{"total":18,"items":[{"citing_arxiv_id":"2606.24903","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Spectral Phase Diagram for Binary Few-Shot Classification: Intrinsic Dimensionality, Geometric Saturation, and Representational Diagnosis","primary_cat":"cs.LG","submitted_at":"2026-06-12T16:46:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Defines saturation index S(K) = erank(Σ̂_W^(K))/K that identifies when linear discriminant stabilizes in binary few-shot classification, with empirical phase diagram and stopping-rule AUC of 0.752 on 17 tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06724","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Synthics: Synthetic Physics-like Datasets for Machine Learning","primary_cat":"cs.LG","submitted_at":"2026-06-04T21:20:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Bayesian PCFG generates synthetic physics-like regression datasets matching eight structural features of the Feynman corpus and enabling equivalent hyperparameter tuning performance to real data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06329","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficient Mean Curvature Computation on High-Dimensional Data Manifolds","primary_cat":"cs.LG","submitted_at":"2026-06-04T16:04:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"An exact algebraic identity plus low-rank SVD and Haar-measure null-space approximation reduce per-point mean curvature cost from O(m^4) to O(k^2 m + k m p^2) with 50-300x speedups and negligible accuracy loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02740","ref_index":28,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"ScoreStop: Gradient-based early stopping using functional score tests","primary_cat":"stat.ML","submitted_at":"2026-06-01T18:05:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ScoreStop introduces a functional score test for early stopping in gradient boosting, testing the null that the current predictor minimizes population risk with a scale-invariant statistic of known asymptotic distribution.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21033","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification","primary_cat":"cs.LG","submitted_at":"2026-05-20T11:10:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Presents pseudo-polynomial DP algorithm O(W k n²) for weighted kNN Banzhaf valuation and O(n k²) for unweighted, plus Monte Carlo estimators, after proving #P-hardness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20716","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification","primary_cat":"cs.LG","submitted_at":"2026-05-20T05:15:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Path-based adaptive weighting of random forest trees via decision path patterns delivers statistically significant accuracy gains on 36 binary classification benchmarks with minimal class-recall regression.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18889","ref_index":43,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Soft Learning","primary_cat":"cs.LG","submitted_at":"2026-05-16T22:14:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"UNKNOWN","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Soft Learning optimally combines heterogeneous ML specialists via cross-validated non-negative least squares, achieving top performance on 70% of 37 datasets with formal guarantees and 72-435x CPU speedups over deep networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16041","ref_index":113,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Explainable AI Isn't Enough! Rethinking Algorithmic Contestability","primary_cat":"stat.ML","submitted_at":"2026-05-15T15:14:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10137","ref_index":31,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks","primary_cat":"stat.ML","submitted_at":"2026-05-11T07:46:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"outperform AutoML baselines on benchmark datasets. Third, the models exhibitstructural adaptivity: because the prior covers a wide range of DGPs, predictions automatically reflect whichever structure (e.g. sparsity, smoothness, nonlinearity, or heteroscedasticity) is consistent with the observed data, avoiding the misspecification that afflicts single-model-class baselines [31]. Unfortunately, while these models output PPDs that quantify uncertainty in a noisy future response, they do not directly output posterior distributions for the latent regression function, which is what is needed by TS. A potential solution comes from Bayesian predictive inference, which shows how to recover posterior parameter distributions from PPDs [8, 9]."},{"citing_arxiv_id":"2605.02609","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Gradient-Discrepancy Acquisition for Pool-Based Active Learning","primary_cat":"cs.LG","submitted_at":"2026-05-04T13:56:09+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces gradient-discrepancy acquisition criterion derived from Luo et al. 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In:Cell Reports Methods 3.4 (Apr. 2023), p. 100461. ISSN : 26672375. DOI: 10.1016/j.crmeth.2023.100461. [105] Marvin N. Wright and Andreas Ziegler. \"ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R\". In: Journal of Statistical Software 77.1 (2017), pp. 1-17. [106] Ali Zare et al. \"A Comparison between Accelerated Failure-time and Cox Proportional Hazard Models in Analyzing the Survival of Gastric Cancer Patients.\" In: Iranian journal of public health 44.8 (2015), pp. 1095-102. ISSN : 03044556. DOI: 10.1007/s00606-006-0435-8 . URL: http : / / www . ncbi . nlm . nih . gov / pubmed / 26587473 % 7B % 5C % %7D0Ahttp :"},{"citing_arxiv_id":"1907.03334","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models","primary_cat":"cs.LG","submitted_at":"2019-07-07T19:12:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A CBR system based on similarity of local explanations provides visualizations that fraud analysts at a Dutch bank found useful and easy to use for processing ML-generated fraud alerts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.03324","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Human-Grounded Evaluation of SHAP for Alert Processing","primary_cat":"cs.LG","submitted_at":"2019-07-07T17:50:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Human-grounded evaluation finds no significant performance improvement from adding SHAP explanations to model confidence scores in alert processing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}