Cover Image

Strong Linear Baselines Strike Back: Closed-Form Linear Models as Gaussian Process Conditional Density Estimators for TSAD

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yugay, Aleksandr, Cui, Hang, Pei, Changhua, Zaytsev, Alexey
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Uniting contrastive and generative learning for event sequences models
by: Yugay, Aleksandr, et al.
Published: (2024)

A theoretical framework for self-supervised contrastive learning for continuous dependent data
by: Marusov, Alexander, et al.
Published: (2025)

Enhancing the Reliability of Medical AI through Expert-guided Uncertainty Modeling
by: Khalin, Aleksei, et al.
Published: (2026)

Uncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices
by: Kostenok, Elizaveta, et al.
Published: (2023)

Holistic Uncertainty Estimation For Open-Set Recognition
by: Erlygin, Leonid, et al.
Published: (2024)

Bayesian Modeling and Estimation of Linear Time-Varying Systems using Neural Networks and Gaussian Processes
by: Shulman, Yaniv
Published: (2025)

Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
by: Koldasbayeva, Diana, et al.
Published: (2025)

Beyond Simple Averaging: Improving NLP Ensemble Performance with Topological-Data-Analysis-Based Weighting
by: Proskura, Polina, et al.
Published: (2024)

Parameter-Efficient Neural CDEs via Implicit Function Jacobians
by: Kuleshov, Ilya, et al.
Published: (2025)

Adapters Strike Back
by: Steitz, Jan-Martin O., et al.
Published: (2024)

Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
by: Wenger, Jonathan, et al.
Published: (2024)

Tsururu: A Python-based Time Series Forecasting Strategies Library
by: Kostromina, Alina, et al.
Published: (2025)

Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
by: Serov, Egor, et al.
Published: (2026)

U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Series
by: Kuleshov, Ilya, et al.
Published: (2026)

A Spectral Framework for Closed-Form Relative Density Estimation
by: Bach, Francis
Published: (2026)

Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models
by: Peng, Bincheng, et al.
Published: (2026)

Recommendations for Baselines and Benchmarking Approximate Gaussian Processes
by: Ober, Sebastian W., et al.
Published: (2024)

Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models)
by: Geng, Scott, et al.
Published: (2026)

Kryptonite-N: Machine Learning Strikes Back
by: Li, Albus, et al.
Published: (2024)

Gaussian Process Regression for Inverse Problems in Linear PDEs
by: Li, Xin, et al.
Published: (2025)

Physics-informed Gaussian Processes as Linear Model Predictive Controller
by: Tebbe, Jörn, et al.
Published: (2024)

Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge
by: Bazarova, Alexandra, et al.
Published: (2024)

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
by: Lin, Jihao Andreas, et al.
Published: (2024)

Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models
by: Yamaoka, Kanta, et al.
Published: (2026)

Closed-Form Training Dynamics Reveal Learned Features and Linear Structure in Word2Vec-like Models
by: Karkada, Dhruva, et al.
Published: (2025)

Normalizing self-supervised learning for provably reliable Change Point Detection
by: Bazarova, Alexandra, et al.
Published: (2024)

WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
by: Stepikin, Alexander, et al.
Published: (2025)

Language steering in latent space to mitigate unintended code-switching
by: Goncharov, Andrey, et al.
Published: (2025)

Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems
by: Hu, Amber, et al.
Published: (2024)

Derivation of Closed Form of Expected Improvement for Gaussian Process Trained on Log-Transformed Objective
by: Watanabe, Shuhei
Published: (2024)

From Continual Learning to SGD and Back: Better Rates for Continual Linear Models
by: Evron, Itay, et al.
Published: (2025)

Estimating Causal Effects in Gaussian Linear SCMs with Finite Data
by: Maiti, Aurghya, et al.
Published: (2026)

Physics-informed Gaussian Process Regression in Solving Eigenvalue Problem of Linear Operators
by: Bai, Tianming, et al.
Published: (2026)

Sample Complexity of Agnostic Multiclass Classification: Natarajan Dimension Strikes Back
by: Cohen, Alon, et al.
Published: (2025)

Online Forgetting Process for Linear Regression Models
by: Li, Yuantong, et al.
Published: (2020)

Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention
by: Kovtun, Elizaveta, et al.
Published: (2023)

InDiD: Instant Disorder Detection via Representation Learning
by: Romanenkova, Evgenia, et al.
Published: (2021)

Dynamic Low-rank Approximation of Full-Matrix Preconditioner for Training Generalized Linear Models
by: Matveeva, Tatyana, et al.
Published: (2025)

TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection
by: Haq, Ijaz Ul, et al.
Published: (2023)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)