Tag: distributions
-
Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss
Mitigating Long-Tailed Anomaly Score Distributions with Importance-Weighted Loss arXiv:2601.02440v1 Announce Type: new Abstract: Anomaly detection is crucial in industrial applications for identifying rare and unseen patterns to ensure system reliability. Traditional models, trained on a single class of normal data, struggle with real-world distributions where normal data exhibit diverse patterns, leading to class imbalance and…
-
Sampling from multimodal distributions with warm starts: Non-asymptotic bounds for the Reweighted Annealed Leap-Point Sampler
Sampling from multimodal distributions with warm starts: Non-asymptotic bounds for the Reweighted Annealed Leap-Point Sampler arXiv:2512.17977v1 Announce Type: new Abstract: Sampling from multimodal distributions is a central challenge in Bayesian inference and machine learning. In light of hardness results for sampling — classical MCMC methods, even with tempering, can suffer from exponential mixing times —…
-
Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations
Robust Learnability of Sample-Compressible Distributions under Noisy or Adversarial Perturbations arXiv:2506.06613v1 Announce Type: new Abstract: Learning distribution families over $mathbb{R}^d$ is a fundamental problem in unsupervised learning and statistics. A central question in this setting is whether a given family of distributions possesses sufficient structure to be (at least) information-theoretically learnable and, if so, to…
-
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions
Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions arXiv:2505.07825v1 Announce Type: new Abstract: We propose a hybrid generative model for efficient sampling of high-dimensional, multimodal probability distributions for Bayesian inference. Traditional Monte Carlo methods, such as the Metropolis-Hastings and Langevin Monte Carlo sampling methods, are effective for sampling from single-mode distributions…
-
Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry
Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry arXiv:2504.14164v1 Announce Type: new Abstract: We introduce a novel, geometry-aware distance metric for the family of von Mises-Fisher (vMF) distributions, which are fundamental models for directional data on the unit hypersphere. Although the vMF distribution is widely employed in a variety of probabilistic learning tasks involving…
-
Lessons from COVID-19: Why Probability Distributions Matter
Lessons from COVID-19: Why Probability Distributions Matter Understanding Distributions with Extremes: Probability for Data Science Series (END) Continue reading on Towards Data Science ยป Sunghyun Ahn Go to original source
-
MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle
MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle arXiv:2412.02090v1 Announce Type: new Abstract: Maximum entropy principle (MEP) offers an effective and unbiased approach to inferring unknown probability distributions when faced with incomplete information, while neural networks provide the flexibility to learn complex distributions from data. This paper proposes a novel…
-
ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation
ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation arXiv:2411.19090v1 Announce Type: new Abstract: Algorithmic bias continues to be a key concern of learning analytics. We study the statistical properties of the Absolute Between-ROC Area (ABROCA) metric. This fairness measure quantifies group-level differences in classifier performance through the absolute difference in ROC curves. ABROCA is…