#function-approximation News & Analysis

3 articles tagged with #function-approximation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AINeutralarXiv – CS AI · 8h ago6/10

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Geometric Kolmogorov--Arnold Network (GeoKAN)

Researchers introduce Geometric Kolmogorov-Arnold Networks (GeoKANs), an advancement in KAN-type neural networks that learn geometry-adapted coordinate systems rather than relying on fixed Euclidean inputs. By adapting a diagonal Riemannian metric during training, GeoKAN redistributes computational capacity toward regions of rapid variation, making it particularly effective for physics-informed learning and differential equation problems.

AINeutralarXiv – CS AI · 8h ago6/10

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Towards Differentially Private Reinforcement Learning with General Function Approximation

Researchers present the first theoretical framework for differentially private reinforcement learning with general function approximation, achieving regret bounds of Õ(K^3/5) that match linear-case performance. This breakthrough extends privacy guarantees beyond tabular and linear settings, combining batched policy updates with the exponential mechanism for improved privacy-utility tradeoffs in online RL systems.

AINeutralarXiv – CS AI · 8h ago6/10

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R-GTD: A Geometric Analysis of Gradient Temporal-Difference Learning in Singular Regimes

Researchers propose R-GTD, a regularized gradient temporal-difference learning algorithm that maintains convergence guarantees even when the feature interaction matrix becomes singular—a practical limitation in existing GTD methods. The geometric analysis provides explicit error bounds and addresses a key stability challenge in off-policy reinforcement learning with function approximation.