{"total":27,"items":[{"citing_arxiv_id":"2606.23838","ref_index":44,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Degeneracy Distillery","primary_cat":"cs.LG","submitted_at":"2026-06-22T18:18:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A method called the degeneracy distillery uses symbolic transformations to flatten the Fisher information matrix globally from simulations alone, identifying independent parameter combinations and reducing neural posterior estimation simulation budgets by up to 10x.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23575","ref_index":56,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression","primary_cat":"cs.LG","submitted_at":"2026-06-22T16:41:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22850","ref_index":137,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"To select or not to select: predictively consistent priors instead of model selection","primary_cat":"stat.ME","submitted_at":"2026-06-22T04:52:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20497","ref_index":47,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Interpretable Meta-Learning for Multi-Objective Chemical Search","primary_cat":"cs.CE","submitted_at":"2026-06-18T17:12:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Linear meta-learning surrogates trained across chemical objectives and auxiliary properties adapt rapidly to new multi-objective molecular searches and outperform baselines by 78% in Pareto performance on spin-crossover complexes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19148","ref_index":126,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast Computation of Free-Support Wasserstein Medians","primary_cat":"stat.CO","submitted_at":"2026-06-17T14:50:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19084","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Optimal score function estimation via derivatives constraints","primary_cat":"math.ST","submitted_at":"2026-06-17T13:55:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Sobolev-ball constraints on the hypothesis class achieve minimax rates for score estimation on the torus and, under conjecture, for generative modeling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18058","ref_index":105,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multiscale reconstruction of protein conformations from cryo-EM images","primary_cat":"eess.IV","submitted_at":"2026-06-16T15:35:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A multiscale optimization method using explicit protein backbone geometry reconstructs atomic models from cryo-EM data, showing improved RMSD and TM scores on three simulated datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12610","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Mathematics of AI 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equation for linear regression with conditionally homoskedastic non-Gaussian errors, sharing influence function and asymptotic variance c2 g2 / N.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10023","ref_index":115,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions","primary_cat":"astro-ph.CO","submitted_at":"2026-06-08T18:08:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07680","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Counting Process View of Relational Event Models: Practical Asymptotics","primary_cat":"stat.ME","submitted_at":"2026-06-04T21:47:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Reviews asymptotic normality conditions for counting-process REMs under varying limits of n and T, with simulations illustrating effects of modeling choices like windowing and log transforms on Cox-type models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00233","ref_index":82,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Density Evolution: A Multiscale View of Density Estimation","primary_cat":"math.ST","submitted_at":"2026-05-29T18:08:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20681","ref_index":154,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis","primary_cat":"stat.ME","submitted_at":"2026-05-20T03:48:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16593","ref_index":199,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients","primary_cat":"stat.AP","submitted_at":"2026-05-15T19:56:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09834","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Supercharging Bayesian Inference with Reliable AI-Informed Priors","primary_cat":"stat.ML","submitted_at":"2026-05-11T00:21:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08001","ref_index":134,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Scale selection for geometric medians on product manifolds","primary_cat":"math.ST","submitted_at":"2026-05-08T16:57:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06742","ref_index":164,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bayesian Modeling and Prediction of Generalized Contact Matrices","primary_cat":"stat.ME","submitted_at":"2026-05-07T14:30:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A Bayesian model for multi-feature contact matrices that uses tensor structures and contingency table theory to satisfy structural constraints and impute missing contact features, validated on simulations and US/German survey data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03885","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking","primary_cat":"cs.CV","submitted_at":"2026-05-05T15:45:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A mixture model with adaptive KDE and per-image cross-validation raises estimated human fixation consistency by 5-15% median log-likelihood and up to 2 AUC points over fixed-bandwidth Gaussian baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Adaptive Bandwidth MethodsAdaptive bandwidth methods address a fun- damental limitation of fixed-bandwidth KDE: a single bandwidth cannot simul- taneously resolve fine structure in dense regions and avoid noise amplification in sparse regions. Early work on nearest-neighbor methods [6,41] determined bandwidth from local data spacing, effectively adapting to local density. Abram- son [1] subsequently proposed scaling the bandwidth at each data point inversely with the square root of a pilot density estimate, yielding a simple and effective adaptive estimator. Despite their widespread use in spatial statistics, adaptive 4 bandwidth methods have not previously been applied to fixation density estima- tion. Our approach combines adaptive bandwidth estimation with the mixture"},{"citing_arxiv_id":"2605.00363","ref_index":86,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space","primary_cat":"math.ST","submitted_at":"2026-05-01T02:54:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13968","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Divisible sandpiles via random walks in random scenery","primary_cat":"math.PR","submitted_at":"2026-04-15T15:18:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"On infinite bounded-degree graphs, divisible sandpiles with i.i.d. initial masses of mean μ stabilize almost surely if μ < 1 and masses have finite p-moment for p > 3, but explode if μ ≥ 1; the conditions are nearly sharp via counterexamples on other graphs.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"o∈D K, then (10)v K(o) =E o \u0002 SτDK (o) \u0003 . 12 AHMED BOU-RABEE, YUVAL PERES, AND ECATERINA SAVA-HUSS In particular, (11)v K(o) = max \u001a 0,sup C⊆K, Cconnected o∈C X v∈C gC(v) σ(v)−1 \u0001\u001b . Proof.SinceKis finite and proper,E x[τK]<∞for everyx∈K, sov K(x)<∞. Ifx /∈K, then τK = 0 andv K(x) = 0. The same first-step decomposition as in the proof of Theorem 3.2 gives (12)v K(x) = \u0012 ζ(x) + 1 deg(x) X y∼x vK(y) \u0013+ , x∈K . LetD :=D K(o). Ifo /∈D K, thenv K(o) = 0. Ifo∈D, then the positive part in (12) is not active onD, and sinceDis a connected component ofD K, every neighbor ofDoutsideDhasv K = 0. The exit payoffx7→E x[SτD] satisfies the same equation onDwith the same zero boundary values (by conditioning on the first step), so the maximum principle givesv K(o) =E o[SτD]."},{"citing_arxiv_id":"2604.13341","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Newton's Algorithm as a Gradient Flow: A Geometric Framework for Recursive Mixture Estimation","primary_cat":"stat.ME","submitted_at":"2026-04-14T23:07:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Newton's recursive mixture estimator is a discrete gradient flow on the Fisher-Rao manifold of probability measures.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.11118","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Distributionally Robust K-Means Clustering","primary_cat":"cs.LG","submitted_at":"2026-04-13T07:32:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Distributionally robust k-means minimizes worst-case squared distance over a Wasserstein-2 ball around the empirical distribution, yielding a tractable soft-clustering algorithm with monotonic block coordinate descent and local linear convergence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06843","ref_index":59,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast and accurate noise removal by curve fitting using orthogonal polynomials","primary_cat":"physics.data-an","submitted_at":"2026-04-08T09:04:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Reformulating local polynomial fitting with orthogonal Chebyshev polynomials yields two algorithms that cut memory use, improve scalability, and deliver orders-of-magnitude better numerical accuracy than Vandermonde-based methods for Savitzky-Golay filters.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Together, they dictate the trade-off between noise suppression and signal distortion [55, 56]. Finding the optimal configuration is a non-trivial task. Many approaches can be found in the literature, ranging from manual trial-and-error approaches [57], to automated methods like grid searches [22] or noise-residual matching [58]. Statistical frameworks based on Stein's [59]Unbiased Risk Estimator (SURE) have also been developed [56, 60]. However, these optimization strategies are characterized by a significant computational bur- den, where a smoothing operation is turned into a heavy, iterative search problem. For instance, we mentionthe needforiterative numericalsolverslike Newton's method tofindoptimal smooth- ing parameters [60], the requirement for repeated evaluations across a range of candidate window"},{"citing_arxiv_id":"2604.05225","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R","primary_cat":"stat.CO","submitted_at":"2026-04-06T22:41:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"fastml is an R package that enforces leakage-free preprocessing through guarded resampling and provides a unified interface for safer automated ML including survival analysis.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"flexsurv: A platform for parametric survival modeling in R. Journal of Statistical Software, 70(8):1-33, 2016. doi: 10.18637/jss.v070.i08. J D Kalbfleisch and R L Prentice. The Statistical Analysis of Failure Time Data . Wiley, second edition, 2002. Sayash Kapoor and Arvind Narayanan. Leakage and the reproducibility crisis in machine-learning- based science. Patterns, 4(9):100804, 2023. doi: 10.1016/j.patter.2023.100804. Shachar Kaufman, Saharon Rosset, Claudia Perlich, and Ori Stitelman. Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data , 6(4), 2012. doi: 10.1145/2382577.2382579. Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan"},{"citing_arxiv_id":"2512.23476","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Sensitivity Analysis on the Sphere and a Spherical ANOVA Decomposition","primary_cat":"math.NA","submitted_at":"2025-12-29T13:59:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A parity-augmented ANOVA decomposition is established for functions on the sphere using orthogonal bases to capture geometry-induced variable dependencies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2411.10915","ref_index":70,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bias in Large Language Models: Origin, Evaluation, and Mitigation","primary_cat":"cs.CL","submitted_at":"2024-11-16T23:54:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A literature review that categorizes bias in LLMs, surveys evaluation and mitigation techniques, and discusses ethical implications.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.05381","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches","primary_cat":"econ.EM","submitted_at":"2019-07-02T08:18:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Adaptive GLM with MQLE and GP regression with UCB for dynamic insurance pricing, showing parameter convergence and regret analysis under delayed claims.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}