TARCO corrects measurement-error-induced correlated contamination in tree-aggregated compositional regression via bias-corrected estimating equations, tree-aware PSD stabilization, and sparse regularization, with finite-sample bounds and sign consistency.
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AssayBench is a new gene-ranking benchmark for phenotypic CRISPR screens that shows zero-shot generalist LLMs outperform both biology-specific LLMs and trainable baselines on adjusted nDCG.
An adaptive fused orthogonal estimator recovers latent clusters exactly with high probability and achieves pooled parametric rates plus asymptotic normality matching an oracle in semiparametric heterogeneous clustered multitask learning.
Participatory provenance auditing of Canada's AI strategy consultation shows official AI summaries exclude 15-17% of participants more than random baselines, with 33-88% exclusion for dissent clusters.
GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.
citing papers explorer
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Tree-aggregated regression for compositional data with measurement errors
TARCO corrects measurement-error-induced correlated contamination in tree-aggregated compositional regression via bias-corrected estimating equations, tree-aware PSD stabilization, and sparse regularization, with finite-sample bounds and sign consistency.
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AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents
AssayBench is a new gene-ranking benchmark for phenotypic CRISPR screens that shows zero-shot generalist LLMs outperform both biology-specific LLMs and trainable baselines on adjusted nDCG.
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Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
An adaptive fused orthogonal estimator recovers latent clusters exactly with high probability and achieves pooled parametric rates plus asymptotic normality matching an oracle in semiparametric heterogeneous clustered multitask learning.
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Participatory provenance as representational auditing for AI-mediated public consultation
Participatory provenance auditing of Canada's AI strategy consultation shows official AI summaries exclude 15-17% of participants more than random baselines, with 33-88% exclusion for dissent clusters.
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Group-Aware Matrix Estimation and Latent Subspace Recovery
GAME is a convex estimator using overlapping nuclear-norm penalties on subgroup submatrices for low-rank matrix completion with known overlapping groups, providing finite-sample guarantees on reconstruction error and subgroup subspace recovery.
- Query-efficient model evaluation using cached responses