Author

Nir Shlezinger

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Preprint Sep 2026

Conformalized Kalman Filters for State Estimation with Trustworthy Confidence Regions

Kalman-type filters are widely used for tracking dynamic systems, yet the confidence regions commonly derived from their estimated covariances can become unreliable under nonlinearities, non-Gaussian disturbances, and model mismatch. In this work, we develop a conformal prediction (CP) framework for equipping Kalman-ty...

Olga Weisman, Nir Shlezinger, Bracha Laufer-Goldshtein · 0 citations

Preprint Aug 2026

EM-KalmanNet: Learned Expectation-Maximization for Adaptive Tracking in Partially Known, Block-Wise Time-Varying State-Space Models

State estimation in partially known state space (SS) models is challenging when the dynamics or observation model varies across short data blocks. Classical model-based approaches, such as the expectation-maximization (EM) Kalman filter, jointly recover the latent states and the unknown model parameters, but rely on li...

O. Cohen, Nir Shlezinger, T. Routtenberg · 0 citations

#machine learning Preprint Sep 2026

MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs

MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support heterogeneous traffic patterns, decentralized transmit-power allocation becomes increasingly challenging. We develop a unified learned optim...

Tomer Alter, Nir Shlezinger, Michael Segal · 0 citations