GSS replaces tree-based MCTS with shared successor state layers and SNIS backups, proving polynomial horizon sample complexity under density overlap conditions.
JAX : composable transformations of P ython+ N um P y programs
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Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
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Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning
GSS replaces tree-based MCTS with shared successor state layers and SNIS backups, proving polynomial horizon sample complexity under density overlap conditions.
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Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.