XMSE-Aware Adaptive Empirical Bayes Estimation
arXiv:2606.26975v1 Announce Type: new Abstract: Empirical Bayes (EB) estimators can match the first-order asymptotic risk of maximum likelihood (ML) while behaving very differently at second order:…
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Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference
arXiv:2606.27090v1 Announce Type: new Abstract: Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies…
Ribbon: Scalable Approximation and Robust Uncertainty Quantification
arXiv:2606.27269v1 Announce Type: new Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models. Both fully Bayesian and bootstrap resampling methods provide principled…
When are likely answers right? On Sequence Probability and Correctness in LLMs
arXiv:2606.27359v1 Announce Type: new Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under…
Learning Probabilistic Filters with Strictly Proper Scoring Rules
arXiv:2606.26497v1 Announce Type: cross Abstract: Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a…
Explainable Outlier Detection for Interval-valued Data
arXiv:2606.26307v1 Announce Type: cross Abstract: Explainability is increasingly recognized as a key aspect of outlier detection. However, for complex data structures such as interval-valued data, it…
Learning from a Biased Sample
arXiv:2209.01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those…
No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference
arXiv:2505.20178v2 Announce Type: replace Abstract: Prediction-Powered Inference (PPI) is a popular strategy for combining gold-standard and possibly noisy pseudo-labels to perform statistical estimation. Prior work has…
Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters
arXiv:2512.07074v3 Announce Type: replace-cross Abstract: Statistically correcting measured cross sections for detector effects is an important step across many applications. In particle physics, this inverse problem…
A unifying view of contrastive learning, importance sampling, and bridge sampling for energy-based models
arXiv:2604.08116v2 Announce Type: replace-cross Abstract: In the last decades, energy-based models (EBMs) have become an important class of probabilistic models in which a component of the…
A probabilistic framework for online test-time adaptation
arXiv:2606.26457v1 Announce Type: new Abstract: This paper presents a probabilistic framework for online test-time adaptation problems. In them, a model is trained on labeled data but…
Random Walk on B'ezier Curves for Global Optimization
arXiv:2606.26714v1 Announce Type: new Abstract: Balancing exploration and exploitation remains a central challenge in metaheuristic optimization. To address this issue, this paper proposes B'ezier Walk Evolution…
The Red Queen G"odel Machine: Co-Evolving Agents and Their Evaluators
arXiv:2606.26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their search methods…
Three-Objective Integral R2 Subset Selection: NP-Hardness and Submodular Approximation
arXiv:2606.26591v1 Announce Type: cross Abstract: Selecting a fixed number of representative points from a finite Pareto-front approximation is a fundamental post-processing task in multiobjective optimization. This…
Surviving by Serving: Functional Relevance Drives Self-Organization in Complex Adaptive Systems
arXiv:2606.26733v1 Announce Type: cross Abstract: Complex adaptive systems often develop organized structures without centralized control. Yet the local mechanisms by which functional organization emerges and persists…
CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention
arXiv:2606.27229v1 Announce Type: cross Abstract: Recurrent models must forget in order to remember, yet the state of the art decides what to erase without consulting what…
Revisiting the Platonic Representation Hypothesis: An Aristotelian View
arXiv:2602.14486v2 Announce Type: replace-cross Abstract: The Platonic Representation Hypothesis suggests that representations from neural networks are converging to a common statistical model of reality. We show…
NervePool: A Simplicial Pooling Layer
arXiv:2305.06315v3 Announce Type: replace-cross Abstract: For deep learning problems on graph-structured data, pooling layers are important for down sampling, reducing computational cost, and to minimize overfitting.…
Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
arXiv:2606.04574v2 Announce Type: replace-cross Abstract: This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair…
Statistical Properties of the King Wen Sequence: An Anti-Habituation Structure That Does Not Improve Neural Network Training
arXiv:2604.09234v2 Announce Type: replace-cross Abstract: The King Wen sequence of the I-Ching (c. 1000 BC) orders 64 hexagrams -- states of a six-dimensional binary space --…
Equivariance and Augmentation for Bayesian Neural Networks
arXiv:2606.26273v1 Announce Type: new Abstract: Symmetries are important for many deep learning tasks, ranging from applications in the sciences to medical imaging. However, there is an…
Sutra: Tensor-Op RNNs as a Compilation Target for Vector Symbolic Architectures
arXiv:2605.20919v3 Announce Type: replace Abstract: Sutra is a typed, purely functional programming language whose compiled forward pass is a PyTorch neural network. The compiler beta-reduces the…
Dataset Usage Inference without Shadow Models or Held-out Data
arXiv:2606.26257v1 Announce Type: new Abstract: How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this…
Spurious Rewards Paradox: Mechanistically Understanding How RLVR Activates Memorization Shortcuts in LLMs
arXiv:2601.11061v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Qwen 2.5…