DiffPhD delivers a unified differentiable projective dynamics solver for heterogeneous hyperelastic elastodynamics with contact that achieves up to 10x speedup and stable convergence on 100x stiffness contrasts while preserving strict gradient accuracy.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =
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A methodological framework for separable effects analysis that distinguishes four-arm and two-arm designs, with EIF-based estimation and falsification tests.
A new adaptive ℓ₂-regularized Newton boosting algorithm for decision trees delivers global O(1/k²) convergence on general convex losses, recovering classical Newton boosting as a special case under stronger assumptions.
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DiffPhD: A Unified Differentiable Solver for Projective Heterogeneous Materials in Elastodynamics with Contact-Rich GPU-Acceleration
DiffPhD delivers a unified differentiable projective dynamics solver for heterogeneous hyperelastic elastodynamics with contact that achieves up to 10x speedup and stable convergence on 100x stiffness contrasts while preserving strict gradient accuracy.
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Separable Effects in Four-Arm and Two-Arm Designs
A methodological framework for separable effects analysis that distinguishes four-arm and two-arm designs, with EIF-based estimation and falsification tests.
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Gradient Regularized Newton Boosting Trees with Global Convergence
A new adaptive ℓ₂-regularized Newton boosting algorithm for decision trees delivers global O(1/k²) convergence on general convex losses, recovering classical Newton boosting as a special case under stronger assumptions.