The paper discovers latent variables from dependency patterns in data.
problem Finding latent variables that explain unmeasured dependencies in data.
method Systematic search for latent variables using dependency patterns in data.
result Latent variables can be systematically discovered to explain data dependencies.
We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in …
An empirical study of joint bivariate probability distribution of two consecutive price increments for a set of stocks at time scales ranging from one minute to thirty minutes reveals asymmetric structures with respect to the axes y=0, y=x, x=0 and y=-x. All four asymmetry patterns remarkably resemble a four-blade mill…
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
Market Mill is a complex dependence pattern leading to nonlinear correlations and predictability in intraday dynamics of stock prices. The present paper puts together previous efforts to build a dynamical model reflecting the market mill asymmetries. We show that certain properties of the conditional dynamics at a sing…
Flexible Cox model for time-dependent covariates with complex sparsity patterns.
problem Lack of flexibility in enforcing specific sparsity patterns in time-dependent Cox models.
method Proposes a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.
result Achieves accurate estimation with low false alarm rates for complex covariate structures.
Modeling and estimating dynamic graphs from binary pattern sequences.
problem Extracting dominant correlation structures from time-dependent binary patterns.
method State-space model of an Ising-type network composed of multiple undirected graphs, sequential Bayes algorithm.
result The method outperforms traditional methods in uncovering overlapping graphs and estimating dynamics of weights.
A new method for handling missing values in data.
problem Handling missing values in machine learning models.
method Sharing pattern submodels with sparsity-inducing regularization.
result Sharing pattern submodels provide robust predictions and maintain/improve pattern submodel performance.
RBM models reveal how hidden unit tail behavior affects pattern reconstruction.
problem Understanding how the tail behavior of hidden units in RBMs influences pattern reconstruction.
method Identified an effective energy function for RBMs and studied its local minima.
result The ability to reconstruct patterns depends on the tail behavior of the hidden unit prior distribution.
Methodology to measure non-linear correlations using copulas and clustering.
problem Measuring pairwise correlations between variables in datasets.
method Copulas for encoding dependence, optimal transport for geometry, clustering for summarizing patterns.
result Novel dependence coefficient parameterized by clusters centers.
Detects dependencies between high-dimensional data and outcomes.
problem Analyzing educational data with high-dimensional student skills.
method n-TARP clustering to quantify and validate dependencies.
result Valid dependencies between student skills and course grades observed.
New algorithms for efficient learning with long-term rewards in contextual bandits.
problem Efficient learning with long-term rewards in contextual bandits.
method Proposes new algorithms leveraging sparsity to discover dependence patterns and arm parameters.
result Regret upper bounds for data-poor and data-rich regimes, showing improved sample complexity.
New method recovers block-sparse signals with common sparsity patterns.
problem Recovering block-sparse signals with common sparsity patterns in MMV.
method Pattern-coupled hierarchical Gaussian prior model with EM framework.
result Proposed method automatically captures block sparse structure.
Multi-Output Dependence (MOD) learning is a generalization of standard classification problems that allows for multiple outputs that are dependent on each other. A primary issue that arises in the context of MOD learning is that for any given input pattern there can be multiple correct output patterns. This changes the…
Stacked LSTM networks improve traffic volume forecasting.
problem Accurate traffic volume prediction for better planning.
method Applying stacked Long Short-Term Memory (LSTM) networks for time series forecasting.
result Stacked LSTM networks enhance the accuracy of traffic volume predictions.
We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a condition on existence of novel-words that are unique to each topic. We present a suite of highly efficient algorithms based…
ReNN integrates domain rules into neural networks for better interpretability and accuracy.
problem Lack of interpretability and need for large datasets in neural networks.
method ReNN combines local pattern detection with rule-based global synthesis, using a two-stage optimization strategy.
result ReNN improves inference accuracy and interpretability by incorporating domain rules.
A new method detects and displays pairwise dependence between variates.
problem Detecting and visualizing dependence between variates of different types.
method Recursive random binning with approximations to Pearson's statistic.
result The method is well-calibrated and powerful against common test alternatives.
Algorithm detects hidden spike-patterns in neural networks with one-shot learning.
problem Detecting hidden spike-patterns in high activity neural networks.
method Constructive algorithm using spike-timing-dependent plasticity (STDP) and lateral inhibition.
result Successful one-shot detection of new spike-patterns after long intervals.
The analysis of observed conditional distributions of both lagged and simultaneous intraday price increments of a basket of stocks reveals phenomena of dependence - induced volatility smile and kurtosis reduction. A model based on multivariate t-Student distribution shows that the observed effects are caused by colelct…
Fairness in Naive Bayes classifiers by identifying and eliminating discrimination patterns.
problem Ensuring fairness in machine learning models that use partial observations.
method Discover and eliminate discrimination patterns in naive Bayes classifiers through iterative learning.
result An algorithm that learns fair naive Bayes classifiers by removing discrimination patterns.
Paper mines rank data patterns from rankings.
problem Mining rank data patterns from rankings.
method Proposes algorithms for frequent rankings and dependencies.
result Experimental validation of algorithms on synthetic and real data.
Study reveals LLM personas have two distinct components: frame-robust aggregated traits and frame-dependent geometric features.
problem Evaluation of LLM personas via psychometric questionnaires discards within-instance correlation structure.
method Constructed within-instance correlation matrices from IPIP-50 responses and analyzed geometry on SPD manifolds under manipulated question orderings.
result Persona expression comprises two dissociable components: aggregated features (Big Five scores) and geometric features (SPD manifold).
Origami patterns are classified based on their symmetry groups.
problem Classifying the symmetry groups of origami patterns.
method Iteratively compute intersection points and lines to construct mathematical origami sets, then classify them based on wallpaper groups.
result Determine which wallpaper groups can be constructed from given origami patterns.
This paper explores non-periodic folding of Spidron units, revealing nonlinear dynamics.
problem Understanding the kinematics and nonlinear phenomena of Spidron units.
method Analysis of single unit cell kinematics and recursive construction of multiple cells.
result Non-periodic folding restricts isotropic folding as the number of unit cells increases.
Paper proposes a new HMM approach for better action recognition.
problem Capturing complex temporal dependency patterns in skeleton-based actions.
method Introduces a hierarchical HMM with a latent variable layer for dynamic inference.
result Proposed approach effectively models complex sequential data and handles missing values.
Proposes a new voice conversion model that preserves pitch patterns.
problem Preserving pitch patterns while changing speaker identity.
method Variational-autoencoder-based model with an auxiliary network.
result Ensures the conversion result correctly reflects specified F0/timbre information.
Proposes a progressive label correction method for feature-dependent label noise.
problem Real-world large-scale datasets often suffer from heterogeneous, feature-dependent label noise.
method A progressive label correction algorithm that iteratively refines the model.
result A classifier trained with this strategy converges to be consistent with the Bayes classifier for various noise patterns.
LOUPE optimizes MRI sub-sampling patterns using machine learning.
problem Optimizing sub-sampling patterns for MRI scans to improve reconstruction accuracy.
method End-to-end learning strategy combining sub-sampling pattern optimization and reconstruction model training.
result LOUPE yields more accurate reconstructions compared to standard under-sampling schemes.
Uniform diameter bound for reflection group disk patterns.
problem Uniform bounded diameter conjecture for reflection groups.
method Skinning map analysis and discrete extremal width on Coxeter graph.
result Diameter of skinning image is bounded by a constant.
CACTI improves tabular data imputation by leveraging missingness patterns and contextual information.
problem Tabular data imputation with improved accuracy and robustness.
method Masked autoencoding approach with median truncated copy masking and contextual information.
result Average R2 gain of 7.8% over the next best method across various datasets and missingness conditions. cPCA discovers unique patterns in multiple datasets.
problem Discovering unique patterns in datasets with multiple sources.
method Generalizes PCA for multiple datasets, identifying dataset-specific structures.
result cPCA identifies patterns missed by standard PCA.
Proposes RT decomposition for better multi-relational link prediction.
problem Improving multi-relational link prediction in knowledge graphs.
method Relational Tucker3 (RT) decomposition, decouples entity and relation embeddings, allows parameter sharing, and learns sparsity patterns.
result RT decomposition can outperform existing sparse models in multi-relational link prediction.
Mixed labyrinth fractals can have finite or infinite arc lengths.
problem Characterizing the length of arcs in mixed labyrinth fractals.
method Analyzing sequences of labyrinth patterns to determine arc lengths.
result Arc lengths can be finite or infinite depending on pattern choice.
CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.
problem Challenges in modeling cumulative effects of time-dependent exposures on survival outcomes.
method CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data.
result CENNSurv reveals multi-year lagged and short-term behavioral shifts in survival outcomes.
The paper uses pattern similarity-based methods for mid-term electricity demand forecasting.
problem Forecasting monthly electricity demand with seasonal patterns.
method Pattern similarity-based machine learning models (nearest neighbor, fuzzy neighborhood, kernel regression, GRNN).
result The proposed models outperform classical and state-of-the-art models in accuracy and simplicity.
Paper uses STPN for energy/power prediction in complex systems.
problem Predicting energy in complex dynamical systems.
method Spatiotemporal pattern network (STPN) framework with mutual information metric.
result Improved energy prediction accuracy in wind and residential energy contexts.
A number of recent emerging applications call for studying data streams, potentially infinite flows of information updated in real-time. When multiple co-evolving data streams are observed, an important task is to determine how these streams depend on each other, accounting for dynamic dependence patterns without impos…
Study reveals lead-lag patterns between onshore and offshore RMB exchange rates.
problem Understanding the lead-lag relationship between onshore and offshore RMB exchange rates.
method Employed the thermal optimal path method to analyze daily and minute-scale data.
result Lead-lag patterns are influenced by market factors and US dollar appreciation.
An important problem in multi-label classification is to capture label patterns or underlying structures that have an impact on such patterns. This paper addresses one such problem, namely how to exploit hierarchical structures over labels. We present a novel method to learn vector representations of a label space give…
Khovanov and chromatic homologies show similar patterns, improving chromatic bounds and computing Jones polynomial.
problem Understanding similarities between Khovanov and chromatic homologies.
method Analyzing isomorphism and improving bounds using explicit formulas.
result Improved bounds for chromatic homology and explicit formula for chromatic homology rank.
Investment patterns in Eurozone linked to economic indicators, revealing network interconnectedness.
problem Understanding the relationship between foreign portfolio investment and economic indicators in the Eurozone network.
method Analysis of strongly connected investment network of Eurozone and its major trading partners, using centrality measures and network visualization techniques.
result Strong correlation between investment patterns and economic indicators within the Eurozone network, with varying centrality measures among network members.
Recent studies have revealed a number of striking dependence patterns in high frequency stock price dynamics characterizing probabilistic interrelation between two consequent price increments x (push) and y (response) as described by the bivariate probability distribution P(x,y) [1,2,3,4]. There are two properties, the…
Analyzes attention structure in GPT-2 model, revealing specific patterns.
problem Understanding attention mechanisms in Transformer models.
method Visualized and analyzed attention for GPT-2 model, examining interactions over a corpus.
result Attention targets different parts of speech at varying depths, aligning with dependency relations in middle layers.
Study quantifies pedestrian traffic patterns in NYC.
problem Understanding pedestrian traffic dynamics in urban areas.
method Publicly available traffic camera data in NYC, time series analysis.
result Pedestrian traffic exhibits diurnal patterns with weekday peaks and no peak on weekends.
EvoRate metric assesses learnability of sequential data by measuring predictive information.
problem Model misspecification due to misinterpreting patterns in sequential data.
method Predictive information framework based on mutual information between past and future.
result Temporal patterns fundamentally constrain learnability; optimal predictors cannot outperform intrinsic information limit.
New link detection results using closures of 3-braids.
problem Link detection using homology theories.
method Closure operations on 3-braids and homology theories.
result Detection of specific links using link Floer homology, Khovanov homology, and annular Khovanov homology.
Pattern recognition in neuroimaging distinguishes between two types of models: encoding- and decoding models. This distinction is based on the insight that brain state features, that are found to be relevant in an experimental paradigm, carry a different meaning in encoding- than in decoding models. In this paper, we a…