The paper integrates statistical significance and discriminative power in pattern discovery.
problem Discovering actionable patterns that meet rigorous statistical significance and discriminative power criteria.
method Integrates statistical significance and discriminative power criteria into state-of-the-art algorithms.
result Improves discriminative power and statistical significance of discovered patterns without quality deterioration.
The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. However, some of those dependencies are generated by causal structures involving varia…
This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact disc…
Identifying important components or factors in large amounts of noisy data is a key problem in machine learning and data mining. Motivated by a pattern decomposition problem in materials discovery, aimed at discovering new materials for renewable energy, e.g. for fuel and solar cells, we introduce CombiFD, a framework …
This paper formalizes a latent variable inference problem we call {\em supervised pattern discovery}, the goal of which is to find sets of observations that belong to a single ``pattern.'' We discuss two versions of the problem and prove uniform risk bounds for both. In the first version, collections of patterns can be…
FSR efficiently discovers significant patterns with few resampled datasets.
problem Mining significant patterns in transactional data, especially subgroups.
method FSR uses resampling to bound the supremum deviation of quality statistics, providing rigorous guarantees on false discoveries.
result FSR effectively discovers significant subgroups with a small number of resampled datasets.
This paper introduces a new system for discovering patterns in morphogenetic systems using modular architecture and unsupervised learning.
problem Discovering novel patterns in morphogenetic systems is challenging and often relies on manual tuning.
method Introduces a hierarchical, modular architecture for unsupervised learning of diverse representations combined with goal exploration algorithms.
result The new system efficiently adapts diversity search towards user preferences with minimal feedback.
Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to multidimensional data. CDL has proven useful for image denoising or inpainting, as well as for pattern discovery on multivariate signals. As estimated patterns can be positioned anywhere in signals or images, optimization techniques face…
Boolean matrix factorization (BMF) is a popular and powerful technique for inferring knowledge from data. The mining result is the Boolean product of two matrices, approximating the input dataset. The Boolean product is a disjunction of rank-1 binary matrices, each describing a feature-relation, called pattern, for a g…
TSML tackles anomaly detection and pattern discovery in industrial time series data.
problem Extracting and exploiting information from large industrial data to reduce downtimes and manufacturing errors.
method TSML uses a pipeline of lightweight filters to process industrial time series data in parallel.
result TSML effectively detects anomalies and discovers patterns in industrial time series data.
A novel framework infers causal direction from symbolic sequences using pattern entropy.
problem Challenges in discovering causal direction from temporal symbolic data.
method Dictionary Based Pattern Entropy (DPE) framework integrating AIT and Shannon Information Theory. result Minimizing pattern level uncertainty yields a robust framework for causal discovery.
In many complex dynamical systems, artificial or natural, one can observe self-organization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), have been widely used as abstract models enabling the study of various aspects of self-organization and morphogenesis, such as the emergence …
iPrompt uses LLMs to generate natural-language explanations of data patterns.
problem Finding and explaining patterns in data using natural language.
method Interpretable autoprompting (iPrompt) that generates natural-language explanations based on LLMs.
result iPrompt can accurately find and explain data patterns, improving upon human-written prompts.
Framework explains deep learning candlestick recognition.
problem Deep learning models explain candlestick patterns in a black box.
method Local search adversarial attacks to explain model reasoning.
result Model perceives candlestick patterns similarly to human traders.
Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses t…
Gaussian processes are typically used for smoothing and interpolation on small datasets. We introduce a new Bayesian nonparametric framework -- GPatt -- enabling automatic pattern extrapolation with Gaussian processes on large multidimensional datasets. GPatt unifies and extends highly expressive kernels and fast exact…
Interpretable framework evaluates structure learning methods for causal discovery from observational data.
problem Evaluation of structure learning methods under assumption violations in causal discovery.
method Six-dimensional evaluation metric (DOS) tailored for causal discovery.
result Amortized causal discovery delivers results with high proximity to the optimal solution.
AFTNet uses a network-constrained Weibull model for biomarker discovery.
problem Discovering biomarkers from survival data with correlated predictors.
method Survival analysis method based on Weibull AFT model, incorporating network constraints and penalized likelihood for variable selection.
result Theoretical consistency and efficient algorithm for AFTNet estimator validated on synthetic and real data.
The understanding of geographical reality is a process of data representation and pattern discovery. Former studies mainly adopted continuous-field models to represent spatial variables and to investigate the underlying spatial continuity/heterogeneity in the regular spatial domain. In this article, we introduce a more…
In this paper, we investigate the multi-variate sequence classification problem from a multi-instance learning perspective. Real-world sequential data commonly show discriminative patterns only at specific time periods. For instance, we can identify a cropland during its growing season, but it looks similar to a barren…
Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral dens…
SigTime learns interpretable signatures from time series data.
problem Discovering meaningful patterns in time series data with high complexity and limited interpretability.
method Jointly trains two Transformer models using shapelet-based and feature engineering representations.
result Learned shapelets serve as interpretable signatures for time series classification.
Time Series Motif Discovery (TSMD) is defined as searching for patterns that are previously unknown and appear with a given frequency in time series. Another problem strongly related with TSMD is Word Segmentation. This problem has received much attention from the community that studies early language acquisition in ba…
New scoring rule predicts causal relations from data with selection bias.
problem Discovering causal relations from independence constraints under selection bias and confounding.
method Local Y-Structure patterns and a scoring rule for Y-Structures.
result Y-Structure scoring rule successfully predicts causal relations in real-world data.
Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.
problem Causal inference in non-stationary, autocorrelated time series data.
method Decomposes time series into trend, seasonal, and residual components; performs component-specific causal analysis.
result Framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, especially under strong non-stationarity and temporal autocorrelation.
The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach t…
TimeGraph creates synthetic datasets for robust time-series causal discovery.
problem Lack of reliable synthetic benchmark datasets for robust time-series causal discovery.
method Developed comprehensive synthetic datasets with temporal properties, including trends, seasonality, and noise.
result Demonstrated significant variations in algorithm performance under realistic temporal conditions.
Simulated DAGs can mislead structure learning algorithms due to variance patterns.
problem Structure learning algorithms can be misled by variance patterns in simulated DAG models.
method Introduced varsortability as a measure of agreement between marginal variance order and causal order.
result Performance of structure learning algorithms can be explained by high varsortability, but this does not generalize to real-world data.
CRE discovers interpretable subgroups with heterogeneous treatment effects.
problem Identifying subgroups with notable treatment effect heterogeneity.
method Causal Rule Ensemble (CRE) using an ensemble-of-trees approach.
result CRE offers interpretable decision rules and high stability in subgroup discovery.
Randomization helps verify if data mining results are due to inherent patterns.
problem Verify if data mining results are due to inherent patterns or coincidental findings.
method Metropolis sampling based on local swaps to randomize data while preserving discovered patterns.
result Randomized data often reveals that clustering results imply frequent pattern discovery.
Bayesian model enhances phenotype discovery in asthma EHRs.
problem Lack of interpretability in unsupervised learning phenotyping of EHR data.
method Operationalized a Bayesian latent class framework with clinical knowledge priors.
result Identified an asthma sub-phenotype with elevated eosinophil levels and allergy markers.
FoundCause: Causal Discovery with Latent Confounders from Observational Data
problem Causal discovery from observational data
method FoundCause, an amortized causal discovery model trained on synthetic data
result FoundCause outperforms classical and amortized methods on real-world datasets
iKF method uncovers complex variable interactions for scientific discovery.
problem Limited interpretability of existing models in decision-making applications.
method Iterative Kings' Forests (iKF) method to uncover multi-order interactions.
result iKF provides strong interpretive power for explainable modeling.
Paper studies binary random projections with controllable sparsity patterns for computational and accuracy advantages.
problem Improving computational efficiency and accuracy in random projections.
method Proposes two sparse binary projection models with controllable sparsity patterns.
result Significant computational advantages and improved accuracies in empirical evaluations.
Robust subgroup discovery finds non-redundant, statistically significant subgroups.
problem Finding interpretable, robust subgroups from data.
method Formulated subgroup lists for univariate and multivariate targets, used MDL principle and greedy heuristic SSD++.
result SSD++ outperforms previous methods in quality and size of subgroup lists.
This thesis relaxes assumptions for causal discovery, making methods applicable to more complex systems.
problem Learning causal structures from observational data with latent variables.
method Alternative definition of k-Triangle Faithfulness for non-Gaussian distributions and uniform consistency proof.
result Uniform consistency of causal discovery algorithm under modified faithfulness assumption.
In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible. The availability of such rich and detailed physiological measurements calls for the development of advanced data analysis tools, as commonly used techniques do …
Diamond method controls FDR for trustworthy feature interaction discovery in ML models.
problem Limited interpretability of ML models due to black box nature.
method Diamond method integrates model-X knockoffs framework to control FDR for non-additive interactions.
result Diamond method ensures accurate discovery of feature interactions with FDR control.
Deep learning uncovers patterns between knot types.
problem Discovering connections between combinatorial and hyperbolic knot invariants.
method Statistical approach using linear regression and deep learning.
result Revealed empirical connections between knot types.
As humans we are driven by a strong desire for seeking novelty in our world. Also upon observing a novel pattern we are capable of refining our understanding of the world based on the new information---humans can discover their world. The outstanding ability of the human mind for discovery has led to many breakthroughs…
Novel approach models life events using causal discovery and survival analysis.
problem Modeling life event choices and occurrence from a probabilistic perspective.
method Bi-level problem formulation: causal discovery for life events graph, survival analysis for time-to-event modeling.
result Identification of causal relationships and factors influencing transition rates between life events.
New model predicts energy prices under different scenarios.
problem Complex causal relationships in energy markets with continuous regime changes.
method Augmented Time Series Structural Causal Models (ATSCM) integrating neural causal discovery.
result Enables novel counterfactual queries in energy markets.
Method finds interestingly dense subgroup connections in graphs.
problem Understanding patterns in graph connectivity based on node attributes.
method Information-theoretic definition of interestingness for subgroup connections.
result Identifies pairs of node subgroups with high or low edge density.
Study examines lead-lag relationships between VIX and VIX futures markets over time.
problem Understanding dynamic interaction patterns between VIX and VIX futures markets.
method Utilized the symmetric thermal optimal path (TOPS) method to analyze time-dependent lead-lag relationships.
result Observed alternate lead-lag relationship instead of dominance between VIX and VIX futures markets.
AI aids in mathematics research and problem-solving.
problem Complex mathematical problems and discoveries.
method Explains AI principles and diverse applications in math.
result AI assists in discovering patterns, proving theorems, and challenging conjectures.
Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especi…
New knot polynomials reveal patterns and mutations.
problem Understanding the structure and behavior of knot polynomials.
method Using a specific multivariable polynomial invariant derived from Nichols algebras.
result Emerging patterns in knot polynomials, including genus bounds and unexpected mutations.
Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.
problem Challenges in asset management due to heterogeneous deterioration rates in pump equipment.
method Bayesian hierarchical hazard modeling with causal discovery, GPU-accelerated No-U-Turn Sampling (NUTS), and DirectLiNGAM.
result Identified striking heterogeneity in deterioration rates, with negative effects 400 times larger than positive effects.