A new method detects epileptic events in EEG signals by integrating labeler categories.
problem Human oversight of brief epileptic events in EEG signals leads to inaccurate diagnoses.
method Integrates EEG signal features with one-hot encoded labeler categories for improved detection.
result The method outperforms consensus-trained detectors and maintains confidence bounds.
Efficient method for vertex embedding and community detection.
problem Vertex embedding and community detection.
method Normalized one-hot graph encoder and rank-based cluster size measure.
result Excellent numerical performance of graph encoder ensemble algorithm.
Study hot spots on warped product manifolds and infinite cones.
problem Analyzing hot spots on specific geometric structures.
method Examining solutions to the heat equation on warped product manifolds and infinite cones.
result Hot spots behavior on warped product manifolds and infinite cones determined.
Hot spots conjecture proven for small eigenvalue domains.
problem Hot spots conjecture for hyperbolic planar domains with small eigenvalues.
method Proved a variant of Rauch's hot spots conjecture.
result Second Neumann Laplace eigenfunctions have no interior critical points on large convex domains.
Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. The…
The study proves constant-curvature analogues of hot spots conjecture for triangles.
problem Proving the hot spots conjecture in constant curvature domains.
method Analyzing geodesic triangles of constant negative curvature and using Killing fields.
result First mixed Dirichlet-Neumann Laplace eigenfunctions have no non-vertex critical points in constant curvature triangles.
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process. Automatic data-driven approaches are widely needed and seen as a promising area of investment. This paper proposes an effective machine learning algorithm to predict industrial…
The paper compares one-hot encoding to Naïve Bayes for categorical variables.
problem Incorrect one-hot encoding affects Naïve Bayes performance.
method Mathematical and experimental analysis of PoB vs. categorical Naïve Bayes.
result Posterior probabilities are usually greater in the PoB case, but agree on the maximum a posteriori class label.
Hollow-tree Super resolves feature importance in large datasets.
problem Lack of effective scaling for large feature numbers in boosted tree models.
method Hollow-tree Super (HOTS) methodology for feature importance visualization.
result HOTS effectively resolves feature importance and directionality in high-dimensional neuroscientific data.
Researchers use Gaussian Process Regression to improve accuracy of a low-cost hot-wire anemometer.
problem Improving accuracy of low-cost hot-wire anemometers in varying temperatures.
method Probabilistic calibration using Gaussian Process Regression.
result The method provides good performance in estimating actual wind speeds, including uncertainty.
Enhances GBDT robustness with one-hot encoding and regularization.
problem Low robustness of GBDT models against covariate perturbation.
method One-hot encoding to linear framework, risk decomposition, L1 or L2 regularization. result Regularization enhances GBDT robustness.
Proposes HOT method for robust multi-view learning.
problem Inability of traditional methods to handle unaligned and non-distributionally aligned views.
method Hierarchical optimal transport (HOT) method that penalizes sliced Wasserstein distances between different views.
result HOT method achieves robust performance on both synthetic and real-world tasks.
This paper deals with the problem of large-scale linear supervised learning in settings where a large number of continuous features are available. We propose to combine the well-known trick of one-hot encoding of continuous features with a new penalization called \emph{binarsity}. In each group of binary features comin…
Method detects interactions for better CTR prediction.
problem Predicting click-through rate with high-dimensional categorical features and time-varying interactions.
method Online Random Intersection Chains (ORIC) for detecting informative interactions.
result ORIC detects high-interpretability interactions that improve CTR prediction.
Researchers prove hot spots conjecture for Gaussian spaces.
problem Hot spots conjecture for Gaussian domains.
method Variational principle for Hodge Laplacian on weighted manifolds and Hodge decomposition.
result First nontrivial eigenfunction extrema are on the boundary for specified domains.
SAMM monitors model drift in data streams without labels.
problem Detecting concept drift in unsupervised data streams.
method Time and space efficient unsupervised streaming algorithm for drift detection; generates explanations for drift.
result SAMM detects useful anomalous events for fraud detection.
We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variat…
Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real data often encounters an issue of imbalanced data distribution which will lead t…
New theory shows how learning algorithms can create a bias towards negative outcomes.
problem Negativity bias in adaptive learning algorithms.
method Generalization of the Hot Stove Effect to settings with negative estimates leading to smaller sample sizes.
result Negativity bias persists even when negative estimates do not lead to avoidance.
Quasi-orthonormal encoding reduces high dimensionality for categorical data.
problem High dimensionality and low sample size issues in categorical data encoding.
method Quasi-orthonormal encoding (QOE) for categorical data.
result QOE reduces dimensionality and improves machine learning performance.
The paper analyzes consistency of graph-based semi-supervised learning methods for binary and multi-class classification.
problem Consistency of semi-supervised learning algorithms on graphs with noisy labels and well-clustered unlabelled data.
method The study examines graph-based probit and one-hot encoding methods for binary and multi-class classification, analyzing the consistency of optimization-based techniques.
result The analysis reveals insights into the rational function choice for optimization, improving the consistency of semi-supervised learning algorithms.
GANs predict injection molding parts geometry from hot parts images.
problem Predicting final part geometry from early-stage measurements in injection molding.
method Used pix2pix GAN architecture to translate thermographic images to geometry.
result DMD analysis of GAN predictions reveals geometrical parameters.
For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. "Dirty" non-curated data gives rise to categorical variables with a very high cardinality but redundancy: several categories reflect the same ent…
The paper explores efficient ways to represent categorical data.
problem Wasteful one-hot encoding of categorical variables.
method Investigates alternative, lower-dimensional real-valued representations.
result Proposed methods retain all predictive information without one-hot encoding.
Predictive policing models can be biased by differential crime reporting rates.
problem Bias in predictive policing models due to differential crime reporting.
method Simulation based on Bogotá, Colombia's victimization and crime reporting data.
result Differential crime reporting rates can lead to misallocation of police patrols.
Study of 13,456 hot stellar systems reveals multi-layered grouping.
problem Understanding physical and evolutionary properties of Hot Stellar Systems.
method Used stellar mass, effective radius, and mass-to-luminosity ratio to group HSS into eight homogeneous ellipsoidal groups, then merged them through a multi-phased syncytial algorithm.
result Identified two complex-structured groups of HSS, one older and smaller, the other brighter and younger.
A fast graph embedding method for large graphs.
problem Efficiently embedding large graphs for various applications.
method One-hot graph encoder embedding with linear complexity.
result Graph encoder embedding is approximately normally distributed and converges to its mean.
Enhances graph neural networks with structural message-passing for better generalization.
problem Limited representation power and inability to learn basic graph topological properties.
method Proposes a framework that includes a one-hot encoding of nodes and parametrized message and update functions ensuring permutation equivariance.
result Achieves state-of-the-art results on molecular graph regression on the ZINC dataset.
Community detection was a hot topic on network analysis, where the main aim is to perform unsupervised learning or clustering in networks. Recently, semi-supervised learning has received increasing attention among researchers. In this paper, we propose a new algorithm, called weighted inverse Laplacian (WIL), for predi…
Study heat profiles and eigenfunctions using Brownian motion.
problem Investigate heat profiles and eigenfunctions of Laplace equations.
method Probabilistic tools based on Brownian motion and Feynman-Kac formulae.
result Supremum norm bounds for ground state Dirichlet eigenfunctions and comparison of maximum temperatures.
Paper introduces MLRH, a probabilistic model for multilabel classification.
problem Multilabel classification challenges in various domains.
method Introduces hidden variables to relax one-hot-encoding in logistic regression.
result Probabilistic model achieves competitive performance compared to other algorithms.
New methods encode high-cardinality string variables efficiently.
problem Efficient encoding of high-cardinality string categorical variables.
method Two approaches: Gamma-Poisson matrix factorization and min-hash encoder.
result Improves supervised learning with high-cardinality categorical variables.
Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism…
One of the ubiquitous representation of long DNA sequence is dividing it into shorter k-mer components. Unfortunately, the straightforward vector encoding of k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse yet, the distance between any pair of one-hot vectors is equidistant. This is partic…
Paper tackles noisy labels in deep learning networks.
problem Learning with noisy labels in deep neural networks.
method Sparse regularization strategy to approximate one-hot constraint.
result Improves performance of commonly-used loss functions in noisy labels and class imbalance.
Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning and a hot topic of current research. Some examples include data simulators that are widely used in engineering and scientific research, genera…
Develops comparison methods for semilinear elliptic problems on Riemannian manifolds with Ricci lower bound.
problem Analyzing positive solutions of semilinear Dirichlet problems on Riemannian manifolds with Ricci lower bound.
method Sharp pointwise gradient comparison method, derived from admissibility and structural conditions on f.
result Explicit isoperimetric-type inequality and quantitative hot-spot localization estimate.
New model identifies anticyclonic patterns causing drought and heat.
problem Identifying atmospheric drivers of drought and heat.
method Smoothed convolutional neural network classifier for anticyclonic circulations.
result Helps identify important drivers of hot and dry extremes in climate simulations.
A new sampler for FLMs improves token-level decoding controls.
problem Sampling from FLMs using standard methods collapses marginals and produces invalid sequences.
method Samples clean one-hot endpoints from FLM token marginals and uses Ornstein-Uhlenbeck bridges conditioned on these endpoints.
result The method preserves token-wise posterior-predictive marginals and improves quality-diversity tradeoff.
This paper proposes a method to reduce complexity in GLMs with categorical predictors.
problem Wasteful, hard-to-interpret, and prone to overfitting of traditional one-hot encoding for high-cardinality categorical predictors.
method Clustering categories of categorical predictors through a numerical method that preserves or improves accuracy while reducing the number of coefficients.
result Clustering categories of categorical predictors reduces complexity substantially without harming accuracy.
Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a "one-hot" encoding of the discrete symbols. Despite its simplicity, such approach yields the number of parameters that grows linearly with the vocabulary s…
Gradient flow in a potential energy (or Euclidean action) landscape provides a natural set of paths connecting different saddle points. We apply this method to General Relativity, where gradient flow is Ricci flow, and focus on the example of 4-dimensional Euclidean gravity with boundary S^1 x S^2, representing the can…
BUDS balances privacy and utility by shuffling data, achieving strong privacy with minimal loss.
problem Balancing privacy and utility in crowd-sourced statistical databases.
method One-hot encoding, iterative shuffling, loss estimation, risk minimization.
result Achieves ε=0.02 for privacy, maintaining a privacy bound of ε=ln[t/((n1−1)S)]. TabSODA improves imputation of surveys with skips and ordinal data.
problem Handling structural skips and ordinal responses in survey data.
method TabSODA uses an Elucidated Diffusion Model with skip pattern detection and ordinal awareness.
result TabSODA reduces ordinal missing-at-random (MACE) by up to 23.7% and improves categorical accuracy by up to 9%.
New indicator detects financial strain through smart meter data.
problem Fuel poverty in households, affecting millions.
method Smart meters and machine learning for behavior measurement.
result Early detection of financial strain in households.
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.
Embedding methods such as word embedding have become pillars for many applications containing discrete structures. Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying linear transformation based on "one-hot" encoding of the discrete symbols. …
Improved DLG extracts accurate labels from gradients, overcoming DLG's convergence issues.
problem Private training data leakage from shared gradients in distributed learning systems.
method Proposes iDLG, a simple approach to synthesize accurate labels from gradients.
result iDLG reliably extracts ground-truth labels from gradients, unlike DLG.