Model predicts treatment initiation from clinical data using patient-clinician relations.
problem Predicting treatment initiation from clinical time series data considering patient-clinician relations.
method Graph-Augmented Time-Sensitive Model using top eigenvectors of graph Laplacian.
result Relational similarity improves prediction over baselines, e.g., 5% improvement in AUPRC.
Proposes on-the-fly joint feature selection and classification for time-sensitive decisions.
problem Online feature selection and classification for time-sensitive decision making.
method Proposes a framework for joint feature selection and classification on-the-fly, optimizing feature usage and classification accuracy.
result Demonstrates superior performance over state-of-the-art methods on various datasets.
Time-aware fact-checking improves veracity predictions for time-sensitive claims.
problem Fact-checking decisions should consider temporal information of claims and evidence.
method Investigated four temporal ranking methods to optimize evidence ranking for fact-checking models.
result Time-aware evidence ranking surpasses relevance assumptions and improves veracity predictions for time-sensitive claims.
Paper proposes unsupervised learning for optimizing deep neural networks in time-sensitive applications.
problem Inaccurate optimization solutions from supervised learning for real-time applications.
method Proposes unsupervised deep learning to learn latent function with optimization constraints as supervision.
result DNN can optimize variable problems in ultra-reliable communications without supervision labels.
Enhances LightGCN for credit bond recommendations with dynamic node embeddings.
problem Challenges in static embeddings for rapidly evolving user interests in finance.
method Causal graph convolution for dynamic node embeddings over chronological user-item interactions.
result Significantly enhances LightGCN performance in financial product recommendations.
A simple GI loss improves temporal generalization without complex methods.
problem Temporal drift between train and test distributions in evolving data.
method Gradient Interpolation (GI) loss to regularize temporal complexity.
result GI loss outperforms complex methods on real-world datasets.
A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to the huge burst of help requests generated in a very short period. To improve the efficiency of the emergency response in t…
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treat…
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph…
Neural Jump ODEs improve online filtering and classification with robust performance.
problem Online filtering and classification in settings with irregular and partial observations.
method Modeling conditional expectation using Neural Jump ODEs, with theoretical convergence guarantees.
result Demonstrated superior performance over classical methods, especially in complex scenarios.
Improves text-to-speech speed by interleaving character reading and audio synthesis.
problem Latency in text-to-speech models limits their use in time-sensitive tasks.
method Reinforcement learning to train an agent to choose the order of character reading and audio synthesis.
result The proposed method successfully balances latency and audio quality.
Study uses DRL with Lagrangian relaxation to solve temporal control tasks with STL constraints.
problem Optimal control problems with temporal logic constraints.
method Extended CMDP formulation, Lagrangian relaxation, two-phase constrained DRL algorithm.
result Demonstrated learning performance of the proposed algorithm through simulations.
New framework estimates staged tree models using hierarchical clustering on the probability simplex.
problem Estimating staged tree models with context-specific dependencies.
method Hierarchical clustering on the probability simplex, using simplex-based divergences and linkage methods.
result Total Variation divergence with Ward.D2 linkage produces staged trees with better model fit, structure recovery, and computational efficiency.
Deep learning speeds up real-time emission monitoring.
problem Real-time greenhouse gas emission monitoring under transient conditions.
method Bayesian inference with deep learning surrogate of CFD outputs.
result Near-real-time predictions with orders-of-magnitude faster runtimes.
Scheduling the transmission of time-sensitive data to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users under a constraint on the average number of transmissions at the source node. After each transmission, the sourc…
ELM speeds up financial machine learning tasks.
problem Efficiently solving time-sensitive financial tasks with machine learning.
method Single-layer neural networks with random initialization and convex optimization.
result ELM achieves significant computational efficiency in financial applications.
Single-step samplers generate high-quality samples efficiently.
problem Sampling from unnormalized distributions is computationally expensive.
method Developed consistent diffusion samplers that generate samples in a single step.
result Single-step samplers produce high-fidelity samples with less than 1% of traditional samplers' evaluations.
The paper analyzes sensitivities of cash flows using PDEs and Hansen-Scheinkman decomposition.
problem Large-time sensitivities of cash flows in quantitative finance.
method PDE representation of pricing operator with Hansen-Scheinkman decomposition.
result Detailed convergence rates of sensitivities are provided.
Improved aggregation methods learn from all ICU events without preprocessing for better patient risk analysis.
problem Lack of efficient methods to dynamically assess patient status in ICU.
method Improved aggregation methods for a deep learning architecture that learns from all events without preprocessing.
result Models achieve strong performance (AUROC 0.87) in patient mortality classification.
Analog method solves portfolio optimization problems faster and more efficiently.
problem Accurate covariance matrix estimation and fast optimal portfolio selection for financial applications.
method Two-step process using equilibrium propagation and analog Hopfield networks.
result Fully analog pipeline calculates optimal portfolios in energy-efficient manner.
Lambda Learner improves model freshness in data streams.
problem Balancing model freshness and computational costs in data streams.
method Incremental updates in response to mini-batches from data streams.
result Lambda Learner outperforms offline models in time-sensitive updates.
Adaptive time decay functions improve financial product recommendation accuracy.
problem Inaccurate recommendations due to static historical data in finance.
method Time-dependent collaborative filtering with personalized decay functions.
result Significant improvements over state-of-the-art benchmarks in financial product recommendation.
Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. How…
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time…
Recent advances in adversarial Deep Learning (DL) have opened up a largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. With the wide-spread usage of DL in critical and time-sensitive applications, including unmanned vehicles, drones, and video surveillance systems, onli…
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classificati…
Human matting, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the matting problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. This user-in-the-loop nature makes th…
Sequential hypothesis testing is a desirable decision making strategy in any time sensitive scenario. Compared with fixed sample-size testing, sequential testing is capable of achieving identical probability of error requirements using less samples in average. For a binary detection problem, it is well known that for k…
The paper predicts when emails will be opened using survival analysis.
problem Predicting the time to open emails with varying user engagement levels.
method Survival analysis with Cox Proportional Hazards model and mixture model.
result The mixture model achieves the best accuracy in predicting email opening times.
Efficient framework for robust training of GNNs against adversarial attacks.
problem Adversarial attacks on graph neural networks leading to incorrect predictions.
method Greedy search algorithms and zeroth-order methods for efficient robust training.
result Significantly less computationally expensive and more robust than state-of-the-art methods.
New DL algorithm detects critical chest X-ray findings without manual annotations.
problem Lack of explainability and manual annotation costs for DL models in medical imaging.
method Multi-instance learning approach to jointly classify and localize critical findings in CXR.
result Competitive classification results on three CXR datasets.
This paper reviews early time series classification methods.
problem Minimizing class prediction delay in time-sensitive applications.
method Divided into four categories: prefix based, shapelet based, model based, and miscellaneous approaches.
result Demonstrates reasonable performance in various applications.
Emulator speeds up landslide run-out modeling sensitivity analysis.
problem Computational challenges in assessing landslide run-out model sensitivity.
method Gaussian process emulation integrated into r.avaflow.
result Strong interactions detected between friction coefficients and release volume.
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent k…
Sepsis is a life-threatening host response to infection associated with high mortality, morbidity, and health costs. Its management is highly time-sensitive since each hour of delayed treatment increases mortality due to irreversible organ damage. Meanwhile, despite decades of clinical research, robust biomarkers for s…
Stock prediction aims to predict the future trends of a stock in order to help investors to make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice…
Isthmus platform simplifies ML/AI integration in healthcare.
problem Challenges in deploying ML models in healthcare due to data quality, regulatory, and security issues.
method Turnkey, cloud-based platform addressing data quality, clinical relevance, and regulatory compliance.
result Reduces time to market for operationalizing ML/AI in healthcare.
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.
TAMO optimizes multiple objectives in-context using transformers.
problem Balancing competing objectives in expensive, black-box problems.
method Fully amortized, transformer-based policy for multi-objective optimization.
result Significant speedup in proposal time with improved Pareto quality.
A Bayesian framework models dynamic probability predictions over time.
problem Dynamic probability predictions over time in various settings.
method Gaussian latent information martingale (GLIM) framework.
result GLIM outperforms baseline methods in predicting future uncertainties.
Paper analyzes CCT sensitivity in constrained power systems, offering insights into system stability and parameter changes.
problem Identifying preventive control measures to avoid large generation losses during disturbances.
method Derived first-order CCT sensitivity for generic constrained power systems using trajectory sensitivity computation.
result Sensitivity of CCT to system parameters, providing insights into feasibility and stability.
New algorithm reduces age of information in wireless networks with unknown channel reliability.
problem Learning optimal source-channel pairs to minimize age of information in wireless networks.
method Introduces AoI regret, novel learning algorithm with bounded AoI regret.
result Developed a learning algorithm with O(1) AoI regret, improving upon Θ(logT). The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
problem Improving generative model performance.
method Integrates a learnable Gauge Field into Flow ODEs.
result Gauge Flow Models outperform traditional Flow Models in Flow Matching experiments.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
MALC combines interpretable linear models with black-box models for better predictions and transparency.
problem Combining interpretability with black-box models for better predictions.
method Formulates MALC as a convex optimization problem and uses accelerated proximal gradient method for training.
result MALC provides an efficient frontier balancing prediction accuracy and transparency.
Revises Bayesian model averaging for foundation models.
problem Ensemble pre-trained and lightly-finetuned foundation models for improved classification performance.
method Introduces trainable linear classifiers and computationally cheaper model averaging scheme (OMA).
result Ensembled models can better predict on various datasets.