arXiv Machine Learning July 3, 2026 By Abdullah Burkan Bereketoglu

Composite Reward Design in PPO-Driven Adaptive Filtering

Read the original on arXiv Machine Learning →

arXiv:2506. 06323v2 Announce Type: replace-cross Abstract: Model-free and reinforcement learning-based adaptive filtering methods are gaining traction for denoising in dynamic, non-stationary environments such as wireless signal channels, biomedical monitoring, and sensor networks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 15

Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators

arXiv:2606. 14195v1 Announce Type: new Abstract: Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation.

By Naichang Ke, Pongpisit Thanasutives, Yoshinobu Kawahara

arXiv Machine Learning

5d ago

Variance Reduction Based Experience Replay for Policy Optimization

arXiv:2602. 05379v2 Announce Type: replace-cross Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and accelerate policy optimization.

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Stabilizing the Q-Gradient Field for Policy Smoothness in Actor-Critic Methods

arXiv:2601. 22970v2 Announce Type: replace-cross Abstract: Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment.

By Jeong Woon Lee, Kyoleen Kwak, Daeho Kim, Hyoseok Hwang

arXiv Machine Learning

Jul 24

Generative Bayesian Filtering for State Estimation

arXiv:2607. 20521v1 Announce Type: new Abstract: The state of a dynamic system evolves over time, switching among several latent modes that govern its observable behavior.

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arXiv Machine Learning

Jun 3

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification

arXiv:2606. 02767v1 Announce Type: cross Abstract: Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning.

By Jiho Lee, Nisar R. Ahmed, Rebecca Russell

arXiv Machine Learning

Jul 31

Variance-Aware Baselines and Adaptive Learning Rates for Reinforcement Learning with Verifiable Rewards

arXiv:2511. 23310v3 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective paradigm for post-training large language models, yet the design of its baselines and learning-rate schedules remains largely heuristic.

By Zixun Huang, Jiayi Sheng, Zeyu Zheng

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