proposed convex norms and completion algorithm.
result Excess risk bounds show improved performance compared to uncoupled norms.
Proposes a new model for clustering with heavier tails.
problem Clustering with heavy-tailed data.
method Finite mixture of skewed sub-Gaussian stable distributions, maximum likelihood estimation, EM algorithm.
result The proposed model can robustly handle heavy-tailed data.
Paper proposes BTuD for unsupervised feature selection.
problem Feature selection in unsupervised learning.
method Bayesian Tucker decomposition (BTuD) with Gaussian residual.
result Successfully applied to various datasets.
A new method for one-class classification using ellipsoidal encapsulation.
problem One-class classification for data optimization.
method Iterative transformation into an optimized subspace with regularization terms.
result Better results in one-class classification compared to existing methods.
Exact PA algorithms learn to rank with interval labels.
problem Learning to rank with interval labels.
method Exact passive-aggressive algorithms solving convex optimization problems.
result Maintains threshold ordering and achieves accurate classifiers.
In our work, we propose a novel formulation for supervised dimensionality reduction based on a nonlinear dependency criterion called Statistical Distance Correlation, Szekely et. al. (2007). We propose an objective which is free of distributional assumptions on regression variables and regression model assumptions. Our…
The paper proposes a method to model financial data asynchronously using copulas.
problem Modeling intraday financial returns of multiple assets due to asynchronous data.
method Proposes a consistent estimator of the correlation coefficient for Elliptical copulas and an improved estimator for non-elliptical copulas.
result The proposed estimator reduces bias in estimating copula parameters for a general class of copulas.
A method to automatically learn proposal distributions for energy-based regression models.
problem Manual design and initial estimate of proposal distributions for energy-based regression models.
method Introduces a method to learn an effective proposal distribution automatically, parameterized by a separate network head, and derives a unified training objective to minimize KL divergence and negative log-likelihood.
result Consistently outperforms conventional MDN training on four real-world regression tasks within computer vision.
Paper proposes online algorithms for multiclass classification with partial labels.
problem Classifying data with partial labels.
method Avg Perceptron, Max Perceptron, Avg Pegasos, Max Pegasos algorithms.
result Mistake bounds for Avg Perceptron and regret bound for Avg Pegasos.
Proposes a method to improve SLMC for multimodal distributions.
problem Difficulty of applying SLMC to multimodal distributions.
method Parallel adaptive annealing with VAE-SLMC.
result Can proficiently obtain accurate samples from multimodal distributions.
This paper proposes a new method to improve the MH algorithm for Bayesian estimation.
problem Difficulty in tuning the proposal distribution for efficient convergence in MH algorithms.
method Uses damped BFGS updates to incorporate gradient and curvature information from numerical optimization.
result Empirically demonstrates improved mixing and convergence of MH algorithm realisations.
Proposes a method to refine PDE-driven high-dimensional rare-event simulation.
problem Challenges in constructing accurate surrogates for rare-event simulation.
method Adaptive importance sampling framework that refines a locally constructed surrogate.
result Achieves accuracy comparable to true-model adaptive importance sampling with fewer high-fidelity evaluations.
Two new scalable K-means initialization methods proposed for large-scale clustering.
problem Efficient initialization for large-scale clustering problems.
method Divide-and-conquer approach and random projection method for multiple lower-dimensional subspaces.
result The proposed methods outperform state-of-the-art in large-scale clustering tasks.
We propose a new methodology based on the Marshall-Olkin (MO) copula to model cross-border systemic risk. The proposed framework estimates the impact of the systematic and idiosyncratic components on systemic risk. Initially, we propose a maximum-likelihood method to estimate the parameter of the MO copula. In order to…
Improved object detection for scientific document images.
problem Current object detectors fail to accurately localize regions in scientific document images.
method Revised R-CNN model with region embedding for fine-grained proposals.
result 17% mAP improvement over standard object detection models.
Adaptive sampling method improves efficiency in complex target distributions.
problem Efficiency of importance sampling in complex target distributions, especially multimodal distributions in high-dimensional spaces.
method Proposes an adaptive scheme combining global sampling with delayed weighting to promote efficient exploration of target distributions.
result The proposed algorithm is geometrically convergent under mild assumptions and demonstrates improved efficiency in various numerical experiments.
Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization …
Proposes neural network-enhanced Cox model for time-to-event prediction.
problem Improving time-to-event prediction accuracy.
method Extends Cox proportional hazards model with neural networks, using a scaled loss function.
result Proposed methodology outperforms existing methods in terms of Brier score and binomial log-likelihood.
Proposes a new graph trend filtering model for inhomogeneous graph signals.
problem Estimating piecewise smooth signals over a graph with varying smoothness levels.
method Introduces a l2,0 norm penalized Graph Trend Filtering (GTF) model and two solution methods: spectral decomposition and simulated annealing.
result The GTF model performs better than existing approaches in denoising, support recovery, and semi-supervised classification.
Proposes a gradient-based variable selection method for binary classification in RKHS.
problem Variable selection in high-dimensional data analysis.
method Gradient-based representation of large-margin classifier with group-lasso penalty.
result Selection consistency and risk bound of the estimated classifier.
This paper uses MH algorithm to improve variational inference and GANs.
problem Improving sampling efficiency in Bayesian inference and GANs.
method Proposes learning an independent sampler to maximize MH acceptance rate, related to variational inference. Deduces GANs from MH perspective.
result Improves variational inference and GANs performance on real-world datasets.
Proposes an ensemble loss function for robust regression.
problem Improving robustness of simple regression models in noisy environments.
method Ensemble techniques applied to a simple regressor with a half-quadratic learning algorithm.
result Significantly improves performance of simple regressors in noisy environments.
Proposes a method for selecting variables in nonparametric learning using power series kernels.
problem Variable selection in nonparametric learning with power series kernels.
method Two-stage estimation: consistent function approximation followed by l1-type penalized variable selection.
result The method achieves variable selection consistency for power series kernels.
Distributed statistical inference has recently attracted enormous attention. Many existing work focuses on the averaging estimator. We propose a one-step approach to enhance a simple-averaging based distributed estimator. We derive the corresponding asymptotic properties of the newly proposed estimator. We find that th…
REP-GAN improves GANs by reparameterizing proposals for better sample quality and efficiency.
problem Poor sample efficiency in GANs due to independent proposal sampling.
method REParameterizing Markov chains into the latent space of the generator to create dependent proposals.
result Empirically shows significant improvement in sample efficiency and quality.
Proposes supervised method for whole DAG causal structure learning.
problem Learning causal directions from data, especially for whole DAG structure.
method Supervised learning approach using permutation equivariant models.
result Promising results compared to previous approaches on synthetic and real data.
We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC metho d itself. It turns out that autocorrelations between the data generated …
Proposes MPCA for robust PCA using mode estimation.
problem Outliers sensitivity in PCA.
method Modal Principal Component Analysis (MPCA) based on mode estimation.
result MPCA shows advantages over conventional methods.
Proposes a Koopman operator method for time-dependent reliability analysis of nonlinear systems.
problem Challenges in time-dependent reliability analysis of nonlinear dynamical systems.
method Koopman operator approach for transforming nonlinear systems into linear ones, combined with deep learning for intrinsic coordinates.
result Robust and generalizable approach for time-dependent reliability analysis, superior to purely data-driven methods.
Proposes CMP method for reducing tensor object dimensions in binary classification.
problem Reduction of tensor object dimensions while maintaining class separability.
method Proposes Common Mode Patterns (CMP) method considering class labels.
result CMP method increases inter-class separability compared to MPCA.
Proposes HTM for aggregating ranked data considering user accuracy.
problem Aggregating ranked data from heterogeneous users with varying accuracy levels.
method Heterogeneous Thurstone Model (HTM) with alternating gradient descent algorithm.
result Algorithm converges linearly and outperforms existing methods.
Paper proposes a method to reduce sensor drift in electronic noses.
problem Sensor drift in electronic noses.
method Discriminative subspace projection approach.
result The method minimizes within-class variance and maximizes between-class variance using label information.
Proposes a Bayesian model for variable clustering with Gaussian graphical models to handle noise.
problem Noise in partial correlations can affect variable clustering results.
method Develops a Bayesian model that accounts for small but not zero partial correlations, evaluates using marginal likelihood.
result The proposed method is more accurate than BIC in noisy settings and provides more sensible clustering results.
Proposes approximating computationally expensive explainability techniques using conformal regression.
problem Computational expense of score-based explainability techniques limits their applicability in time-critical contexts.
method Uses conformal prediction framework to approximate SHAP and TreeSHAP explanations.
result Significantly improves execution time and produces tight validity guarantees.
Proposes a non-crossing deep neural network quantile regression method.
problem Quantile crossing in nonparametric quantile regression.
method Non-crossing constraints via rectified linear unit penalty function.
result Established non-asymptotic upper bounds for excess risk.
New algorithms for risk management in incomplete markets.
problem Risk management in incomplete markets with various sources of incompleteness.
method Machine-learning-based algorithms to solve hedging problems.
result One algorithm is flexible and can use multiple risk criteria.
Proposes FMPCA for federated tensor data dimensionality reduction.
problem Integration of MPCA into federated learning.
method Federated Multilinear Principal Component Analysis (FMPCA).
result FMPCA preserves performance of traditional MPCA in federated learning.
Proposes a neural network loss function for better uncertainty estimation.
problem Challenges in estimating predictive uncertainty of neural networks.
method Bayesian Validation Metric (BVM) framework with ensemble learning.
result Competitive and robust uncertainty estimation on in-distribution and out-of-distribution data.
Proposes a new model for complex multivariate event data.
problem Modeling complex multivariate event data with spatio-temporal dynamics.
method Integrates spatial information into latent state evolution through learned temporal and spatial decay dynamics.
result Successfully recovers sensible temporal and spatial intensity structure in multivariate spatio-temporal point patterns.