Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version
2026-04-16T07:23:59Z•b24d4bb655164a9c6bc1950357835a32a283064e7c77c624eef42dbc9c7235f1
ADMMGaussian mixture modelsLangevin dynamicsM-productcausal representation learningclusteringcommittorconditional diffusionconformal inferencecovariance estimationimportance samplingkernel ridge regressionlow-rank tensor completionmachine learningmanifold optimizationmeta-learningnon-Markovian processesoptimizationrare-event samplingsemi-banditsstochastic controlt-SNE limitationstime-series forecastingtransition path theory
What happened
Collection of new arXiv papers (stat.ML) dated 2026-04-16 covering theoretical and algorithmic advances in machine learning, optimization, and stochastic processes. Key contributions include: off-model Monte Carlo training with importance sampling and adaptive updates for fully non‑Markovian stochastic control; framing committor estimation / rare-event sampling as a stochastic optimal control problem; identifiability results and a two-stage estimator for potentially degenerate Gaussian mixture latent-variable models under piecewise-affine mixing; joint manifold-based representation learning +G
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- b24d4bb655164a9c6bc1950357835a32a283064e7c77c624eef42dbc9c7235f1
- Enrichment time
- 2026-04-16T07:23:59Z
- AI-assisted enrichment
- Yes
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