:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yadav, Shashank, Subbian, Vignesh
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2506.19035
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering
by: Ghaderi, Hamid, et al.
Published: (2024)

FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
by: Souligne, Nick, et al.
Published: (2026)

FairLogue: Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using the All of Us Research Program
by: Souligne, Nick, et al.
Published: (2026)

PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping
by: Pungitore, Sarah, et al.
Published: (2025)

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data
by: Ghaderi, Hamid, et al.
Published: (2023)

Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks
by: Pungitore, Sarah, et al.
Published: (2025)

Towards Foundation Models for Critical Care Time Series
by: Burger, Manuel, et al.
Published: (2024)

Revealing Interpretable Failure Modes of VLMs
by: Chaudhary, Isha, et al.
Published: (2026)

SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models
by: Pungitore, Sarah, et al.
Published: (2025)

Evaluating Simplification Algorithms for Interpretability of Time Series Classification
by: Håvardstun, Brigt, et al.
Published: (2025)

Rethinking Large Language Models For Irregular Time Series Classification In Critical Care
by: Zheng, Feixiang, et al.
Published: (2026)

Towards Self-Supervised Foundation Models for Critical Care Time Series
by: Jagd, Katja Naasunnguaq, et al.
Published: (2025)

Differential Machine Learning for Time Series Prediction
by: Yadav, Akash, et al.
Published: (2025)

PINNs Failure Modes are Overfitting
by: Andersen, Nigel T., et al.
Published: (2026)

Predicting the Containment Time of California Wildfires Using Machine Learning
by: Bhardwaj, Shashank
Published: (2025)

KnowIt: Deep Time Series Modeling and Interpretation
by: Theunissen, M. W., et al.
Published: (2025)

Fast and Interpretable Mortality Risk Scores for Critical Care Patients
by: Zhu, Chloe Qinyu, et al.
Published: (2023)

Development and Evaluation of an Ontology for Non-Invasive Respiratory Support in Acute Care
by: Islam, Md Fantacher, et al.
Published: (2025)

EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs
by: Berghaus, David
Published: (2026)

Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
by: de Souza, Douglas Baptista, et al.
Published: (2024)

Toward Interpretable Evaluation Measures for Time Series Segmentation
by: Chavelli, Félix, et al.
Published: (2025)

Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics
by: Tirukkonda, Vignesh, et al.
Published: (2024)

Interpretable Time Series Autoregression for Periodicity Quantification
by: Chen, Xinyu, et al.
Published: (2025)

Correlating Time Series with Interpretable Convolutional Kernels
by: Chen, Xinyu, et al.
Published: (2024)

Algorithmic Recourse in Abnormal Multivariate Time Series
by: Han, Xiao, et al.
Published: (2023)

Interpretable Deep Regression Models with Interval-Censored Failure Time Data
by: Yuan, Changhui, et al.
Published: (2025)

Aligning Language Models with Clinical Expertise: DPO for Heart Failure Nursing Documentation in Critical Care
by: Fan, Junyi, et al.
Published: (2025)

Learning Interpretable Differentiable Logic Networks for Time-Series Classification
by: Yue, Chang, et al.
Published: (2025)

Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classification
by: Barašin, Irina, et al.
Published: (2024)

Multi-Stage Prototype Learning for Interpretable Time Series Classification
by: Kalisetti, Bhavesh, et al.
Published: (2021)

Interpretability in Deep Time Series Models Demands Semantic Alignment
by: De Felice, Giovanni, et al.
Published: (2026)

Failure Modes of Maximum Entropy RLHF
by: Çağatan, Ömer Veysel, et al.
Published: (2025)

Physics-Guided Counterfactual Explanations for Large-Scale Multivariate Time Series: Application in Scalable and Interpretable SEP Event Prediction
by: Patil, Pranjal, et al.
Published: (2026)

CRITS: Convolutional Rectifier for Interpretable Time Series Classification
by: Kuratomi, Alejandro, et al.
Published: (2025)

Mechanistic Interpretability for Transformer-based Time Series Classification
by: Kalnāre, Matīss, et al.
Published: (2025)

ST-Tree with Interpretability for Multivariate Time Series Classification
by: Du, Mingsen, et al.
Published: (2024)

Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning
by: Obata, Kohei, et al.
Published: (2026)

ProtoTSNet: Interpretable Multivariate Time Series Classification With Prototypical Parts
by: Małkus, Bartłomiej, et al.
Published: (2025)

$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
by: Boniol, Paul, et al.
Published: (2025)

CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
by: Tajeuna, Etienne, et al.
Published: (2026)