{"total":10,"items":[{"citing_arxiv_id":"2605.21899","ref_index":5,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit","primary_cat":"stat.CO","submitted_at":"2026-05-21T02:13:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Theoretical analysis of multiproposal MCMC in the infinite proposal limit using involutive theory yields new methods and inter-method relationships.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.05493","ref_index":157,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A renormalization-group inspired lattice-based framework for piecewise generalized linear models","primary_cat":"stat.ME","submitted_at":"2026-05-06T22:27:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01676","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling","primary_cat":"stat.ML","submitted_at":"2026-05-03T02:22:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MissBGM jointly models data generation and missingness in a Bayesian neural generative framework to produce consistent imputations with principled posterior uncertainty.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20723","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Tokenised Flow Matching for Hierarchical Simulation Based Inference","primary_cat":"cs.LG","submitted_at":"2026-04-22T16:07:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TFMPE combines likelihood factorisation with tokenised flow matching to enable efficient hierarchical SBI from single-site simulations, producing well-calibrated posteriors at lower computational cost on a new benchmark and real models.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"parameters, making it a natural setting for tokenised amortised inference. The observations are synthetic data generated by a validated 1D CFD solver calibrated to real arterial geometries, rather than real patient measurements. Governing equations and vessel model.We use a standard 1D pulse-wave model for area A(x, t)and flowQ(x, t)on each vessel: ∂A ∂t + ∂Q ∂x = 0,(8) ∂Q ∂t + ∂ ∂x \u0012 α Q2 A \u0013 + A ρ ∂p ∂x =− KRQ A ,(9) with momentum-flux coefficientα= 1in our solver. Pressure is related to area by p(A)−p ext = β A0 \u0010√ A− p A0 \u0011 ,(10) 23 Figure 7: Posterior consistency measured byℓ-C2ST (lower is better) across the hierarchical SBI benchmark for TFMPE ablations. Each experiment is described in Section A.5. All results are"},{"citing_arxiv_id":"2604.13457","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Excited-State Quantum Chemistry on Qumode-Based Processors via Variational Quantum Deflation","primary_cat":"quant-ph","submitted_at":"2026-04-15T04:24:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"QumVQD enables excited-state quantum chemistry calculations on bosonic qumode hardware by enforcing particle-number symmetry and using Hamiltonian fragmentation, achieving chemical accuracy on H2 and spectroscopic accuracy on vibrational modes with far fewer entangling gates than qubit equivalents.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2302.08724","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Piecewise Deterministic Markov Processes for Bayesian Neural Networks","primary_cat":"stat.ML","submitted_at":"2023-02-17T06:38:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces an adaptive thinning scheme to make PDMP-based MCMC feasible for Bayesian inference in neural networks by handling model-specific IPPs efficiently.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2006.12024","ref_index":111,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Bayesian Neural Networks: An Introduction and Survey","primary_cat":"stat.ML","submitted_at":"2020-06-22T06:30:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"A survey introducing Bayesian Neural Networks and comparing approximate inference methods to enable uncertainty quantification in neural network predictions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1912.11554","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro","primary_cat":"stat.ML","submitted_at":"2019-12-24T22:09:36+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"NumPyro delivers a JIT-compilable iterative NUTS sampler by composing Pyro effect handlers with JAX transformations, achieving faster performance than prior implementations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1912.01603","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dream to Control: Learning Behaviors by Latent Imagination","primary_cat":"cs.LG","submitted_at":"2019-12-03T18:57:16+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Dreamer learns to control from images by imagining and optimizing behaviors in a learned latent world model, outperforming prior methods on 20 visual tasks in data efficiency and final performance.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"As derived in Appendix B, the bound includes reconstruction terms for observations and rewards and a KL regularizer. The expectation is taken under the dataset and representation model, JREC .= Ep (∑ t ( J t O + J t R + J t D )) + const J t O .= ln q(ot | st) J t R .= ln q(rt | st) J t D .= −β KL ( p(st | st−1, at−1, ot)  q(st | st−1, at−1) ) . (10) We implement the transition model as a recurrent state space model (RSSM; Hafner et al., 2018), the representation model by combining the RSSM with a convolutional neural network (CNN; LeCun et al., 1989) applied to the image observation, the observation model as a transposed CNN, and the reward model as a dense network. The combined parameter vector θ is updated by stochastic"},{"citing_arxiv_id":"1906.11700","ref_index":2,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Efficient algorithms for modifying and sampling from a categorical distribution","primary_cat":"cs.DS","submitted_at":"2019-06-27T14:48:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Huffman tree data structure supports O(log n) sampling and modification of categorical distributions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}