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Every paper Pith has read. Search by title, abstract, or pith.
172 papers in stat.CO · page 1
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Mean shift particles approximate integrals from unnormalized densities
To discretize continually: Mean shift interacting particle systems for Bayesian inference
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Regret equals covariance between costs and decisions
Regret Equals Covariance: A Closed-Form Characterization for Stochastic Optimization
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Valiant learnability equals poly-size query compression
What is Learnable in Valiant's Theory of the Learnable?
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R package enables real-time GPU monitoring inside workflows
CudaMon: An R Package to Monitor NVIDIA GPUs, Showcased by Monitoring a GPU-accelerated Single-cell Analysis Workflow in R
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Package turns anomaly scores into calibrated p-values
Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'
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Density overlaps cause UAV attack misclassifications
XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles
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Stabilised weights speed up recursive likelihood inference
Stabilised weighted data subsampling for accelerated inference in models with recursive likelihoods
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The paper proposes a likelihood-free Bayesian filtering method using coupling-informed…
Coupling-Informed Transport Maps for Bayesian Filtering in Nonlinear Dynamical Systems
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Neural nets learn time series clusters from simulations
Amortized Neural Clustering of Time Series based on Statistical Features
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Bayesian model represents brain networks as mixtures of latent templates
A Bayesian Adaptive Latent Mixture Model for Zero-Inflated Weighted Brain Connectome Analysis
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Array-RQMC cuts walk-on-spheres variance by up to 2290 times
Walk on spheres and Array-RQMC
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Adaptive multi-marginal couplings cut MCMC meeting times by half
Multi-Marginal Couplings for Metropolis-Hastings
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Entropy-maximizing resampling mixes across disconnected manifolds
Manifold Sampling via Entropy Maximization
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Surrogates estimate time-dependent failure probabilities efficiently
Time-variant reliability using time-dependent surrogate models
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Self-predicted data calibrates Bayesian regression better than Laplace
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
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Bayesian mixture clusters mixed health outcomes with low-rank regressions
Bayesian low-rank latent-cluster regression for mixed health outcomes
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Coupled noises lift diversity in diffusion batches at zero added cost
Couple to Control: Joint Initial Noise Design in Diffusion Models
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The paper develops a polynomial-time algorithm using semidefinite programming relaxation…
Efficient Robust Constrained Signal Detection via Kolmogorov Width Approximations
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Predictive resampling yields exact Bayesian posteriors
Variational predictive resampling
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VPR with mean-field predictives matches exact posteriors
Variational predictive resampling
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Writer monads automate MCMC kernel composition
gemlib.mcmc: composable kernels for Metropolis-within-Gibbs sampling schemes
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MCMC reaches O(ε²) error for time-changed SDE parameters at O(ε^{-2} log²ε) cost
Parameter Estimation for Partially Observed Time-Changed SDEs
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Metropolis-Hastings steps fix discretization bias in diffusion correctors
Metropolis-Adjusted Diffusion Models
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Random forest surrogate cuts likelihood evaluations in phylogenetic SMC
Accelerating Bayesian Phylogenetic Inference via Delayed Acceptance Sequential Monte Carlo with Random Forest Surrogates
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Shared parametric value function scales RL measurement to large tasks
Reinforcement Learning Measurement Model
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GPU solver speeds up entropic optimal transport calculations
cuRegOT: A GPU-Accelerated Solver for Entropic-Regularized Optimal Transport
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RQMC in walk-on-spheres beats Monte Carlo variance rates
Randomized quasi-Monte Carlo for walk on spheres
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Rolling calibration window optimizes conformal coverage for time series
Rolling-Origin Conformal Prediction under Local Stationarity and Weak Dependence
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Sampling varied commonsense proofs improves AI judgment of likely truths
Abductive Reasoning with Probabilistic Commonsense
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Differentiable relaxation recovers latent partial orders from linear traces
A Differentiable Bayesian Relaxation for Latent Partial-Order Inference
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QUBO reformulation finds higher-quality splits for regression trees
QUBO-Based Calibration for Regression Trees
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Time-position preconditioner unifies mode coverage and local exploration
Time-Inhomogeneous Preconditioned Langevin Dynamics
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Pretrained transformer solves PU classification in one forward pass
In-Context Positive-Unlabeled Learning
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Metaverse framework proposed for immersive statistical education
Welcome to the Statverse: A Metaverse for Data Science
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Direct sampling replaces Metropolis for global scale in sparse regression
Spectral Collapsed Gibbs Sampler for Bayesian Sparse Regression
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Symmetry-aware nets learn non-stationary GP kernels scalably
Permutation-preserving Functions and Neural Vecchia Covariance Kernels
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Decomposing coefficients by graph nodes yields stable doubly sparse regression
Proximal Projection for Doubly Sparse Regularized Models
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High-dimensional statistics connects to optimization and random matrices
High-Dimensional Statistics: Reflections on Progress and Open Problems
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Diffusion on incidence matrices generates better hypergraphs
Hypergraph Generation via Structured Stochastic Diffusion
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Penalized KLIC curbs over-selection of complex GMM models in longitudinal data
Penalized KLIC Model Selection for the Generalized Method of Moments in Longitudinal Data with Time-Dependent Covariates
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HIMCE cuts imputation error and halves MICE runtime in high dimensions
HIMCE: High-dimensional multiple imputation via covariance-mode updating for neuroimaging and spatiotemporal blocks
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Neural network assigns climate probabilities across the Sahara
Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks
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Power can fall when adding more permutations to Monte Carlo tests
More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests
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Joint amortized VI improves Bayesian predictive accuracy
Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification
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Covariance decomposition scales multi-fidelity spatio-temporal GPs
A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models
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Nonparametric Hawkes model tops prediction for clustered extremes
Bayesian Modelling of Nonstationary Extreme Values Using a Nonparametric Hawkes Process
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Bayesian recursions track if a process is currently in control
Sequential Bayesian Monitoring for Recoverable and Drifting Processes
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Kernel discrepancy defines intrinsic ESS for manifold MCMC
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
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AI agents rate psychiatric symptoms better than humans on tricky cases
ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms