From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators
2026-07-02T07:24:01Z•c7121665913e9c36745956894300d8dca23aabbf43daf72f4ad83d1fd0edf0c1
Cover theoryFourier Neural OperatorsKalman filteringNavier–StokesPDEsRealPage','REITs'TVARalgorithmic housingfunction-countinggenomics applicationsgraphical modelsgroup-Lassolow-dimensional structuremultitask learningneural estimationoperator learningprocess-noise estimationrank-based losssample complexityseparable graphsshared sparsityspectral methodstime-seriesuncertainty quantificationvariational inference
What happened
Collection of new arXiv ML/STAT papers (2026-07-02) covering theory and methods: (1) Fourier Neural Operators — approximation and polynomial sample-complexity guarantees for time-T solution operators of dissipative PDEs (incl. Navier–Stokes) via spectral-discretization assumptions; (2) neural estimation of time-varying AR(p) parameters with uncertainty quantification under Gaussian and Laplace noise; (3) hierarchical variational Kalman filtering — surrogate state for explicit process-noise inference and faster, single-step hyperparameter fitting; (4) deep multitask learning with unknown monot0
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- c7121665913e9c36745956894300d8dca23aabbf43daf72f4ad83d1fd0edf0c1
- Enrichment time
- 2026-07-02T07:24:01Z
- AI-assisted enrichment
- Yes
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