{"total":196,"rows":[{"id":"2112.08866","title":"Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks","created":"2021-12-16","cited_by_count":49,"area":null},{"id":"2110.15767","title":"Adversarial Robustness with Semi-Infinite Constrained Learning","created":"2021-10-29","cited_by_count":39,"area":null},{"id":"2208.11866","title":"NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators","created":"2022-08-25","cited_by_count":33,"area":null},{"id":"2107.02926","title":"Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors","created":"2021-07-06","cited_by_count":29,"area":null},{"id":"2108.03782","title":"Pathfinder: Parallel quasi-Newton variational inference","created":"2021-08-09","cited_by_count":26,"area":null},{"id":"2208.07743","title":"Langevin Diffusion Variational Inference","created":"2022-08-16","cited_by_count":26,"area":null},{"id":"2107.11820","title":"A Survey of Monte Carlo Methods for Parameter Estimation","created":"2021-07-25","cited_by_count":23,"area":null},{"id":"2103.10153","title":"A Probabilistic State Space Model for Joint Inference from Differential Equations and Data","created":"2021-03-18","cited_by_count":21,"area":null},{"id":"2202.04599","title":"Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo","created":"2022-02-09","cited_by_count":19,"area":null},{"id":"2202.10847","title":"UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography","created":"2022-02-22","cited_by_count":19,"area":null},{"id":"2201.05561","title":"Probabilistic Mass Mapping with Neural Score Estimation","created":"2022-01-14","cited_by_count":17,"area":null},{"id":"2102.07542","title":"High-Dimensional Gaussian Process Inference with Derivatives","created":"2021-02-15","cited_by_count":16,"area":null},{"id":"2302.06584","title":"Thermodynamic AI and the fluctuation frontier","created":"2023-02-09","cited_by_count":15,"area":null},{"id":"2409.09787","title":"BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching","created":"2024-09-15","cited_by_count":15,"area":null},{"id":"2112.12194","title":"Surrogate Likelihoods for Variational Annealed Importance Sampling","created":"2021-12-22","cited_by_count":14,"area":null},{"id":"2408.13230","title":"Amortized Bayesian Multilevel Models","created":"2024-08-23","cited_by_count":14,"area":null},{"id":"2203.14849","title":"Safe Active Learning for Multi-Output Gaussian Processes","created":"2022-03-28","cited_by_count":13,"area":null},{"id":"2306.04952","title":"Entropy-based Training Methods for Scalable Neural Implicit Sampler","created":"2023-06-08","cited_by_count":13,"area":null},{"id":"2105.10470","title":"Geometric variational inference","created":"2021-05-21","cited_by_count":12,"area":null},{"id":"2108.04085","title":"Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data","created":"2021-08-05","cited_by_count":12,"area":null},{"id":"2202.00755","title":"Lagrangian Manifold Monte Carlo on Monge Patches","created":"2022-02-01","cited_by_count":11,"area":null},{"id":"2403.13748","title":"Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs","created":"2024-03-20","cited_by_count":11,"area":null},{"id":"2406.16689","title":"Coding schemes in neural networks learning classification tasks","created":"2024-06-24","cited_by_count":11,"area":null},{"id":"2201.11137","title":"Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization","created":"2022-01-26","cited_by_count":10,"area":null},{"id":"2203.09940","title":"Alleviating Adversarial Attacks on Variational Autoencoders with MCMC","created":"2022-03-18","cited_by_count":10,"area":null},{"id":"2502.06335","title":"Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks","created":"2025-02-10","cited_by_count":10,"area":null},{"id":"2101.06070","title":"Efficient Semi-Implicit Variational Inference","created":"2021-01-15","cited_by_count":9,"area":null},{"id":"2104.08438","title":"Bayesian graph convolutional neural networks via tempered MCMC","created":"2021-04-17","cited_by_count":9,"area":null},{"id":"2107.00140","title":"Applications of the Free Energy Principle to Machine Learning and Neuroscience","created":"2021-06-30","cited_by_count":9,"area":null},{"id":"2111.01409","title":"Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters","created":"2021-11-02","cited_by_count":9,"area":null},{"id":"2301.07609","title":"Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification","created":"2023-01-18","cited_by_count":9,"area":null},{"id":"2312.16429","title":"GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework","created":"2023-12-27","cited_by_count":9,"area":null},{"id":"2404.15253","title":"GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo","created":"2024-04-23","cited_by_count":9,"area":null},{"id":"2405.15926","title":"Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers","created":"2024-05-24","cited_by_count":9,"area":null},{"id":"2410.11067","title":"Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix","created":"2024-10-14","cited_by_count":9,"area":null},{"id":"2312.03878","title":"Domain constraints improve risk prediction when outcome data is missing","created":"2023-12-06","cited_by_count":8,"area":null},{"id":"2406.03154","title":"Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation","created":"2024-06-05","cited_by_count":8,"area":null},{"id":"2110.00610","title":"Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions","created":"2021-10-01","cited_by_count":7,"area":null},{"id":"2207.02189","title":"Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time","created":"2022-07-05","cited_by_count":7,"area":null},{"id":"2306.04895","title":"Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty","created":"2023-06-08","cited_by_count":7,"area":null},{"id":"2308.14945","title":"Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals","created":"2023-08-28","cited_by_count":7,"area":null},{"id":"2402.17911","title":"Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows","created":"2024-02-27","cited_by_count":7,"area":null},{"id":"2407.04617","title":"Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation","created":"2024-07-05","cited_by_count":7,"area":null},{"id":"2407.20444","title":"Importance Corrected Neural JKO Sampling","created":"2024-07-29","cited_by_count":7,"area":null},{"id":"2112.01586","title":"HMC with Normalizing Flows","created":"2021-12-02","cited_by_count":6,"area":null},{"id":"2208.04506","title":"Second Order Ensemble Langevin Method for Sampling and Inverse Problems","created":"2022-08-09","cited_by_count":6,"area":null},{"id":"2210.11925","title":"Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo","created":"2022-10-21","cited_by_count":6,"area":null},{"id":"2312.04711","title":"Luck, skill, and depth of competition in games and social hierarchies","created":"2023-12-07","cited_by_count":6,"area":null},{"id":"2402.04933","title":"Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits","created":"2024-02-07","cited_by_count":6,"area":null},{"id":"2410.09355","title":"On Divergence Measures for Training GFlowNets","created":"2024-10-12","cited_by_count":6,"area":null},{"id":"2101.03271","title":"HypoSVI: Hypocenter inversion with Stein variational inference and Physics Informed Neural Networks","created":"2021-01-09","cited_by_count":5,"area":null},{"id":"2110.14625","title":"Entropy-based adaptive Hamiltonian Monte Carlo","created":"2021-10-27","cited_by_count":5,"area":null},{"id":"2111.11510","title":"Bootstrap Your Flow","created":"2021-11-22","cited_by_count":5,"area":null},{"id":"2203.10592","title":"Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents","created":"2022-03-20","cited_by_count":5,"area":null},{"id":"2204.06043","title":"A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms","created":"2022-04-12","cited_by_count":5,"area":null},{"id":"2207.03859","title":"Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study","created":"2022-07-08","cited_by_count":5,"area":null},{"id":"2210.07278","title":"Meta-Uncertainty in Bayesian Model Comparison","created":"2022-10-13","cited_by_count":5,"area":null},{"id":"2311.10945","title":"An Empirical Bayes Framework for Open-Domain Dialogue Generation","created":"2023-11-18","cited_by_count":5,"area":null},{"id":"2406.14838","title":"Bayesian neural networks for predicting uncertainty in full-field material response","created":"2024-06-21","cited_by_count":5,"area":null},{"id":"2410.06978","title":"Mixing of the No-U-Turn Sampler and the Geometry of Gaussian Concentration","created":"2024-10-09","cited_by_count":5,"area":null},{"id":"2412.08824","title":"Disentangling impact of capacity, objective, batchsize, estimators, and step-size on flow VI","created":"2024-12-11","cited_by_count":5,"area":null},{"id":"2509.01082","title":"RefineStat: Efficient Exploration for Probabilistic Program Synthesis","created":"2025-09-01","cited_by_count":5,"area":null},{"id":"2509.13717","title":"A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks","created":"2025-09-17","cited_by_count":5,"area":null},{"id":"2105.02845","title":"A Unifying and Canonical Description of Measure-Preserving Diffusions","created":"2021-05-06","cited_by_count":4,"area":null},{"id":"2106.10238","title":"Nonparametric Hamiltonian Monte Carlo","created":"2021-06-18","cited_by_count":4,"area":null},{"id":"2203.04432","title":"Variational Inference with Locally Enhanced Bounds for Hierarchical Models","created":"2022-03-08","cited_by_count":4,"area":null},{"id":"2204.09039","title":"A stochastic Stein Variational Newton method","created":"2022-04-19","cited_by_count":4,"area":null},{"id":"2206.01545","title":"Mesh-free Eulerian Physics-Informed Neural Networks","created":"2022-06-03","cited_by_count":4,"area":null},{"id":"2208.00579","title":"Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization","created":"2022-08-01","cited_by_count":4,"area":null},{"id":"2211.02476","title":"Sparse Gaussian Process Hyperparameters: Optimize or Integrate?","created":"2022-11-04","cited_by_count":4,"area":null},{"id":"2302.09159","title":"Bayesian Quantification with Black-Box Estimators","created":"2023-02-17","cited_by_count":4,"area":null},{"id":"2306.00352","title":"Improving Energy Conserving Descent for Machine Learning: Theory and Practice","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2307.03460","title":"On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers","created":"2023-07-07","cited_by_count":4,"area":null},{"id":"2309.13593","title":"Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling","created":"2023-09-24","cited_by_count":4,"area":null},{"id":"2401.16657","title":"Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo","created":"2024-01-30","cited_by_count":4,"area":null},{"id":"2402.08062","title":"Avoiding Catastrophe in Online Learning by Asking for Help","created":"2024-02-12","cited_by_count":4,"area":null},{"id":"2407.00761","title":"Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models","created":"2024-06-30","cited_by_count":4,"area":null},{"id":"2407.15110","title":"Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models","created":"2024-07-21","cited_by_count":4,"area":null},{"id":"2408.09140","title":"Learning to Explore for Stochastic Gradient MCMC","created":"2024-08-17","cited_by_count":4,"area":null},{"id":"2501.17737","title":"Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiation","created":"2025-01-29","cited_by_count":4,"area":null},{"id":"2108.11579","title":"Modeling Item Response Theory with Stochastic Variational Inference","created":"2021-08-26","cited_by_count":3,"area":null},{"id":"2206.04663","title":"Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry","created":"2022-06-09","cited_by_count":3,"area":null},{"id":"2211.04752","title":"Bayesian Neural Networks for Macroeconomic Analysis","created":"2022-11-09","cited_by_count":3,"area":null},{"id":"2311.08594","title":"Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency","created":"2023-11-14","cited_by_count":3,"area":null},{"id":"2312.06177","title":"Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems","created":"2023-12-11","cited_by_count":3,"area":null},{"id":"2405.09817","title":"Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data","created":"2024-05-16","cited_by_count":3,"area":null},{"id":"2501.16587","title":"HopCast: Calibration of Autoregressive Dynamics Models","created":"2025-01-27","cited_by_count":3,"area":null},{"id":"2502.03279","title":"Posterior SBC: Simulation-Based Calibration Checking Conditional on Data","created":"2025-02-05","cited_by_count":3,"area":null},{"id":"2504.02627","title":"Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers","created":"2025-04-03","cited_by_count":3,"area":null},{"id":"2511.01064","title":"Generalized Guarantees for Variational Inference in the Presence of Even and Elliptical Symmetry","created":"2025-11-02","cited_by_count":3,"area":null},{"id":"2605.10378","title":"Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation","created":"2026-05-11","cited_by_count":3,"area":null},{"id":"2202.05621","title":"Nonlinear MCMC for Bayesian Machine Learning","created":"2022-02-11","cited_by_count":2,"area":null},{"id":"2208.06120","title":"Bayesian Inference with Latent Hamiltonian Neural Networks","created":"2022-08-12","cited_by_count":2,"area":null},{"id":"2209.13716","title":"Hamiltonian Adaptive Importance Sampling","created":"2022-09-27","cited_by_count":2,"area":null},{"id":"2210.00462","title":"Improved Stein Variational Gradient Descent with Importance Weights","created":"2022-10-02","cited_by_count":2,"area":null},{"id":"2307.13763","title":"Sobolev Space Regularised Pre Density Models","created":"2023-07-25","cited_by_count":2,"area":null},{"id":"2309.13950","title":"Local and Global Trend Bayesian Exponential Smoothing Models","created":"2023-09-25","cited_by_count":2,"area":null},{"id":"2312.01577","title":"RJHMC-Tree for Exploration of the Bayesian Decision Tree Posterior","created":"2023-12-04","cited_by_count":2,"area":null},{"id":"2401.05244","title":"Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks","created":"2024-01-10","cited_by_count":2,"area":null},{"id":"2406.04859","title":"Stochastic full waveform inversion with deep generative prior for uncertainty quantification","created":"2024-06-07","cited_by_count":2,"area":null}],"next_cursor":"100"}