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Found 71 papers, 23 papers with code
Multimodal Structure Preservation Learning
When selecting data to build machine learning models in practical applications, factors such as availability, acquisition cost, and discriminatory power are crucial considerations.
TimeSeriesExam: A time series understanding exam
We believe that the ability to programatically generate questions is fundamental to assessing and improving LLM's ability to understand and reason about time series data.
Exploring Representations and Interventions in Time Series Foundation Models
These findings underscore the value of representational analysis for optimizing models and demonstrate how conceptual steering offers new possibilities for more controlled and efficient time series analysis with TSFMs.
Implicit Reasoning in Deep Time Series Forecasting
Recently, time series foundation models have shown promising zero-shot forecasting performance on time series from a wide range of domains.
Bifurcation Identification for Ultrasound-driven Robotic Cannulation
This is primarily due to the limited availability of ground truth data, in particular, data from live subjects, needed for training and validating reliable models.
A SAT-based approach to rigorous verification of Bayesian networks
Recent advancements in machine learning have accelerated its widespread adoption across various real-world applications.
Enhanced Uncertainty Estimation in Ultrasound Image Segmentation with MSU-Net
Autonomous robotic ultrasound systems can aid in needle insertion for medication delivery and support non-experts in such tasks.
A Rate-Distortion View of Uncertainty Quantification
In this paper, we introduce Distance Aware Bottleneck (DAB), i. e., a new method for enriching deep neural networks with this property.
Exploring Loss Design Techniques For Decision Tree Robustness To Label Noise
In the real world, data is often noisy, affecting not only the quality of features but also the accuracy of labels.
MOMENT: A Family of Open Time-series Foundation Models
We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis.
Signal Quality Auditing for Time-series Data
Signal quality assessment (SQA) is required for monitoring the reliability of data acquisition systems, especially in AI-driven Predictive Maintenance (PMx) application contexts.
Motion Informed Needle Segmentation in Ultrasound Images
Third, to our knowledge we are the first to implement a learnable filter to incorporate non-linear needle motion for improving needle segmentation.
Forecasting Treatment Response with Deep Pharmacokinetic Encoders
Our hybrid global-local architecture improves over patient-specific models by 15. 8% on average.
AQuA: A Benchmarking Tool for Label Quality Assessment
We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning tools facing mislabeled data.
Hierarchically Coherent Multivariate Mixture Networks
Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings.
Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes
In this paper we propose a statistical approach to recovering sparse phenogroups (or subtypes) that demonstrate differential treatment effects as compared to the study population.
Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction
Robot-guided catheter insertion has the potential to deliver urgent medical care in situations where medical personnel are unavailable.
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings.
Classifying Unstructured Clinical Notes via Automatic Weak Supervision
Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes.
The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling
The application of a digital twin framework is highlighted in the field of predictive maintenance, and its extensions utilizing machine learning and physics based modeling.
Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact
Our weakly supervised models perform competitively with traditional supervised techniques and require less involvement from domain experts, demonstrating their use as efficient and practical alternatives to supervised learning in HC applications of ML.
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations.
auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data
Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death.
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
However, the common practice of relaxing discrete constraints to a continuous domain to ease optimization when learning kernels or metrics can harm generalization, as information which only encodes linkage is transformed to informing distances.
Generative Modeling Helps Weak Supervision (and Vice Versa)
The model outperforms baseline weak supervision label models on a number of multiclass image classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples.
Counterfactual Phenotyping with Censored Time-to-Events
Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring.
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.
Weak Supervision for Affordable Modeling of Electrocardiogram Data
We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points.
Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns
We demonstrate that the set of CSTS patterns is a concise representation of all spatio-temporal sequential patterns that can be discovered in a given dataset.
Provably Robust Model-Centric Explanations for Critical Decision-Making
We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be gleaned from LIME and SHAP, popular data-centric explanation tools in Artificial Intelligence (AI).
Kernel Density Decision Trees
FDTs address the sensitivity and tendency to overfitting of decision trees by representing uncertainty through fuzzy partitions.
ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS
In domains such as clinical research, where data collection and its careful characterization is particularly expensive and tedious, this reliance on pointillisticaly labeled data is one of the biggest roadblocks to the adoption of modern data-hungry ML algorithms.
Deep Attentive Variational Inference
Moreover, the architecture for this class of models favors local interactions among the latent variables between neighboring layers when designing the conditioning factors of the involved distributions.
End-to-End Weak Supervision
Aggregating multiple sources of weak supervision (WS) can ease the data-labeling bottleneck prevalent in many machine learning applications, by replacing the tedious manual collection of ground truth labels.
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Dependency Structure Misspecification in Multi-Source Weak Supervision Models
Data programming (DP) has proven to be an attractive alternative to costly hand-labeling of data.
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
We validate our proposed method, DMIDAS, on high-frequency healthcare and electricity price data with long forecasting horizons (~1000 timestamps) where we improve the prediction accuracy by 5% over state-of-the-art models, reducing the number of parameters of NBEATS by nearly 70%.
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors.
Leveraging Expert Consistency to Improve Algorithmic Decision Support
However, there is frequently a construct gap: a gap between the construct of interest to the decision-making task and what is captured in proxies used as labels to train ML models.
Robust Multi-view Representation Learning
Our synthetic and real-world experiments show promising results for the application of these models to robust representation learning.
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.
Self-Reflective Variational Autoencoder
The Variational Autoencoder (VAE) is a powerful framework for learning probabilistic latent variable generative models.
Preference-based Reinforcement Learning with Finite-Time Guarantees
If preferences are stochastic, and the preference probability relates to the hidden reward values, we present algorithms for PbRL, both with and without a simulator, that are able to identify the best policy up to accuracy $\varepsilon$ with high probability.
System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis
This paper reviews current literature in the field of predictive maintenance from the system point of view.
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner.
Pairwise Feedback for Data Programming
We propose to improve modeling of latent class variables in the programmatic creation of labeled datasets by incorporating pairwise feedback into the process.
Mutually Regressive Point Processes
Despite many potential applications, existing point process models are limited in their ability to capture complex patterns of interaction.
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning
By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition.
Zeroth Order Non-convex optimization with Dueling-Choice Bandits
We consider a novel setting of zeroth order non-convex optimization, where in addition to querying the function value at a given point, we can also duel two points and get the point with the larger function value.
Active Learning for Graph Neural Networks via Node Feature Propagation
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.
Thresholding Bandit Problem with Both Duels and Pulls
The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold.
Active Learning Graph Neural Networks via Node Feature Propagation
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.
DASGrad: Double Adaptive Stochastic Gradient
Adaptive moment methods have been remarkably successful for optimization under the presence of high dimensional or sparse gradients, in parallel to this, adaptive sampling probabilities for SGD have allowed optimizers to improve convergence rates by prioritizing examples to learn efficiently.
Nonlinear Semi-Parametric Models for Survival Analysis
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis.
Double Adaptive Stochastic Gradient Optimization
We observe that the benefits of~\textsc{DASGrad} increase with the model complexity and variability of the gradients, and we explore the resulting utility in extensions of distribution-matching multitask learning.
On the Interaction Effects Between Prediction and Clustering
Machine learning systems increasingly depend on pipelines of multiple algorithms to provide high quality and well structured predictions.
Learning under selective labels in the presence of expert consistency
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making.
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Finally, we present experiments that show the efficacy of RR and investigate its robustness to various sources of noise and model-misspecification.
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction.
Novel Prediction Techniques Based on Clusterwise Linear Regression
In this paper we propose two novel approaches on how to solve this problem.
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.
Characterization of Hemodynamic Signal by Learning Multi-View Relationships
We aim to investigate the usefulness of nonlinear multi-view relations to characterize multi-view data in an explainable manner.
Scaling Active Search using Linear Similarity Functions
In this paper, we consider the problem of Active Search where we are given a similarity function between data points.
Noise-Tolerant Interactive Learning from Pairwise Comparisons
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.
Clustering on the Edge: Learning Structure in Graphs
With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples.
Batched Lazy Decision Trees
We introduce a batched lazy algorithm for supervised classification using decision trees.
Canonical Autocorrelation Analysis
We present an extension of sparse Canonical Correlation Analysis (CCA) designed for finding multiple-to-multiple linear correlations within a single set of variables.
Lass-0: sparse non-convex regression by local search
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step.
Performance Bounds for Pairwise Entity Resolution
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets.
Real-Time Visual Analysis of Microvascular Blood Flow for Critical Care
Our method is able to extract microcirculatory measurements that are consistent with clinical intuition and it has a potential to become a useful tool in critical care medicine.
Projection Retrieval for Classification
In many applications classification systems often require in the loop human intervention.
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text
In an empirical study, we construct 190 different text classification tasks from a real-world benchmark, and the unlabeled documents are a mixture from all these tasks.
