Density Estimation

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8 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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Estimating Mixture Distributions via Stochastic Mirror Descent
May 24, 2026Mohammadreza Ahmadypour, Tara Javidi, Farinaz KoushanfarStochastic Optimization Statistical Learning Theory
Department of Electrical and Computer Engineering University of California San Diego, San Diego, CA, USA
Generative OOD-regularized Model-based Policy Optimization
May 23, 2026Aysin Tumay, Jiahe Huang, Elise Jortberg +1Offline RL OOD Detection
University of California, San Diego · Abiomed
Symbolic Density Estimation for Discrete Distributions
May 20, 2026Ziwen Liu, Meng LiDensity Estimation
Rice University Houston, TX
Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
May 19, 2026Serhii ZabolotniiParameter Estimation Density Estimation
Department of Information, Multimedia Technologies and Design, Cherkasy State Business College, Cherkasy, 18028, Ukraine · State Scientific Research Institute of Armament and Military Equipment Testing and Certification, Cherkasy, Ukraine · Department of Cybernetics and Applied Mathematics, Uzhhorod National University, Uzhhorod, Ukraine
StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
May 15, 2026Gurjeet Jagwani, Stephen Thorp, Sinan Deger +1Continuous Normalizing Flows Flow-Based Generative Modeling
Institute of Astronomy and Kavli Institute for Cosmology, University of Cambridge, Cambridge, UK · Research Computing Services, University of Cambridge, Cambridge, UK · Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK +1
Adaptive Kernel Density Estimation with Pre-training
May 13, 2026Ruitong Zhang, Ke DengKernel Density Estimation Density Estimation
Department of Statistics and Data Science Tsinghua University Beijing, China
A Spectral Framework for Closed-Form Relative Density Estimation
May 11, 2026Francis BachDensity Ratio Estimation Spectral Methods
SIERRA · Inria - Ecole Normale Supérieure · PSL Research University
Slum Detection and Density Mapping with AlphaEarth Foundations: A Representation Learning Evaluation Across 12 Global Cities
May 11, 2026Shuyang Hou, Ziqi Liu, Haoyue Jiao +7Geospatial Foundation Models Geospatial Representation Learning
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China
Constant-Target Energy Matching: A Unified Framework for Continuous and Discrete Density Estimation
May 9, 2026Zhijun Zeng, Yixuan Jiang, Pipi Hu +1Energy-Based Models Density Estimation
Qiuzhen College Tsinghua University Beijing 100084, China · Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408, China · Yau Mathematical Sciences Center Tsinghua University, Beijing 100084, China and Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408, China
Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
May 6, 2026Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu +4Distribution Shift 3D Object Detection
Institute for Automotive Engineering, RWTH Aachen · Computer Vision Institute, RWTH Aachen
Raising the Ceiling: Better Empirical Fixation Densities for Saliency Benchmarking
May 5, 2026Susmit Agrawal, Jannis Hollman, Matthias KümmererBenchmark Design Eye Tracking
Tübingen AI Center, University of Tübingen, Germany
Amortized Vine Copulas for High-Dimensional Density and Information Estimation
Apr 22, 2026Houman SafaaiCopula Models Amortized Inference
Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University
On Bayesian Softmax-Gated Mixture-of-Experts Models
Apr 22, 2026Nicola Bariletto, Huy Nguyen, Nhat Ho +1Bayesian Mixture Models Bayesian Inference
The University of Texas at Austin
Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning
Feb 1, 2026Pietro Carlotti, Nevena Gligić, Arya FarahiEvidential Deep Learning OOD Detection
Department of Statistics and Data Sciences University of Texas at Austin
CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
Oct 29, 2025Xuchen Feng, Siyu LiaoNormalizing Flows Density Estimation
School of Integrated Circuits Sun Yat-sen University

May 24, 2026, stat.MLJ/K move · Enter open · S save

Estimating Mixture Distributions via Stochastic Mirror Descent

Mohammadreza Ahmadypour, Tara Javidi, Farinaz Koushanfar

Department of Electrical and Computer Engineering University of California San Diego, San Diego, CA, USA

Stochastic Optimization Statistical Learning Theory Density Estimation

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We revisit the classical problem of estimating an unknown distribution from its samples by fitting a mixture model that minimizes cross-entropy loss. Framing the task as a stochastic convex optimization problem over the space of

M

-component mixture distributions, we propose a family of estimators derived from the stochastic mirror descent (SMD) algorithm. This optimization-based approach provides a principled and flexible framework that generalizes traditional estimators and proposes a variety of novel estimators through the choice of Bregman divergences. A key advantage of our method is that it scales efficiently with the number of candidate components

f_i

; that is, one can employ a large set of basis distributions in the mixture model without incurring significant computational overhead. This enables richer approximations and improved estimation accuracy. Moreover, in the case of categorical distribution (discrete outcomes) our estimators do not require a strict lower bound, in other words our framework does not require the precise knowledge of the support of the distribution. We demonstrate that, under mild conditions, the proposed

\varphi

-SMD estimators achieve near-optimal convergence rates in both Kullback-Leibler (KL) divergence and

\ell_2

-norm and offer practical benefits when computation is expensive. Our numerical analysis highlights improved performance guaranties over classical estimators, particularly in terms of sample efficiency and scalability.

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