Parameter Estimation

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9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation
May 8, 2026Joshua Shay Kricheli, Alexander Lawrence Reid, Soumajyoti Sarkar +2Parameter Estimation Language Model Scaling Laws
Syracuse University · Amazon AGI Foundations
Estimation of Motor Unit Parameters from Surface Electromyograms using an Informed Autoencoder
May 8, 2026Kaja Balzereit, Malte Mechtenberg, Axel SchneiderParameter Estimation
Hochschule Bielefeld, University of Applied Sciences and Arts, Institute for System Dynamics
Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees
May 6, 2026Othmane Mazhar, Huyên PhamParameter Estimation Schrödinger Bridges
LPSM, Sorbonne Université and Université Paris Cité · École Polytechnique, CMAP
Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data
May 5, 2026Jordy Dal Corso, Annalena Kofler, Marco Cortellazzi +2Simulation-Based Inference Parameter Estimation
University of Trento, Trento, Italy · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Potsdam, Germany +1
Noise2Params: Unification and Parameter Determination from Noise via a Probabilistic Event Camera Model
May 4, 2026Owen Root, Julinda Mujo, Min XuEvent-Based Vision Parameter Estimation
Department of Physics and Astronomy, CUNY Hunter College, 695 Park Ave, New York, 10065, NY, USA.
Online Generalised Predictive Coding
May 4, 2026Mehran H. Z. Bazargani, Szymon Urbas, Adeel Razi +2Parameter Estimation Latent Dynamics Modeling
Insight Research Ireland Centre, UCD, Dublin, Ireland. · UCD School of Mathematics and Statistics, UCD, Belfield, Dublin, Ireland. · Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia. +5
Calibration of the underlying surface parameters for urban flood using latent variables and adjoint equation
May 3, 2026Yongfu Tian, Shan Ding, Guofeng Su +1Parameter Estimation Bayesian Inverse Problems
School of Safety Science, Tsinghua University, Beijing, 100084, China
Robust Parameter Learning for Uncertain MDPs
May 2, 2026Yannik Schnitzer, Alessandro Abate, David ParkerRobust Markov Decision Processes Parameter Estimation
University of Oxford, Oxford, United Kingdom
Incompressible Knowledge Probes: Estimating Black-Box LLM Parameter Counts via Factual Capacity
Apr 27, 2026Bojie LiParameter Estimation LLM Evaluation
Pine AI
CLT-Optimal Parameter Error Bounds for Linear System Identification
Apr 23, 2026Yichen Zhou, Stephen TuDynamical Systems Parameter Estimation
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Calibeating Prediction-Powered Inference
Apr 23, 2026Lars van der Laan, Mark Van Der LaanInverse Probability Weighting Parameter Estimation
Department of Statistics, University of Washington · Department of Statistics and Division of Biostatistics, Center for Targeted Machine Learning and Causal Inference, University of California, Berkeley
Revisiting Active Sequential Prediction-Powered Mean Estimation
Apr 20, 2026Maria-Eleni Sfyraki, Jun-Kun WangParameter Estimation Active Learning
University of California San Diego
Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
Apr 17, 2026Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa +1Parameter Estimation Predictive Maintenance
University of Wisconsin–Madison Madison, Wisconsin, USA · George Mason University Fairfax, Virginia, USA
SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
Apr 16, 2026Murat Furkan Mansur, Tufan KumbasarNeural Surrogate Modeling Parameter Estimation
AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye
Achieving $\widetilde{O}(1/ε)$ Sample Complexity for Bilinear Systems Identification under Bounded Noises
Mar 21, 2026Hongyu Yi, Chenbei Lu, Jing YuParameter Estimation Uncertainty Quantification
Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA. · Cornell University AI for Science Institute, Cornell University, Ithaca, NY, USA.
Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
Feb 12, 2026Jean-François GiovannelliParameter Estimation Bayesian Inverse Problems
Groupe Signal-Image, IMS (Univ. Bordeaux, CNRS, BINP), Talence, France
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 +3Parameter Estimation 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
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
Dec 28, 2025Niels Bracher, Lars Kühmichel, Desi R. Ivanova +3Parameter Estimation Amortized Inference
1Rensselaer Polytechnic Institute, USA · 2TU Dortmund University, Germany · University of Oxford, UK.
Diffusion Models in Simulation-Based Inference: A Tutorial Review
Dec 22, 2025Jonas Arruda, Niels Bracher, Ullrich Köthe +2Simulation-Based Inference Parameter Estimation
Bonn Center for Mathematical Life Sciences, Life & Medical Sciences Institute, University of Bonn, Germany · Center for Modeling, Simulation, & Imaging in Medicine (CeMSIM), Rensselaer Polytechnic Institute, NY, USA · Computer Vision and Learning Lab, Heidelberg University, Germany
Symmetric Linear Dynamical Systems are Learnable from Few Observations
Dec 5, 2025Minh Vu, Andrey Y. Lokhov, Marc VuffrayDynamical Systems Parameter Estimation
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
Oct 14, 2025Joanna Marks, Tim Y. J. Wang, O. Deniz AkyildizParameter Estimation Latent Variable Models
Department of Mathematics, Imperial College London
Uniform-in-time convergence bounds for Persistent Contrastive Divergence algorithms
Oct 2, 2025Paul Felix Valsecchi Oliva, O. Deniz Akyildiz, Andrew DuncanParameter Estimation Stochastic Approximation
Department of Mathematics, Imperial College London
Statistical Inference for Policy Evaluation with Temporal Difference Learning
Oct 21, 2024Weichen Wu, Gen Li, Yuting Wei +1Parameter Estimation Confidence Region Estimation
The Voleon Group, New York, NY, USA · Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR. · Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. +1
FastManly: An EM-Gradient Algorithm for Manly Mixture Models
Oct 1, 2024Katharine M. Clark, Paul D. McNicholasParameter Estimation Expectation-Maximization
Department of Mathematics & Statistics, Trent University, Ontario, Canada. · Department of Mathematics & Statistics, McMaster University, Ontario, Canada.

May 8, 2026, cs.LGJ/K move · Enter open · S save

Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation

Joshua Shay Kricheli, Alexander Lawrence Reid, Soumajyoti Sarkar, Venkata Gandikota+1

Syracuse University · Amazon AGI Foundations

Parameter Estimation Language Model Scaling Laws Scaling Laws Neural Scaling Laws

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Neural scaling laws approximate a language model's loss as a power-law function of parameter count

N

and token count

D

. Following Chinchilla-style compute-optimal training, many studies fit scaling laws from runs performed under a fixed tokens-per-parameter (TPP) ratio

k

and set

D = kN

. We show that this collinear design, combined with the empirically common near-equality of the exponents governing

N

and

D

, induces an inherent ill-conditioning in the Gauss-Newton least-squares problem: the condition number of the design grows as the inverse square of the gap between the

N

and

D

-exponents. The scale coefficients become practically unidentifiable, with confidence intervals inflating by an order of magnitude or more, yielding a ``sloppy'' model whose extrapolations degrade sharply off the training ray. We prove this for four scaling-law formalisms and derive a closed-form TPP-diversity threshold that is necessary and sufficient for well-conditioned estimation. Empirically, non-collinear designs outperform collinear ones on held-out splits with a 97.3% win rate across four laws, five corpora, multiple floating point precision modes. We further show the degeneracy is rooted in Jacobian geometry and is not an artifact of the loss function: any smooth estimation objective whose curvature involves the Jacobian inherits the same ill-conditioning.

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