mailitics

Tag: distribution

  • Dissecting Performative Prediction: A Comprehensive Survey

    Dissecting Performative Prediction: A Comprehensive Survey arXiv:2602.10176v1 Announce Type: new Abstract: The field of performative prediction had its beginnings in 2020 with the seminal paper “Performative Prediction” by Perdomo et al., which established a novel machine learning setup where the deployment of a predictive model causes a distribution shift in the environment, which in turn…

  • Mass Distribution versus Density Distribution in the Context of Clustering

    Mass Distribution versus Density Distribution in the Context of Clustering arXiv:2601.10759v1 Announce Type: new Abstract: This paper investigates two fundamental descriptors of data, i.e., density distribution versus mass distribution, in the context of clustering. Density distribution has been the de facto descriptor of data distribution since the introduction of statistics. We show that density distribution…

  • Classification Imbalance as Transfer Learning

    Classification Imbalance as Transfer Learning arXiv:2601.10630v1 Announce Type: new Abstract: Classification imbalance arises when one class is much rarer than the other. We frame this setting as transfer learning under label (prior) shift between an imbalanced source distribution induced by the observed data and a balanced target distribution under which performance is evaluated. Within this…

  • Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models

    Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models arXiv:2512.21593v1 Announce Type: new Abstract: Diffusion models have become a central tool in deep generative modeling, but standard formulations rely on a single network and a single diffusion schedule to transform a simple prior, typically a standard normal distribution, into the target…

  • Beyond Uncertainty Sets: Leveraging Optimal Transport to Extend Conformal Predictive Distribution to Multivariate Settings

    Beyond Uncertainty Sets: Leveraging Optimal Transport to Extend Conformal Predictive Distribution to Multivariate Settings arXiv:2511.15146v1 Announce Type: new Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. A candidate output is included in the prediction set if its non-conformity score is not considered extreme relative to the scores observed on…

  • One-shot Conditional Sampling: MMD meets Nearest Neighbors

    One-shot Conditional Sampling: MMD meets Nearest Neighbors arXiv:2509.25507v1 Announce Type: new Abstract: How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in computer vision, approximate posterior sampling in simulation-based inference,…

  • Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective

    Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective arXiv:2505.14808v1 Announce Type: new Abstract: This work aims to demystify the out-of-distribution (OOD) capabilities of in-context learning (ICL) by studying linear regression tasks parameterized with low-rank covariance matrices. With such a parameterization, we can model distribution shifts as a varying angle between the subspace of the…

  • Evaluating Uncertainty in Deep Gaussian Processes

    Evaluating Uncertainty in Deep Gaussian Processes arXiv:2504.17719v1 Announce Type: new Abstract: Reliable uncertainty estimates are crucial in modern machine learning. Deep Gaussian Processes (DGPs) and Deep Sigma Point Processes (DSPPs) extend GPs hierarchically, offering promising methods for uncertainty quantification grounded in Bayesian principles. However, their empirical calibration and robustness under distribution shift relative to baselines…

  • Mastering the Poisson Distribution: Intuition and Foundations

    Mastering the Poisson Distribution: Intuition and Foundations You’ve probably used the normal distribution one or two times too many. We all have — It’s a true workhorse. But sometimes, we run into problems. For instance, when predicting or forecasting values, simulating data given a particular data-generating process, or when we try to visualise model output…

  • Conformal Prediction under L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations

    Conformal Prediction under L’evy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations arXiv:2502.14105v1 Announce Type: new Abstract: Conformal prediction provides a powerful framework for constructing prediction intervals with finite-sample guarantees, yet its robustness under distribution shifts remains a significant challenge. This paper addresses this limitation by modeling distribution shifts using L’evy-Prokhorov (LP) ambiguity sets, which…

  • Method of Moments Estimation with Python Code

    Method of Moments Estimation with Python Code Let’s say you are in a customer care center, and you would like to know the probability distribution of the number of calls per minute, or in other words, you want to answer the question: what is the probability of receiving zero, one, two, … etc., calls per…

  • Analog Bayesian neural networks are insensitive to the shape of the weight distribution

    Analog Bayesian neural networks are insensitive to the shape of the weight distribution arXiv:2501.05564v1 Announce Type: cross Abstract: Recent work has demonstrated that Bayesian neural networks (BNN’s) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of magnitude energy savings compared to the standard digital implementations. However, while Gaussians…

  • Method of Moments Estimation with Python Code

    Method of Moments Estimation with Python Code How to understand and implement the estimator from scratch Photo by Petr Macháček on Unsplash Let’s say you are in a customer care center, and you would like to know the probability distribution of the number of calls per minute, or in other words, you want to answer the question:…

  • How Neural Networks Learn: A Probabilistic Viewpoint

    How Neural Networks Learn: A Probabilistic Viewpoint Understanding loss functions for training neural networks Machine learning is very hands-on, and everyone charts their own path. There isn’t a standard set of courses to follow, as was traditionally the case. There’s no ‘Machine Learning 101,’ so to speak. However, this sometimes leaves gaps in understanding. If you’re…

  • Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution

    Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution arXiv:2412.05759v1 Announce Type: new Abstract: Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to…