{"total":12,"items":[{"citing_arxiv_id":"2605.23402","ref_index":38,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting","primary_cat":"cs.LG","submitted_at":"2026-05-22T09:13:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21197","ref_index":64,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression","primary_cat":"stat.ME","submitted_at":"2026-05-20T13:57:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17934","ref_index":65,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Conditional Predictive Inference for General Structured Data with Group Symmetries","primary_cat":"stat.ME","submitted_at":"2026-05-18T06:41:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12201","ref_index":20,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Uncertainty Quantification for LLM-based Code Generation","primary_cat":"cs.SE","submitted_at":"2026-05-12T14:40:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12195","ref_index":43,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Fair Conformal Classification via Learning Representation-Based Groups","primary_cat":"cs.LG","submitted_at":"2026-05-12T14:37:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A fair conformal classification method guarantees conditional coverage on adaptively identified subgroups defined via learned representations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08561","ref_index":6,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"CONTRA: Conformal Prediction Region via Normalizing Flow Transformation","primary_cat":"stat.ML","submitted_at":"2026-05-08T23:43:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CONTRA generates sharp multi-dimensional conformal prediction regions by defining nonconformity scores as distances from the center in the latent space of a normalizing flow.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Given a generative model for estimating the conditional density, PCP defines the non-conformity score of a data point (xi,y i)as si = min 1≤k≤K ∥yi − ˆyk i ∥, where{ ˆyi},K k=1 is a sample of the output generated from the estimated conditional density. Then the prediction region atx n+1 is obtained by generating a sample{ ˆyk n+1}K k=1 and form bCPCP(xn+1) = K[ k=1 {y:∥y− ˆyk n+1∥2 ≤s 1−α}, wheres 1−α is the⌈(1−α)(n 2 + 1)⌉-th smallest member of{s i, i∈I 2}. Note that each PCP prediction region is the union ofKballs. They have flexible, but often irregular, disconnected shapes, and are sensitive to the choice ofKandα. This brings challenge to interpreting the regions. Next, the ST-DQR method was proposed by Feldman et al. (2023). It learns an r-dimensional latent representation of the output, e.g., using the conditional variational auto-encoder (CV AE). The latent variable is encouraged to follow a unimodal distribution, so that methods like the directional quantile regression (DQR) are applicable to form convex probability regions for it. Samples are generated in the output space corresponding to points in the latent region. Calibration and the final prediction region are created similarly to the PCP. ST-DQR and our CONTRA are similar in that they both depend on latent representations, but differ substantially in the latent representation. CONTRA uses bijection and are often able to learn the latent variable to follow the Gaussian reference distribution rather closely. In contrast, the latent variable in ST-DQR is typically of lower dimension than the output, and its distribution is only coarsely similar to a reference. And this is why additional steps like DQR are needed to form latent probability regions in ST-DQR, but our CONTRA can directly use HDR of the reference Gaussian. When calibrating the regions, ST-DQR (and PCP) had to introduce yet another step: union balls around generated samples, and calibrate the common radius of the balls. Whereas CONTR"},{"citing_arxiv_id":"2605.07100","ref_index":41,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"TRACE: Transport Alignment Conformal Prediction via Diffusion and Flow Matching Models","primary_cat":"stat.ML","submitted_at":"2026-05-08T01:28:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06581","ref_index":46,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"History-Aware Conformal Prediction Sets for Censored Time-to-Event Outcomes","primary_cat":"stat.ME","submitted_at":"2026-05-07T17:11:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"HAPS constructs shorter conformal prediction sets for censored time-to-event outcomes by using time-varying covariate histories and IPCW, achieving approximate coverage among survivors with up to 75% shorter intervals in simulations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06479","ref_index":145,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Risk-Controlled Post-Processing of Decision Policies","primary_cat":"stat.ML","submitted_at":"2026-05-07T16:03:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Risk-controlled post-processing yields a threshold-structured policy that follows the baseline except where an oracle fallback sharply reduces conditional violation risk, achieving O(log n/n) expected excess risk in i.i.d. settings and exact risk control under exchangeability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06204","ref_index":10,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination","primary_cat":"stat.ML","submitted_at":"2026-05-07T13:12:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Trimming helps conformal prediction under contamination precisely when the anomaly score separates retention probabilities without biasing clean scores, otherwise the retained mixture coefficient prevents substantial decontamination.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04219","ref_index":3,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Classification-Powered Conformal Inference for Zero-inflated Outcomes","primary_cat":"stat.ME","submitted_at":"2026-05-05T19:02:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A classification-integrated conformal framework for zero-inflated outcomes that guarantees marginal coverage and asymptotic minimal length under exchangeability, independent of the underlying models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20472","ref_index":17,"ref_count":1,"confidence":0.35,"is_internal_anchor":false,"paper_title":"Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models","primary_cat":"cs.RO","submitted_at":"2026-04-22T11:58:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Temporal difference calibration aligns uncertainty estimates in vision-language-action models with their value functions for better sequential performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}