Paper Citation Record · LEDGER

A unifying framework for generalised Bayesian online learning in non-stationary environments

As of 15 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 2 inbound Pith citation observations for arXiv:2411.10153.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.10153 v3

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:01:26.039606Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:25:16.785243Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T16:39:57.436540Z

Reference resolution

79 of 79 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 66718e12-07e7-4727-b28a-3f643481031b · outbound

This paper cites Adaptive time series forecasting with markovian variance switching, 2024.

A unifying framework for generalised Bayesian online learning in non-stationary environments Adaptive time series forecasting with markovian variance switching, 2024

Reference 1

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Observation 0c7891c1-6f53-410d-b4f9-7fee28daebaa · outbound

This paper cites an unresolved cited work.

A unifying framework for generalised Bayesian online learning in non-stationary environments Unresolved cited work

Reference 2

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Observation faee6dd5-36f3-4857-80ba-848a579d7278 · outbound

This paper cites Bayesian online prediction of change points.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian online prediction of change points

Reference 3

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Observation a78bfadd-0d3f-40ee-8057-bfba5529219a · outbound

This paper cites Bayesian change-point detection for bandit feedback in non-stationary environments.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian change-point detection for bandit feedback in non-stationary environments

Reference 4

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Observation f047014a-23b7-4257-9dbf-105d5b096ff2 · outbound

This paper cites Restarted bayesian online change-point detector achieves optimal detection delay.

A unifying framework for generalised Bayesian online learning in non-stationary environments Restarted bayesian online change-point detector achieves optimal detection delay

Reference 5

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Observation 8678f8cf-7653-4f14-8da4-bd92b67dc47e · outbound

This paper cites Robust and scalable bayesian online changepoint detection, 2023.

A unifying framework for generalised Bayesian online learning in non-stationary environments Robust and scalable bayesian online changepoint detection, 2023

Reference 6

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Observation 6aa16e8c-c2e1-4b95-94e4-2b24b612ffdf · outbound

This paper cites A survey of methods for time series change point detection.

A unifying framework for generalised Bayesian online learning in non-stationary environments A survey of methods for time series change point detection

Reference 7

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Observation 4b3d1b62-b27e-4a4a-aae6-54bec1a9507b · outbound

This paper cites On Warm-Starting Neural Network Training.

A unifying framework for generalised Bayesian online learning in non-stationary environments On Warm-Starting Neural Network Training

Reference 8

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This paper cites Product partition models for change point problems.

A unifying framework for generalised Bayesian online learning in non-stationary environments Product partition models for change point problems

Reference 9

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Observation 578711f9-ed2d-4284-89a2-f4ea22f82cdc · outbound

This paper cites Detection of abrupt changes: theory and application, volume 104.

A unifying framework for generalised Bayesian online learning in non-stationary environments Detection of abrupt changes: theory and application, volume 104

Reference 10

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Observation 61f7e4e3-79bc-43a2-acc1-cd2cab49886e · outbound

This paper cites Space guidance evolution-a personal narrative.

A unifying framework for generalised Bayesian online learning in non-stationary environments Space guidance evolution-a personal narrative

Reference 11

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Observation 026845ec-1be4-42e1-93d4-dbcd0b1ef2b5 · outbound

This paper cites The infinite hidden markov model.

A unifying framework for generalised Bayesian online learning in non-stationary environments The infinite hidden markov model

Reference 12

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Observation 4d2d8678-d470-436d-ab0b-dd01d480e2be · outbound

This paper cites Bencomo, Jake C.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bencomo, Jake C

Reference 13

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Observation 4f4b3ed0-9bd2-4349-aeba-d7abdd414253 · outbound

This paper cites Bernardo and A.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bernardo and A

Reference 14

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Observation 274aa4af-e11e-420a-ba61-61f8653c3002 · outbound

This paper cites Weight uncertainty in neural network.

A unifying framework for generalised Bayesian online learning in non-stationary environments Weight uncertainty in neural network

Reference 15

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Observation 205d9c66-2218-4090-8d48-8a827174bcfa · outbound

This paper cites JAX : composable transformations of P ython+ N um P y programs, 2018.

A unifying framework for generalised Bayesian online learning in non-stationary environments JAX : composable transformations of P ython+ N um P y programs, 2018

Reference 16

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This paper cites Online continual learning with natural distribution shifts: An empirical study with visual data.

A unifying framework for generalised Bayesian online learning in non-stationary environments Online continual learning with natural distribution shifts: An empirical study with visual data

Reference 17

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Observation 12b8ca9c-5484-4f9c-97d4-05ec468ce228 · outbound

This paper cites Nonlinear bayesian filtering with natural gradient gaussian approximation.

A unifying framework for generalised Bayesian online learning in non-stationary environments Nonlinear bayesian filtering with natural gradient gaussian approximation

Reference 18

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This paper cites Bandits for algorithmic trading with signals.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bandits for algorithmic trading with signals

Reference 19

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This paper cites Detecting toxic flow, 2023 b.

A unifying framework for generalised Bayesian online learning in non-stationary environments Detecting toxic flow, 2023 b

Reference 20

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Observation 5cf3f87f-79d6-4ce5-ad9c-64e0090889a4 · outbound

This paper cites A mixture-of-experts framework for adaptive kalman filtering.

A unifying framework for generalised Bayesian online learning in non-stationary environments A mixture-of-experts framework for adaptive kalman filtering

Reference 21

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Observation 48c0aefb-f812-40df-975b-18a05d1c21c5 · outbound

This paper cites State estimation for discrete systems with switching parameters.

A unifying framework for generalised Bayesian online learning in non-stationary environments State estimation for discrete systems with switching parameters

Reference 22

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Observation 8d62358b-5baa-4bdb-a251-d87c383b4848 · outbound

This paper cites On diagonal approximations to the extended kalman filter for online training of bayesian neural networks.

A unifying framework for generalised Bayesian online learning in non-stationary environments On diagonal approximations to the extended kalman filter for online training of bayesian neural networks

Reference 23

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Observation 72d218dd-aea7-4391-a371-e51e3fcce215 · outbound

This paper cites Low-rank extended kalman filtering for online learning of neural networks from streaming data.

A unifying framework for generalised Bayesian online learning in non-stationary environments Low-rank extended kalman filtering for online learning of neural networks from streaming data

Reference 24

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A unifying framework for generalised Bayesian online learning in non-stationary environments Gee, and Arnaud Doucet

Reference 25

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Observation 2aa68847-5040-497f-b922-e2e565991ca6 · outbound

This paper cites Loss of plasticity in deep continual learning.

A unifying framework for generalised Bayesian online learning in non-stationary environments Loss of plasticity in deep continual learning

Reference 26

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This paper cites Efficient online bayesian inference for neural bandits.

A unifying framework for generalised Bayesian online learning in non-stationary environments Efficient online bayesian inference for neural bandits

Reference 27

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This paper cites Shestpaloff, Leandro S \'a nchez-Betancourt, Jeremias Knoblauch, Matt Jones, Briol Fran c ois-Xavier, and Kevin P.

A unifying framework for generalised Bayesian online learning in non-stationary environments Shestpaloff, Leandro S \'a nchez-Betancourt, Jeremias Knoblauch, Matt Jones, Briol Fran c ois-Xavier, and Kevin P

Reference 28

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This paper cites Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics.

A unifying framework for generalised Bayesian online learning in non-stationary environments Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics

Reference 29

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This paper cites Econometric policy model construction: the post-bayesian approach.

A unifying framework for generalised Bayesian online learning in non-stationary environments Econometric policy model construction: the post-bayesian approach

Reference 30

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Observation 23dca7f7-efd3-4534-9df3-1b749c355a26 · outbound

This paper cites Day-ahead electricity demand forecasting competition: Post-covid paradigm.

A unifying framework for generalised Bayesian online learning in non-stationary environments Day-ahead electricity demand forecasting competition: Post-covid paradigm

Reference 31

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This paper cites On-line inference for multiple changepoint problems.

A unifying framework for generalised Bayesian online learning in non-stationary environments On-line inference for multiple changepoint problems

Reference 32

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This paper cites Efficient bayesian analysis of multiple changepoint models with dependence across segments.

A unifying framework for generalised Bayesian online learning in non-stationary environments Efficient bayesian analysis of multiple changepoint models with dependence across segments

Reference 33

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This paper cites Changepoint detection in the presence of outliers.

A unifying framework for generalised Bayesian online learning in non-stationary environments Changepoint detection in the presence of outliers

Reference 34

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This paper cites The sticky hdp-hmm: Bayesian nonparametric hidden markov models with persistent states.

A unifying framework for generalised Bayesian online learning in non-stationary environments The sticky hdp-hmm: Bayesian nonparametric hidden markov models with persistent states

Reference 35

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This paper cites Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset.

A unifying framework for generalised Bayesian online learning in non-stationary environments Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset

Reference 36

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A unifying framework for generalised Bayesian online learning in non-stationary environments Knowledge discovery from data streams

Reference 37

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Observation 62d2412a-654e-4069-af4e-38d54e0c4743 · outbound

This paper cites Variational learning for switching state-space models.

A unifying framework for generalised Bayesian online learning in non-stationary environments Variational learning for switching state-space models

Reference 38

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This paper cites Comprehensive analysis of change-point dynamics detection in time series data: A review.

A unifying framework for generalised Bayesian online learning in non-stationary environments Comprehensive analysis of change-point dynamics detection in time series data: A review

Reference 39

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This paper cites Kalman filtering and neural networks.

A unifying framework for generalised Bayesian online learning in non-stationary environments Kalman filtering and neural networks

Reference 40

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This paper cites Gradual changes versus abrupt changes.

A unifying framework for generalised Bayesian online learning in non-stationary environments Gradual changes versus abrupt changes

Reference 41

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This paper cites Improving predictions of bayesian neural nets via local linearization.

A unifying framework for generalised Bayesian online learning in non-stationary environments Improving predictions of bayesian neural nets via local linearization

Reference 42

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This paper cites Bayesian Online Natural Gradient (BONG).

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian Online Natural Gradient (BONG)

Reference 43

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This paper cites A new approach to linear filtering and prediction problems.

A unifying framework for generalised Bayesian online learning in non-stationary environments A new approach to linear filtering and prediction problems

Reference 44

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This paper cites Spatio-temporal bayesian on-line changepoint detection with model selection.

A unifying framework for generalised Bayesian online learning in non-stationary environments Spatio-temporal bayesian on-line changepoint detection with model selection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.982230Z

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Observation 2d826955-9c42-4f34-b809-99f0fea1942c · outbound

This paper cites Doubly robust bayesian inference for non-stationary streaming data with -divergences.

A unifying framework for generalised Bayesian online learning in non-stationary environments Doubly robust bayesian inference for non-stationary streaming data with -divergences

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.965880Z

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Observation b7e9ab57-091c-4617-bbbe-8c5a4ea3af2e · outbound

This paper cites An optimization-centric view on bayes' rule: Reviewing and generalizing variational inference.

A unifying framework for generalised Bayesian online learning in non-stationary environments An optimization-centric view on bayes' rule: Reviewing and generalizing variational inference

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.949993Z

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Observation f09dd083-770a-4f61-becc-893740d75cfe · outbound

This paper cites Ridge regression signal processing.

A unifying framework for generalised Bayesian online learning in non-stationary environments Ridge regression signal processing

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.932943Z

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Observation a03b54ce-2601-4cd1-b474-5450a5c155e1 · outbound

This paper cites Continual learning with bayesian neural networks for non-stationary data.

A unifying framework for generalised Bayesian online learning in non-stationary environments Continual learning with bayesian neural networks for non-stationary data

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.916620Z

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Observation bbfa0081-d0a8-4196-8027-77d6c6d6ef3c · outbound

This paper cites The recursive variational gaussian approximation (r-vga).

A unifying framework for generalised Bayesian online learning in non-stationary environments The recursive variational gaussian approximation (r-vga)

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.899398Z

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Observation 373b5d74-db56-4a30-873b-e16a156cc62c · outbound

This paper cites The limited-memory recursive variational gaussian approximation (l-rvga).

A unifying framework for generalised Bayesian online learning in non-stationary environments The limited-memory recursive variational gaussian approximation (l-rvga)

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.882400Z

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Observation b22db6fb-f8b4-491b-8832-cb4a8088f332 · outbound

This paper cites Detecting and adapting to irregular distribution shifts in bayesian online learning, 2021.

A unifying framework for generalised Bayesian online learning in non-stationary environments Detecting and adapting to irregular distribution shifts in bayesian online learning, 2021

Reference 52

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Observation e0c19890-f438-4c0c-8bbe-181d1e534cd9 · outbound

This paper cites A contextual-bandit approach to personalized news article recommendation.

A unifying framework for generalised Bayesian online learning in non-stationary environments A contextual-bandit approach to personalized news article recommendation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.847591Z

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This paper cites Bayesian learning and inference in recurrent switching linear dynamical systems.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian learning and inference in recurrent switching linear dynamical systems

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.831190Z

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Observation de6b2203-f5ce-4f4a-a33b-efffae7514e0 · outbound

This paper cites Robust sequential online prediction with dynamic ensemble of multiple models: A review.

A unifying framework for generalised Bayesian online learning in non-stationary environments Robust sequential online prediction with dynamic ensemble of multiple models: A review

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.811550Z

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Observation 5e071405-b1ac-4201-95fc-eddcde910f58 · outbound

This paper cites Nonstationary bandit learning via predictive sampling.

A unifying framework for generalised Bayesian online learning in non-stationary environments Nonstationary bandit learning via predictive sampling

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.792904Z

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Observation bc789714-007d-421c-8598-a3ec97421e3c · outbound

This paper cites Optimal adaptive estimation of sampled stochastic processes.

A unifying framework for generalised Bayesian online learning in non-stationary environments Optimal adaptive estimation of sampled stochastic processes

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.775834Z

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Observation 4acba0ac-bae0-4539-bc37-4248cc60ab79 · outbound

This paper cites Thompson Sampling in Switching Environments with Bayesian Online Change Point Detection.

A unifying framework for generalised Bayesian online learning in non-stationary environments Thompson Sampling in Switching Environments with Bayesian Online Change Point Detection

Reference 58

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Observation 0e60cc1b-9b45-4db9-9e08-beeaabc13e80 · outbound

This paper cites Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient.

A unifying framework for generalised Bayesian online learning in non-stationary environments Slang: Fast structured covariance approximations for bayesian deep learning with natural gradient

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.758858Z

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Observation ba6c0970-a70b-4012-ac6c-a953e22c2a61 · outbound

This paper cites BAM: Bayes with Adaptive Memory.

A unifying framework for generalised Bayesian online learning in non-stationary environments BAM: Bayes with Adaptive Memory

Reference 60

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Observation d5f016d0-11ea-4002-a64d-36a493cfe963 · outbound

This paper cites Variational Continual Learning.

A unifying framework for generalised Bayesian online learning in non-stationary environments Variational Continual Learning

Reference 61

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This paper cites Online natural gradient as a Kalman filter.

A unifying framework for generalised Bayesian online learning in non-stationary environments Online natural gradient as a Kalman filter

Reference 62

Resolution: verified exact; doi, observed 2026-08-12T20:01:26.096230Z

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Observation 2439cd0f-7ce6-49f4-b5c5-3f426e3dd641 · outbound

This paper cites From hmm's to segment models: A unified view of stochastic modeling for speech recognition.

A unifying framework for generalised Bayesian online learning in non-stationary environments From hmm's to segment models: A unified view of stochastic modeling for speech recognition

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.741133Z

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Observation 0ad8bba6-9015-47b0-9184-31e0d2c8da0c · outbound

This paper cites Bayesian approach to system identification.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian approach to system identification

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.724074Z

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Observation 8bf03430-a014-41be-a4ba-7ef25518977e · outbound

This paper cites Towards robust inference for bayesian filtering of linear gaussian dynamical systems subject to additive change.

A unifying framework for generalised Bayesian online learning in non-stationary environments Towards robust inference for bayesian filtering of linear gaussian dynamical systems subject to additive change

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.705165Z

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Observation 9801af7c-aff0-4623-b363-a10d3fc91a8d · outbound

This paper cites Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling, 2018.

A unifying framework for generalised Bayesian online learning in non-stationary environments Deep bayesian bandits showdown: An empirical comparison of bayesian deep networks for thompson sampling, 2018

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.685536Z

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Observation 88e549ed-2e7e-45ad-b36c-33017063390f · outbound

This paper cites The Bayesian choice: from decision-theoretic foundations to computational implementation, volume 2.

A unifying framework for generalised Bayesian online learning in non-stationary environments The Bayesian choice: from decision-theoretic foundations to computational implementation, volume 2

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.666098Z

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Observation 0a7a4d4a-e26a-4ac3-8e1c-b0ef2c78a3c9 · outbound

This paper cites The ensemble kalman filter: a signal processing perspective.

A unifying framework for generalised Bayesian online learning in non-stationary environments The ensemble kalman filter: a signal processing perspective

Reference 68

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Observation 4f6b263a-aceb-4c5c-a80e-fa4217424b4b · outbound

This paper cites Gaussian process change point models.

A unifying framework for generalised Bayesian online learning in non-stationary environments Gaussian process change point models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.647785Z

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Observation aa2949bf-80e1-4d02-911f-50c7bd6475bb · outbound

This paper cites Bayesian filtering and smoothing, volume 17.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian filtering and smoothing, volume 17

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.630658Z

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Observation 00de899c-0bd0-42b7-90ff-b6e42e11a8e9 · outbound

This paper cites Test-time adaptation with state-space models.

A unifying framework for generalised Bayesian online learning in non-stationary environments Test-time adaptation with state-space models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T20:01:26.000161Z

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Observation b734704b-291c-458d-97dd-d506d1a8e1fb · outbound

This paper cites Bayesian online change point detection with hilbert space approximate student-t process.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian online change point detection with hilbert space approximate student-t process

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.612495Z

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Observation c983a1e5-6c3d-4b82-a59c-2b7266ff0a6e · outbound

This paper cites On the likelihood that one unknown probability exceeds another in view of the evidence of two samples.

A unifying framework for generalised Bayesian online learning in non-stationary environments On the likelihood that one unknown probability exceeds another in view of the evidence of two samples

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T20:01:26.010350Z

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Observation 8192e2b4-31f7-457e-ac86-1cd3a234b911 · outbound

This paper cites Kalman filter for online classification of non-stationary data.

A unifying framework for generalised Bayesian online learning in non-stationary environments Kalman filter for online classification of non-stationary data

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.580906Z

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Observation 89f9db07-8a01-4ad3-a599-8671e246d8b0 · outbound

This paper cites An Evaluation of Change Point Detection Algorithms.

A unifying framework for generalised Bayesian online learning in non-stationary environments An Evaluation of Change Point Detection Algorithms

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T20:01:26.019901Z

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Observation 9b8c194d-7e5e-4778-9afc-cc6569c4826c · outbound

This paper cites Beam sampling for the infinite hidden markov model.

A unifying framework for generalised Bayesian online learning in non-stationary environments Beam sampling for the infinite hidden markov model

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.559369Z

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Observation 96fc0a09-008c-4ac5-9afc-dc00c89cb11a · outbound

This paper cites Bayesian forecasting and dynamic models.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian forecasting and dynamic models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.541231Z

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Observation 67a1c33d-31b5-4dba-bef4-f6768c5fc96d · outbound

This paper cites Bayesian online learning of the hazard rate in change-point problems.

A unifying framework for generalised Bayesian online learning in non-stationary environments Bayesian online learning of the hazard rate in change-point problems

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T20:01:26.523342Z

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Observation 11c7dfd3-8cb2-40af-b498-a3ec777f4ff8 · outbound

This paper cites write newline.

A unifying framework for generalised Bayesian online learning in non-stationary environments write newline

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T20:01:26.039606Z

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Pith citing papers

Observation a22ab754-d70f-4d34-ada6-a1e82a152eac · inbound

Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data cites this paper.

Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data A unifying framework for generalised Bayesian online learning in non-stationary environments

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T23:25:16.785243Z

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Observation c1feae39-4e5a-4247-925b-ad7e5bd12b0e · inbound

A probabilistic framework for online test-time adaptation cites this paper.

A probabilistic framework for online test-time adaptation A unifying framework for generalised Bayesian online learning in non-stationary environments

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:39:57.437995Z

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