Methodology for visualizing labeled datasets with mixed features.
problem Visualization of labeled mixed-featured datasets.
method Developed a Max-Ratio Projection (MRP) method for continuous features and extended it to datasets with discrete and continuous features using Gaussianized distributional transforms and copula models.
result Visualization of labeled mixed-featured datasets using Max-Ratio Projection and Gaussianized distributional transforms.
AutoSeM automatically selects and balances auxiliary tasks in MTL.
problem Choosing and balancing auxiliary tasks in MTL.
method AutoSeM uses a Beta-Bernoulli multi-armed bandit with Thompson Sampling for task selection and a Gaussian Process for learning the mixing ratio.
result AutoSeM achieves significant performance boosts on GLUE language understanding tasks.
In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning). We generate between-class images by mixing two images belonging to different classes with a random ratio. We then input the mixed image to the model and train the model to output the mixing ratio. BC …
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.
In the first part of the paper we survey some nonlocal flows of convex plane curves ever studied so far and discuss properties of the flows related to enclosed area and length, especially the isoperimetric ratio and the isoperimetric difference. We also study a new nonlocal flow of convex plane curves and discuss its e…
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.
Batchboost stabilizes training by mixing and pairing samples, improving accuracy.
problem Stabilizing training in machine learning, especially avoiding overfitting and underfitting.
method Batchboost pipeline with three stages: pairing, mixing, and feeding. Mixing uses mixup technique.
result Batchboost achieves 0.5-3% better accuracy than mixup on CIFAR-10 & Fashion-MNIST.
The study prevents model collapse in overparameterized linear regression by mixing real and synthetic labels.
problem Preventing model collapse in overparameterized linear regression.
method Iterative mixing of real and synthetic labels, deriving generalization error formulae.
result Optimal mixing ratio converges to the reciprocal of the golden ratio for isotropic features.
Study shows death ratio of COVID-19 deaths increases financial volatility.
problem Impact of COVID-19 deaths on financial markets.
method Analysis of VIX index based on daily case reports and death ratios.
result Death ratio positively influences financial volatility.
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 analyze a gradient flow of closed planar curves minimizing the anisoperimetric ratio. For such a flow the normal velocity is a function of the anisotropic curvature and it also depends on the total interfacial energy and enclosed area of the curve. In contrast to the gradient flow for the isoperimetric ratio, we sho…
Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature…
New method separates and deconvolves signals from single-channel mixtures.
problem Separating and deconvolving individual sources from a single-channel mixture.
method Synthesizing-decomposition (S-D) approach using GAN for sources and optimization for filters and sources.
result Achieves PSNR improvements over existing methods in various tasks.
Paper uses machine learning for nowcasting corporate earnings from mixed-frequency data.
problem Predicting corporate earnings for a large cross-section of firms with different frequency data.
method Structured machine learning regressions with sparse-group LASSO regularization for panel data.
result Machine learning models outperform traditional methods in nowcasting corporate earnings.
Paper proposes a new tensor model for mixed memberships and provides error bounds.
problem Estimating mixed memberships in higher-order multiway data.
method Tensor mixed-membership blockmodel, higher-order orthogonal iteration algorithm (HOOI), simplex corner-finding algorithm.
result Consistency of estimation procedure with error bounds under specific conditions.
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.
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.
Proposes incorporating noise sources in machine learning evaluation for more reliable conclusions.
problem Inadequate handling of nondeterminism in machine learning research leads to unreliable results.
method Uses linear mixed effects models (LMEMs) and generalized likelihood ratio tests (GLRT) to analyze performance evaluation scores and assess performance differences.
result Demonstrates how to incorporate various sources of noise and data properties into statistical significance testing and reliability analysis.
Maximizes mixing efficiency in surface braids.
problem Finding the maximum mixing efficiency in surface braids.
method Introduced an efficient algorithm to compute topological entropy and TEPO for surface braids.
result Conjectured a novel candidate braid to have maximal mixing efficiency.
We propose a method that performs anomaly detection and localisation within heterogeneous data using a pairwise undirected mixed graphical model. The data are a mixture of categorical and quantitative variables, and the model is learned over a dataset that is supposed not to contain any anomaly. We then use the model o…
EM algorithm achieves optimal sample complexity for learning two-component mixed linear regression.
problem Learning two-component mixed linear regression under varying signal-to-noise ratios.
method Analysis of EM algorithm convergence rates under different SNR regimes.
result EM algorithm achieves minimax optimal sample complexity in all SNR regimes.
Paper addresses covariate shift in deep learning regression models.
problem Covariate shift in dependent data from different distributions.
method Sparse-penalized deep neural network (SPDNN) estimator for nonparametric regression.
result Adaptive convergence rates for quantile and Huber regression.
Investment strategy using fractional Kelly portfolios for better growth expectations.
problem Understanding optimal growth strategies for investors with varying risk appetites.
method Developed a mathematical framework for fractional-Kelly portfolios, analyzing Sharpe ratios and log-returns.
result Fractional Kelly portfolios provide a simple distributional relationship between Sharpe ratio, fractional coefficient, and log-returns.
Recently, deep neural network (DNN) has made a breakthrough in monaural source enhancement. Through a training step by using a large amount of data, DNN estimates a mapping between mixed signals and clean signals. At this time, we use an objective function that numerically expresses the quality of a mapping by DNN. In …
Adapts Altman's model to compositional data for bankruptcy prediction.
problem Predicting business default using standard financial ratios has issues.
method Uses compositional data methodology with log-ratios and machine learning.
result Compositional methods improve predictive performance, especially random forests.
The paper generalizes deformation results for Fuchsian representations and shows proper affine actions.
problem Infinitesimal deformations of Fuchsian representations do not act properly in certain directions.
method Using results from Labourie--Wentworth, Potrie--Sambarino, and Smilga, the authors introduce affine versions of cross ratios and triple ratios, Margulis invariants, and relate them to infinitesimal Jordan projections.
result A general criterion for existence of proper affine actions in terms of Margulis invariant spectra.
Study long-only minimum variance portfolio in one-factor market with arbitrary sign betas.
problem Characterize the long-only minimum variance portfolio in a one-factor market with mixed-sign betas.
method Explicit solution for long-only minimum variance portfolio, explicit characterization of active set, asymptotic analysis in high-dimensional regime.
result Proportion of active assets in LOMV portfolio converges to F(β∗) in high-dimensional regime, with rate O(F(0)1/3) when F(0)>0. The article proposes modified Gower's coefficients for handling mixed type variables in nearest neighbor methods.
problem Handling mixed type variables in nearest neighbor methods, especially imputation and statistical matching.
method Suggests modifications to the Gower's distance for interval and ratio scaled variables to address unbalanced contributions and outlier sensitivity.
result Improved distance calculations reduce the unbalanced contribution of different variable types and attenuate outlier effects.
The paper finds optimal ways to combine ETFs to minimize costs for investors.
problem Finding the best combination of ETFs to match a target gearing ratio at the lowest expense.
method Linear programming and convex geometry to prove the two-fund theorem for ETFs.
result The cheapest way to achieve a target gearing ratio is by combining the two nearest undominated ETF products.
Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or supported by artificial intelligence resulting in mixed-intelligence traffic. This work explores the impli…
This paper proposes exact and approximation algorithms for Sparse PCA, improving interpretability and scalability.
problem Selecting a prespecified-size principal submatrix from a covariance matrix to maximize its largest eigenvalue.
method Proposes two exact mixed-integer SDPs and a mixed-integer linear program (MILP) for SPCA, analyzes theoretical optimality gaps, and develops approximation algorithms.
result The proposed algorithms achieve strong theoretical optimality and effective scalability, with continuous relaxations close to optimality and MILP solving small to medium-size instances.
Generative adversarial networks (GANs) are pow- erful generative models based on providing feed- back to a generative network via a discriminator network. However, the discriminator usually as- sesses individual samples. This prevents the dis- criminator from accessing global distributional statistics of generated samp…
The seniority of debt, which determines the order in which a bankrupt institution repays its debts, is an important and sometimes contentious feature of financial crises, yet its impact on system-wide stability is not well understood. We capture seniority of debt in a multiplex network, a graph of nodes connected by mu…
Paper studies convergence of Mean-Field GDA dynamics for MNE of continuous games.
problem Finding mixed Nash equilibria in continuous games.
method Two-scale Mean-Field Gradient Descent Ascent dynamics.
result Two-scale Mean-Field GDA converges exponentially to MNE without convexity assumptions.
Independent Component Analysis (ICA) is a popular model for blind signal separation. The ICA model assumes that a number of independent source signals are linearly mixed to form the observed signals. We propose a new algorithm, PEGI (for pseudo-Euclidean Gradient Iteration), for provable model recovery for ICA with Gau…
Wave-U-Net with MHE regularization improves singing voice separation.
problem Singing voice separation from mixed music recordings.
method Wave-U-Net architecture with MHE regularization applied to 1D filters.
result Adding MHE regularization to the loss function consistently improves singing voice separation.
Deep learning model uses mixed supervision for brain tumor segmentation.
problem Costly manual tumor segmentation data.
method Extends segmentation networks with an image-level classification branch.
result Significant improvement in segmentation performance.
Despite their exceptional flexibility and popularity, the Monte Carlo methods often suffer from slow mixing times for challenging statistical physics problems. We present a general strategy to overcome this difficulty by adopting ideas and techniques from the machine learning community. We fit the unnormalized probabil…
Paper addresses uncertainty in model generalization under regime shifts.
problem Uncertainty in model generalization under regime changes.
method Proposes a framework to quantify and separate regime mismatch and sensitivity.
result Obtains exact decomposition and minimax lower bound for regime-aware models.
End-to-end neural network optimizes portfolios by directly learning allocations from features.
problem Error maximization in two-step portfolio optimization.
method Single feed-forward neural network combining prediction and optimization.
result Model-based end-to-end framework achieves Sharpe ratio of 1.16.
A new method for speaker recognition on hyperspheres improves on PLDA's limitations.
problem Improving speaker recognition on hyperspheres with PLDA's limitations.
method Probabilistic Spherical Discriminant Analysis (PSDA) using Von Mises-Fisher distributions.
result PSDA scores are closed-form and can handle various trials, improving over PLDA.
Algorithm distinguishes Gaussian mixtures from pure Gaussians in quasi-polynomial time.
problem Distinguishing mixtures of Gaussian components from pure Gaussians, especially when components are well-separated.
method Sum-of-Squares method, quasi-polynomial time algorithm, bipartitioning sample to separate components.
result Algorithm can reliably distinguish between mixtures and pure Gaussians in quasi-polynomial time.
Optimal SD improves ridge regression performance strictly and precisely.
problem Improving ridge regression performance through self-distillation.
method Analyzes unconstrained SD for ridge regression, deriving optimal mixing weight and asymptotic risk.
result Optimal SD strictly improves ridge regression performance, with exact risk equivalents derived.
We consider the performance of the bootstrap in high-dimensions for the setting of linear regression, where p<n but p/n is not close to zero. We consider ordinary least-squares as well as robust regression methods and adopt a minimalist performance requirement: can the bootstrap give us good confidence intervals fo…
New methods for scalable causal discovery from complex data.
problem Learning causal structures from nonlinear, continuous or mixed data.
method BF-BIC score and BF-LRT test for scalable causal discovery.
result BF-BIC score and BF-LRT test enable scalable causal discovery with competitive accuracy and runtime.
The paper presents a framework to quantify the trade-off between synthetic and real data.
problem Improving generalization with synthetic data when real data is scarce.
method Learning-theoretic framework leveraging algorithmic stability to derive generalization error bounds.
result Optimal synthetic-to-real data ratio minimizing expected test error as a function of Wasserstein distance.
Theoretical analysis of entropy approximation for Gaussian mixtures.
problem Lack of theoretical guarantees for entropy approximation of Gaussian mixtures.
method Theoretical analysis of the error between true and approximate entropy.
result The error converges to zero as the ratios of means to variances tend to infinity, providing a guarantee for high-dimensional problems.
SIMPLE method quantifies uncertainty in network membership profiles.
problem Quantifying statistical uncertainty in network membership profiles.
method Degree-corrected mixed membership model, Hotelling-type statistics.
result Established exact limiting distributions of test statistics.