New concept of sparse regular variation for better understanding of extreme events.
problem Characterizing the dependence structure of extreme events in multivariate settings.
method Introducing sparse regular variation based on Euclidean projection onto the simplex.
result Sparse regular variation and regular variation are equivalent under mild assumptions.
LDA-Link links sequences with sparse or no common events across data sets.
problem Linking sequences with no common events in different data sets.
method Formalizes sequence linkage problem, uses LDA-Link model based on Split-Document model.
result Robust sequence linkage even when sequences have sparse or no common events.
A new method for embedding sparse high-order interactions.
problem Learning embeddings from sparse high-order interaction events.
method Hybridizing sparse hypergraph and matrix Gaussian processes.
result Strong asymptotic bounds on sparsity ratio.
Bayesian model improves categorization of explosions from sparse data.
problem Challenges in categorizing explosions from limited data.
method Bayesian update to Event Categorization Matrix model with Bayesian Decision Theory.
result Consistent gains in overall accuracy and lower false negative rates.
A new model family of zero-inflated Gaussian processes improves prediction and interpretability of rare event data.
problem Poor performance of conventional machine learning on zero-inflated datasets.
method Sparse kernels and latent probit Gaussian processes to zero out kernel rows and columns.
result Improves prediction of zero-inflated data and interpretability of latent mixing models.
The paper proposes a method to identify subgroups with different treatment effects in time-to-event data.
problem Identifying subgroups with differential treatment effects in time-to-event data.
method A mixture model with structured sparsity regularization and novel inference procedure.
result The method effectively recovers sparse phenotypes across real-world clinical studies.
Develops CSI for predicting disease progression from sparse data.
problem Lack of frequent clinical metrics for disease progression.
method Machine learning framework (CSI) for analyzing disease progression from sparse observations.
result CSI effectively predicts disease progression from sparse data.
SparseProp speeds up SNN simulations and training by four orders of magnitude.
problem Efficiently simulating and training large spiking neural networks.
method Event-based algorithm that reduces computational cost from O(N) to O(log(N)) per spike.
result Numerically exact simulations of large spiking networks and efficient training using backpropagation.
Method predicts crime hotspots with high resolution.
problem Forecasting sparse spatiotemporal events like crime.
method Combines RKHS methods with autoregressive smoothing kernels.
result Significantly outperforms baseline models for sparse events.
Study financial contagion and risk in sparse networks with directed edges.
problem Analyzing systemic risk in sparse financial networks with balance-sheet interactions.
method Linear fraction of institutions with zero out-degree, sender-truncated subgraph G_sh, adversarial and random systemic events, explicit fan-in accumulation bound.
result Maximal forward reachability in G_sh is O(log n) with high probability in the subcritical regime, and multi-hit defaults are negligible in the supercritical regime.
A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve…
New method infers missing categorical data from Web markup.
problem Missing information in Web markup nodes.
method Supervised approach for inferring categorical properties.
result Significantly outperforms existing baselines with 79% and 83% F1 score.
CNN improves neutrino event reconstruction in IceCube DeepCore.
problem Difficulties in distinguishing muon neutrinos and reconstructing inelasticity at GeV scale energies.
method 2D Convolutional Neural Network exploiting time and depth translational symmetry.
result CNN model outperforms conventional methods for flavor identification and inelasticity reconstruction.
Flexible joint model predicts events from noisy multivariate data.
problem Predicting events from noisy multivariate time series data with missing values.
method Sparse multiple-output Gaussian processes for scalable joint modeling, optimal event prediction policy.
result Significantly outperforms state-of-the-art techniques in event prediction.
EvAn detects anomalies in event-based camera data with reduced complexity.
problem Anomaly detection in event-based camera data.
method Dual discriminator cGAN on event data with learned representation.
result Reduction in computational complexity compared to state-of-the-art methods.
This paper tackles efficient learning for factorial marked temporal point processes.
problem Efficient learning for factorial marked temporal point processes.
method Decoupled learning method with two procedures: ADM-M and Fast ISTA, and a reformulated Logistic Regression model.
result Empirical results show the efficiency of the decoupled and reformulated method.
New algorithm speeds up sampling for complex statistical models.
problem Sampling parameters of high-dimensional CTMCs is challenging.
method Developed a local version of the Bouncy Particle Sampler (BPS) with exact event times.
result Algorithm achieves favorable computational efficiency for real-data scenarios.
Neural networks improve clinical note representations for predictive modeling.
problem Challenges in using clinical notes for machine learning due to high dimensionality, sparsity, and scarcity of labeled data.
method Used neural networks and transfer learning to learn representations of clinical notes.
result Neural network representations significantly outperformed baseline representations in predictive models.
News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep learning with neural networks to model and link text to a scarce set …
Proposes a new model for time-to-event prediction with uncertainty quantification.
problem Lack of uncertainty in time-to-event predictions using recurrent neural networks.
method Deep Kernel Accelerated Failure Time models combining RNN and sparse Gaussian Process.
result Model delivers better uncertainty estimates compared to related methods.
Adaptive prediction timing improves healthcare outcomes by predicting patient events at the right frequency.
problem Inconsistent prediction granularity in healthcare models.
method Introduces a novel approach using Bayesian recurrent models and a new aggregation method to adapt prediction frequency based on uncertainty.
result Adaptive prediction timing leads to improved predictive performance, especially in the critical first 12 hours of patient stay.
Enhances survival analysis by separating population behavior from individual dynamics.
problem Improving the training and inference of survival analysis models for sparsely occurring events.
method Decouples survival analysis into an aggregated baseline hazard and independent survival scores.
result Achieves competitive performance and robust results without fine-tuning.
New method learns graphical models with latent variables for extreme events.
problem Learning graphical models with latent variables for multivariate extremes.
method Tractable convex program exttt{eglatent} for Hüsler-Reiss models.
result Consistently recovers conditional graph and latent variables.
Online algorithm detects community structure in dynamic event streams.
problem Community detection in networks with temporal event streams.
method Continuous-time point process latent network models with fast online variational inference.
result Online inference achieves comparable community recovery to non-online methods but with computational gains.
STanHop predicts multivariate time series with memory-enhanced capabilities.
problem Predicting multivariate time series with memory-enhanced capabilities.
method Sparse Tandem Hopfield Network (STanHop) with two external memory modules.
result STanHop outperforms dense Hopfield models in memory retrieval error.
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
problem Predicting future ticket prices from sparse and spatiotemporal data.
method Bi-level optimizing deep neural network with coarsening and refining layers, bi-level loss function.
result Our model outperforms other methods in real-world ticket price prediction.
Unsupervised learning filters tweets for emergency services during crises.
problem Challenges in filtering relevant information from social web data during disasters.
method Multi-task domain adversarial attention network for unsupervised domain adaptation.
result The multi-task model outperforms single task models in filtering relevant tweets.
Develops a new point process model for detecting neural spike sequences.
problem Detecting sparse sequences of neural spikes in high-dimensional spike trains.
method A point process model that represents sequence occurrences as marked events in continuous time, with learnable time warping parameters.
result Demonstrates improved detection and modeling of neural spike sequences.
MCLNN improves sound event recognition with fewer parameters.
problem Improving sound event recognition with deep neural networks.
method Developed MCLNN to enforce sparseness and frequency shift invariance.
result MCLNN achieved competitive performance with 12% fewer parameters.
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
New dataset and model improve real-time traffic accident prediction.
problem Reducing traffic accidents through better analysis and prediction.
method Deep neural network (DAP) with recurrent, fully connected, and embedding components.
result Significant improvements in predicting rare accident events.
Bayesian truth discovery uses social network info to improve reliability estimates.
problem Truth discovery from unreliable or biased agents with correlated biases in the same community.
method Laplace variational inference and stochastic variational inference for large networks.
result Our methods outperform other inference methods in sparse observation scenarios.
We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country i took action a toward country j at time t"---known as dyadic events---in order to form an…
Hybrid model improves geopolitical conflict forecasting.
problem Forecasting geopolitical events from sparse, bursty data.
method Sparse Temporal Fusion Transformer (TFT) + Variational Nearest Neighbor Gaussian Process (VNNGP).
result Consistently outperforms standalone TFT in long-range horizons.
Machine learning predicts Atlantic blocking using limited data.
problem Underestimation of blocking event duration in climate models.
method Transfer Learning and Explainable AI (SHAP analysis)
result High-pressure anomalies in specific regions contribute to blocking events.
Unsupervised methods have proven effective for discriminative tasks in a single-modality scenario. In this paper, we present a multimodal framework for learning sparse representations that can capture semantic correlation between modalities. The framework can model relationships at a higher level by forcing the shared …
Model financial default cascades on sparse graphs via hitting times.
problem Capturing systemic risk in large, sparsely-connected financial networks.
method Dynamic particle systems with hitting times and convergence theory.
result Characterization of default time distribution in tree-like networks.
Estimates changes in parameters from sparse binomial observations.
problem Sparse observations of binomial parameters over a large population.
method Two-step procedure: MLE for joint distribution, then for change distribution and magnitude.
result Achieves optimal error bounds for estimating change distribution and magnitude.
Study on detecting and estimating a rank-one tensor in noisy data.
problem Detecting and estimating a rank-one deformation in symmetric random Gaussian tensors.
method Established upper and lower bounds on critical signal-to-noise ratios for various priors.
result Upper and lower bounds match up to a 1+o(1) factor for large tensor order, and are asymptotically tight for sparse signals.
Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events---something not previously studied quantitatively---could yield important insights into the nature and intrinsic dynamics of seizures. A…
This work improves distribution recovery from sparse data using Random Forest implicit regularization.
problem Distribution recovery from limited statistics.
method Closed-form estimator for scaled beta distributions, using composite quantile and moment matching.
result Improved classification accuracy through closed-form distribution recovery and implicit regularization.
Enhances FAVAR models with autoencoder for better economic forecasting and interpretability.
problem Limitations of linear FAVAR models in forecasting and structural analysis.
method Introduces Grouped Sparse autoencoder with time-varying parameters.
result The Grouped Sparse autoencoder produces more interpretable factors and superior forecasting performance.
PhysicsFormer improves TSF models for GSWF with WEATHER-5K dataset.
problem Lack of comprehensive datasets for GSWF.
method PhysicsFormer combines dynamic core and Transformer, enforcing physical consistency.
result PhysicsFormer outperforms TSF models in operational forecasting.
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robus…
GAttNHP predicts future events in temporal knowledge graphs by encoding long-range dependencies and handling mutual excitation.
problem Forecasting future events in temporal knowledge graphs due to long-range dependencies, mutual excitation, and heavy-tailed inter-arrival times.
method GAttNHP uses a self-attention encoder, semantic soft-grouping, and NCQ regression to address these issues.
result GAttNHP improves entity and time prediction on six benchmark TKG datasets compared to state-of-the-art baselines.
New algorithm predicts spatio-temporal events with improved accuracy.
problem Non-stationary spatio-temporal prediction on dense and sparse sequences.
method Probabilistic approach using point processes and self-organizing decision trees.
result Significant performance improvements over baseline and state-of-the-art methods.
We tackle anomaly detection in sparse time series data.
problem Sparse time series with low signal-to-noise ratios and non-uniform performance.
method We introduce a novel generative procedure for benchmark datasets and demonstrate how anomaly score smoothing improves performance.
result Anomaly score smoothing consistently improves performance in low-count time series anomaly detection.
This study benchmarks AI agents for personalized retail promotions using simulations.
problem Optimizing coupon targeting for sparse customer purchase events.
method Comprehensive simulations of customer shopping behaviors; training RL agents on batch data.
result Contextual bandit and deep RL methods outperform static policies in sparse reward environments.