Learning structured linear dynamical systems from missing observations

arXiv:2610.11869 · stat.ML, cs.LG, cs.SY, eess.SY, math.OC, math.ST, stat.TH · Submitted 2026-10-08 · Read on arXiv

stat.ML, cs.LG, cs.SY, eess.SY, math.OC, math.ST, stat.TH

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Objective Function — 5× in this paper · explained in 2 episodes
• Sparsity — 4× in this paper · explained in 4 episodes
• System Identification — 4× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• IEEE Conference on Decision and Control — 1× in this paper
• Non-asymptotic Analysis — 1× in this paper · explained in 2 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework
• Semi-supervised linear regression with missing covariates
• Large Vector Auto Regressions
• Finite Sample Analysis of System Poles for Ho-Kalman Algorithm
• Learning linear dynamical systems under convex constraints
• Online Joint Topology Identification and Signal Estimation from Streams with Missing Data
• Provably Efficient Sensor Allocation for Unknown High-dimensional Systems with Limited Sensing

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