Predictively Oriented Posteriors
arXiv:2510.01915v3 Announce Type: replace-cross Abstract: We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This…
Latest
An Efficient Likelihood Ratio Test for Online Changepoint Detection in the Presence of Autocorrelation
arXiv:2607.16106v1 Announce Type: cross Abstract: Changepoint detection methods have seen considerable development in recent years, with online algorithms capable of identifying structural changes in streaming data…
A Bit of Freedom Goes a Long Way: Classical and Quantum Algorithms for Reinforcement Learning under a Generative Model
arXiv:2507.22854v3 Announce Type: replace-cross Abstract: We propose novel classical and quantum online algorithms for learning finite- and infinite-horizon Markov Decision Processes (MDPs). Our algorithms are based…
Dimension-invariant uniform consistency of the empirical spatial distribution function and its associated spatial depth estimator
arXiv:2607.16092v1 Announce Type: cross Abstract: We provide a proof that the empirical spatial distribution estimator in $mathbb R^d$ as well as the corresponding plug-in estimator of…
Honesty in Causal Forests: When It Helps and When It Hurts
arXiv:2506.13107v5 Announce Type: replace-cross Abstract: Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A…
ASK-NN: An Asymmetric Nearest-Neighbor Test that detects Distribution Drifts in Natural Language
arXiv:2607.15607v1 Announce Type: cross Abstract: Hallucinations and artificial text in LLM-generated outputs often appear as distributional deviations between prompt and response hidden-state distributions. Since prompts or…
Conformal Graph Prediction with Z-Gromov-Wasserstein Distances
arXiv:2603.02460v5 Announce Type: replace Abstract: Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph-valued prediction, principled uncertainty…
Do Generative Models Keep Time? A Time-Aware Evaluation of Synthetic Sequential Tabular Data
arXiv:2607.15606v1 Announce Type: cross Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing, yet a generator can reproduce every marginal and every foreign-key…
Improving Backward Conformal Prediction via Non-Conformity Score Transformation
arXiv:2602.01733v3 Announce Type: replace Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees. While CP yields uncontrolled…
Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis
arXiv:2607.15884v1 Announce Type: new Abstract: This work presents a game-theoretic framework for interpretable hyperparameter-objective interaction analysis rather than proposing a new optimization algorithm. In the proposed…
Deep and Probabilistic Models for Gene Regulatory Network Inference
arXiv:2607.16053v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) link transcription factor (TF) proteins to their target genes, yet reconstructing these networks from genome-wide data remains…
Cluster-Aware Matching via Laplacian Optimal Transport
arXiv:2607.16178v1 Announce Type: new Abstract: In many applications of matching, the point clouds to be matched are not merely unstructured sets of points but rather samples…
Prediction-Only Distillation in Linear and Logistic Regression
arXiv:2607.15450v1 Announce Type: cross Abstract: Self-distillation (SD) is typically studied when the student is retrained on the teacher's original training inputs. In many practical deployments, however,…
Proactive Inpatient Bed Requests for Emergency Department Admissions
arXiv:2607.15432v1 Announce Type: cross Abstract: Emergency department (ED) boarding occurs when admitted patients remain in the ED while awaiting inpatient beds. Boarding is a major driver…
On the Role of Normalization in Binary Iterative Hard Thresholding for 1-bit Compressed Sensing
arXiv:2607.15530v1 Announce Type: cross Abstract: Binary Iterative Hard Thresholding (BIHT) is a simple, yet effective, greedy method for recovering a sparse vector from one-bit sign measurements.…
Diffusion models recover accurate mixture weights despite score function insensitivity
arXiv:2607.15485v1 Announce Type: cross Abstract: Score-based generative models exhibit a puzzling behavior: they often appear to cover all modes of a target multimodal distribution and yet…
Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
arXiv:2508.01018v2 Announce Type: replace-cross Abstract: Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with…
Minimax and Bayes Optimal Best-Arm Identification
arXiv:2506.24007v5 Announce Type: replace-cross Abstract: This study investigates minimax and Bayes optimal strategies for fixed-budget best-arm identification. We consider an adaptive procedure consisting of a sampling…
Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels
arXiv:2606.16073v2 Announce Type: replace-cross Abstract: Sampling from complex, unnormalized probability densities is a fundamental challenge in Bayesian inference and probabilistic modeling. While Markov chain Monte Carlo…
Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length
arXiv:2512.07019v3 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications and actionable future…
Retraining Seeks Stable Signals
arXiv:2607.15623v1 Announce Type: new Abstract: Predictive models deployed at scale influence future data, a phenomenon called performativity. And there is always one way to cope: Train…
Evolutionary Algorithm-Guided LLMs for Physics-Informed Neural Network Design
arXiv:2607.15560v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are unusually sensitive to interacting choices of architecture, activation, loss weighting, collocation, optimization, and constraint enforcement. Large…
Transient State Reorganization and Cell Differentiation in the Developmental Dynamics of Growing Neural Cellular Automata
arXiv:2607.15726v1 Announce Type: new Abstract: Growing Neural Cellular Automata (GNCA) develop complex morphologies from a single seed cell through shared local rules, yet the internal dynamics…
Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning
arXiv:2603.00588v1 Announce Type: cross Abstract: Efficient representation learning is essential for optimal information storage and classification. However, it is frequently overlooked in artificial neural networks (ANNs).…