Maximum Likelihood

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 39

All topics

Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures
Oct 1, 2026Hansheng JiangMaximum Likelihood Gaussian Mixture Models
University of Toronto
Principled MAP estimation for inverse problems: bridging the gap between convergence and performance
Sep 29, 2026Alexandre Lagier, Valentine Tosel, Anne Gagneux +2Bayesian Inverse Problems Iterative Denoising Methods
ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, Inria, LIP UMR 5668, 69342 Lyon Cedex 07, France · Univ. Bordeaux, Inria, Bordeaux INP, IMB, UMR 5251, F-33400 Talence, France · Inria, ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, LIP UMR 5668, 69342 Lyon Cedex 07, France
Error Bounds for Statistical Estimators in BTL Model with Parametric Multivariate Utility Functions
Sep 22, 2026Yicheng Li, Huifu XuMaximum Likelihood Finite-Sample
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong.
Statistical Gains from Looped Estimation under Parameter Budgets
Sep 22, 2026Xinyu Tian, Xiaotong ShenMaximum Likelihood Structural Complexity
School of Statistics, University of Minnesota
Federated Soft Clustering via Generalized Total Variation Minimization
Sep 16, 2026Shamsiiat Abdurakhmanova, Alexander JungClustering Gaussian Mixture Models
Department of Computer Science, Aalto University, Espoo, Finland
Generalized Score Matching for Parameter Estimation on Convex Domains
Sep 11, 2026Nishanth Shetty, Saisuchith Mahajan, Chandra Sekhar SeelamantulaMaximum Likelihood Uncertainty Score
Department of Electrical Engineering Indian Institute of Science, Bengaluru 560012
Artificial Rosetta Stone: Constrained Maximum A Posteriori (MAP) Reconstruction of Symbolic Raga Sequences via Order-k Markov Models
Sep 1, 2026Saanvi Raghavendran, Abhishek BhattacharjeeSymbolic Music Generation Markov
Abstract Math Institute
Stochastic complexity of vectors containing cluster structure
Aug 31, 2026Daniel Nicorici, Olli Yli-Harja, Jaakko AstolaCluster Maximum Likelihood
Medicel Oy, Keilaranta 12, FIN-02150 Espoo, Finland · Institute of Signal Processing, Tampere University of Technology P.O. Box 553, FIN-33101 Tampere, Finland
Decentralized Multitask Learning over Learned Task Graphs
Aug 27, 2026Zirui Wan, Stefan VlaskiGraph Representation Learning Decentralized Learning
Department of Electrical and Electronic Engineering, Imperial College London, UK
Scalable estimation of VARMA models
Aug 6, 2026Daniel Paulin, Victor ElviraMaximum Likelihood Autocorrelations
College of Computing & Data Science Nanyang Technological University Singapore · School of Mathematics University of Edinburgh Edinburgh, UK
On the Order-Conditional Optimality of Gaffke's Bound
Jul 25, 2026George Bissias, Erik Learned-MillerOptimality Maximum Likelihood
University of Massachusetts Amherst
Convergence analysis of a family of Zermelo-type iterations for the Bradley--Terry model
Jul 24, 2026Ruijian Han, Ding Lu, Yiming XuConvergence Maximum Likelihood
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR · Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA
The Regularization Parameter: Sparse Precision Matrix Estimation
Jul 7, 2026Aryan Eftekhari, Daniel Sergio Vega, Ernst-Jan Camiel Wit +1Maximum Likelihood Regularization
Institute of Computing, Faculty of Informatics, Universit`a della Svizzera italiana (USI), Lugano, Switzerland
A Gradient Flow Perspective on Minimum MMD Estimation
Jul 4, 2026Sophia Seulkee Kang, Louis Sharrock, Xiaoyuan Cheng +2Maximum Mean Discrepancy Maximum Likelihood
Independent Researcher · University College London
Sample Complexities of Estimating Gumbel--Max Watermark Proportions with and without Reduction to Pivotal Statistics
Jun 30, 2026Shuwen Chai, Qiaosen WangWatermarking Watermarks
Northwestern University · University of Chicago
Exact Schur-Sylvester Dimensionality Reductions for Non-Smooth Stochastic Complexity and Manifold Sampling
Jun 22, 2026Trenton Lau, Gary P. T. ChoiMaximum Likelihood Optimal Sample Complexity
Department of Mathematics, The Chinese University of Hong Kong
LangMAP: A Language-Adaptive Approach to Tokenization
Jun 22, 2026Clara Meister, Suchir Salhan, Andrzej Szablewski +3Tokenizer Multilingual Language Models
EPFL · University of Cambridge · ETH Zürich
Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection
Jun 17, 2026Richard Yi Da XuDeterminantal Point Process Spectral Decomposition
Hong Kong Baptist University · TadReamk Limited
D2HDMap: Non-visible Driveline Map Prior for Online Vectorized HD Map Prediction
Jun 17, 2026Seojun Shon, Chikao Tsuchiya, Dhaval Bhanderi +3Lane-Level Map Generation Drives
Nissan North America · Nissan Advanced Technology Center - Silicon Valley, Nissan North America, Santa Clara, CA 95014, USA
Mask, Sample, Revise: A Revisable CTMC Inference Stack for Guided Discrete Flow Matching Text-to-Speech
Jun 12, 2026Alef Iury Siqueira Ferreira, Lucas Rafael Stefanel Gris, Luiz Fernando de Araújo Vidal +4Flow-Matching Text-To-Speech Text-To-Audio
Federal University of Goi´as, Goiˆania, Brazil · Federal University of Mato Grosso, Cuiab´a, Brazil · Elsa Speak, S˜ao Paulo, Brazil
Convergence Rates for Neural-Network Estimation with Current-Status Data
Jun 8, 2026Yuan Wu, Tianhui ZhouMaximum Likelihood Neural Approximations
Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA
Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman
Jun 8, 2026Daniil Mikriukov, Ruoyu Sun, Angelos Stefanidis +2Portfolio Optimization Long-Tailed Distribution
University of Liverpool · Xi'an Jiaotong-Liverpool University · Department of Computer Science, University of Liverpool, Liverpool, United Kingdom +1
RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood
May 28, 2026Yifu ZhengSurrogate Gradient Maximum Likelihood
University of Southern California
libhmm: A Modern C++20 Library for Hidden Markov Models with Correct MLE Emission M-Steps
May 28, 2026Gary WolfmanHidden Markov Models Maximum Likelihood
Independent Researcher
Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
May 27, 2026Simon Queric, Cédric Vincent-Cuaz, Charles Bouveyron +1Maximum Likelihood Model Selection
Universit´e Cˆote d’Azur, Inria, CNRS LJAD, Maasai Nice, France · EPFL Lausanne, Switzerland · Universit´e Cˆote d’Azur, Inria, CNRS CEPAM, Maasai Nice, France
Cross-Entropy Games and Frost Training
May 26, 2026Arthur Renard, Franck Gabriel, Valentin Hartmann +1Frictive Policy Optimization Cross-Entropy Losses
Xent Labs · Xent Labs, Université Lyon 1
The Normalized Maximum Likelihood for Regular Non-Smooth Models: Measure-Theoretic Foundations and Geometric Sampling
May 23, 2026Trenton Lau, Gary P. T. ChoiMaximum Likelihood Markovian Sampling
Department of Mathematics, The Chinese University of Hong Kong, Hong Kong
Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation
May 21, 2026Benedikt Lütke Schwienhorst, Nadja Klein, Johannes LedererScore-Based Diffusion Model Maximum Likelihood
Department of Mathematics University of Hamburg Hamburg, Germany · Scientific Computing Center Karlsruhe Institute of Technology2026 Karlsruhe, Germany · Department of MathematicsMay University of Hamburg Hamburg, Germany21
Maximum Likelihood Decoding of Quantum Error Correction Codes
May 17, 2026Hanyan Cao, Ge Yan, Yuxuan Du +1Quantum Error Correction Post-Quantum Cryptography
1Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore · School of Physical and Mathematical Sciences, Nanyang Technological University,2026 Singapore, Singapore
Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
May 12, 2026Haoran Hu, Xingce WangLeast Squares Calibrated Uncertainty
School of Artificial Intelligence Beijing Normal University Beijing 100875, China
COMO: Closed-Loop Optical Molecule Recognition with Minimum Risk Training
Apr 26, 2026Zhuoqi Lyu, Qing KeChemical Structures Maximum Likelihood
Department of Data Science, College of Computing, City University of Hong Kong, Hong Kong, China
OptiMVMap: Offline Vectorized Map Construction via Optimal Multi-vehicle Perspectives
Apr 18, 2026Zedong Dan, Zijie Wang, Wei Zhang +6Lane-Level Map Generation Bird'S-Eye View
Sun Yat-sen University · Zhongguancun Academy · Shenzhen Loop Area Institute +2
Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks
Mar 25, 2026Easton Huch, Michael KeaneDiscrete Choice Models Maximum Likelihood
Postdoctoral Fellow, Johns Hopkins Carey Business School · Carey Distinguished Professor, Johns Hopkins Carey Business School
Quantum Maximum Likelihood Prediction via Hilbert Space Embeddings
Feb 20, 2026Sreejith Sreekumar, Nir WeinbergerMaximum Likelihood Concentration Bounds
It's all In the (Exponential) Family: An Equivalence between Maximum Likelihood Estimation and Control Variates for Sketching Algorithms
Jan 29, 2026Keegan Kang, Kerong Wang, Ding Zhang +3Maximum Likelihood Variance Reduction
Department of Mathematics and Statistics, Bucknell University, USA · School of Data Science, University of Virginia, USA · Department of CSE, IIT Hyderabad, India +2
The Optimization Landscape of Carathéodory Decomposition of Toeplitz Covariances
Nov 3, 2025Daniel Busbib, Ami WieselCovariance Maximum Likelihood
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
Oct 14, 2025Joanna Marks, Tim Y. J. Wang, O. Deniz AkyildizLangevin Dynamics Maximum Likelihood
Department of Mathematics, Imperial College London
A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAP
Aug 4, 2025Minh-Hai Nguyen, Edouard Pauwels, Pierre WeissBayesian Inverse Problems Gaussian Deblurring
IRIT, Toulouse University, Toulouse, France · Toulouse School of Economics, Universit´e Toulouse Capitole et IUF, Toulouse, France
A polynomial time algebraic solution to exact marginal inference in Markov Random Field models
Sep 25, 2017Ikhlef BecharProbabilistic Inference Markov
Aix Marseille Univ, Universit´e de Toulon, CNRS, ENSAM, LSIS La Garde 83957, France

Oct 1, 2026, stat.MEJ/K move · Enter open · S save

Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures

Hansheng Jiang

University of Toronto

Maximum Likelihood Gaussian Mixture Models Sparsity Random

Open paper AskPDF arXiv

The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian location mixture maximizes the likelihood over the infinite-dimensional space of mixing distributions. The maximizing mixing distribution can be nonunique, and the classical bound on its number of atoms grows linearly with the sample size

n

. We show that a vanishingly small random perturbation of the likelihood yields exact polylogarithmic sparsity. The resulting randomly reweighted NPMLE maximizes a weighted likelihood whose independent weights, taken to be Gamma in our analysis, concentrate around one as

n

grows. With high probability, it is unique, has

O\{(\log n/\log\log n)^d+\log n\}

atoms in dimension

d

, nearly maximizes the ordinary likelihood, and estimates the mixture density at a Hellinger rate that is parametric up to logarithmic factors. This sparsity holds for the estimator itself, not for an approximation of it, and requires no support penalty. The proof rests on an effective-dimension principle for positive kernel mixtures: low-dimensional variation of the fitted values controls the support of every extreme point of the set of maximizers. Numerical illustrations verify that the reweighted NPMLE has Hellinger risk and support size comparable to those of the ordinary NPMLE.

Momentum

Related topics

Latest papers 39