Sparse Recovery

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Honest Physical-Support Inference after Latent Dictionary Learning: Collision Singularities and Minimax Resolution
Jul 18, 2026Guan-Ju PengSparse Recovery Dictionary Learning
Institute of Data Science and Information Computing, National Chung Hsing University.
Demixing Sparse Signals from Nonlinear Observations using Generalized Non-convex Regularization
Jul 12, 2026Raziyeh TakbiriSparse Recovery Nonconvex Optimization
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Statistical inverse learning and $\ell^1$ -regularization
Jul 8, 2026Abhishake Rastogi, Tatiana A. Bubba, Tapio Helin +1Sparse Recovery Statistical Learning Theory
Department of Computational Engineering, LUT University, Lappeenranta, Finland. · Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy.
The Regularization Parameter: Sparse Precision Matrix Estimation
Jul 7, 2026Aryan Eftekhari, Daniel Sergio Vega, Ernst-Jan Camiel Wit +1Graph Structure Learning Parameter Estimation
Institute of Computing, Faculty of Informatics, Universit`a della Svizzera italiana (USI), Lugano, Switzerland
Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability
Jul 2, 2026Rodrigo Mendoza-SmithSparse Recovery Activation Sparsity
Independent Researcher.
Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning
Jun 30, 2026Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas LundellSparse Recovery Non-IID Federated Learning
Department of Engineering and Information Technology, Åbo Akademi University
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection
Jun 29, 2026Jan Stenner, Hans Harder, Sebastian PeitzSparse Recovery Multi-Agent Control
Universität Paderborn, Pohlweg 51, 33098 Paderborn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Joseph-von-Fraunhofer-Straße 25, 44227 Dortmund, Germany · TU Dortmund, Joseph-von-Fraunhofer-Straße 25, 44227 Dortmund, Germany
Adaptive Iterative Hard Thresholding for Online High-dimensional Quantile Regression
Jun 26, 2026Zitian Zhou, Nan LinSparse Recovery Quantile Regression
Washington University in St. Louis
Accelerating Hierarchical Sparse Predictive Coding with Hybrid Amortized Inference
Jun 26, 2026Kazuhisa FujitaSparse Recovery Efficient Inference
Department of Clinical Engineering, Komatsu University, 10-10 Doihara-Machi, Komatsu, Ishikawa, Japan 923-0921
Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
Jun 26, 2026Shixiang Liu, Hanming YangRobust Regression Sparse Recovery
School of Statistics, Renmin University of China · Institute of Statistics and Big Data, Renmin University of China
Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications
Jun 23, 2026Arindam Sengupta, Paul Jeanney, Ricardo Vinuesa +2Sparse Recovery Tensor Decomposition
ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros, 3, Madrid, 28040, Spain · Ove Arup, C. de Alfonso XI, 12, Retiro, Madrid, 28014, Spain · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, United States
Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models
Jun 15, 2026Ahmed Karam Eldaly, Matteo Figini, Daniel C. AlexanderSparse Recovery Uncertainty Quantification
Department of Computer Science, University of Exeter, Exeter EX4 4QF, U.K. · UCL Hawkes Institute, Department of Computer Science, University College London, London, U.K.
Deep Unfolded Latent Optimally Partitioned-l2/l1 Networks for Data-driven Block-Sparse Recovery
Jun 10, 2026Takanobu Furuhashi, Hidekata Hontani, Qibin Zhao +1Sparse Recovery Deep Unfolding
Nagoya Institute of Technology · RIKEN Center for Advanced Intelligence Project
Illumination-Invariant Anomaly Detection for Sub-Canopy UAV Multispectral Point Clouds
Jun 8, 2026Likun Chen, Yanfeng Gu, Xian LiSparse Recovery Unsupervised Anomaly Detection
School of Electronics and Information Engineering Harbin Institute of Technology Harbin, China
Learning solution operators of PDEs with sparse approximation methods
Jun 4, 2026Sebastian Neumayer, Daniel Potts, Fabian TaubertSparse Recovery PDE Operator Learning
Sebastian Neumayer · Daniel Potts · Fabian Taubert
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Jun 3, 2026Rishit Dagli, Abir Harrasse, Luke Zhang +4Sparse Recovery Data Selection
Jinesis AI Lab, University of Toronto & Vector Institute · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Thoughtworks +3
Joint Model and Data Sparsification via the Marginal Likelihood
May 28, 2026Alexander Timans, Thomas Möllenhoff, Christian A. Naesseth +2Dataset Pruning Robust Regression
1UvA-Bosch Delta Lab, University of Amsterdam · 2RIKEN Center for AI Project, Tokyo, Japan · Department of Computer Science, Johns Hopkins University.
Bound-Constrained Sparse Representation for Electrical Impedance Tomography
May 27, 2026Chun Zhang, Dong LiuSparse Recovery Image Reconstruction
School of Biomedical Engineering and Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China · Laboratory of Spin Magnetic Resonance and Anhui Province Key Laboratory of Scientific Instrument Development and Application, University of Science and Technology of China, Hefei 230026, China · Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology, Suzhou 215123, China
How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability
May 21, 2026Qishi Zhan, Minxuan Hu, Liang HeSparse Recovery Neural Network Pruning
Marquette University · Cornell University · Tongji University
Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
May 11, 2026Youssef Chaabouni, David GamarnikSparse Recovery Statistical Learning Theory
Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139, USA
SparseForge: Efficient Semi-Structured LLM Sparsification via Annealing of Hessian-Guided Soft-Mask
May 7, 2026Liu Hanzuo, Chaofan Lin, Weixuan Sun +4LLM Pruning Sparse Recovery
†Tsinghua University · ‡Tencent
Learning Interpretable PDE Representations for Generative Reconstructions with Structured Sparsity
Apr 26, 2026Valerie Tsao, Nathaniel Chaney, Manolis VeveakisPhysics-Informed Diffusion Models Sparse Recovery
Department of Civil & Environmental Engineering, Duke University, NC, USA.
A Unified Fractional Regularization Framework for Sparse Recovery
Apr 25, 2026Yinhao Zhao, Yaohua Hu, Hao WangSparse Recovery Nonconvex Optimization
School of Information Science and Technology, ShanghaiTech University, 201210
Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks
Apr 20, 2026Aoran Zhang, Tianyao Wei, Maria J. Guerrero +1Sparse Recovery Non-Negative Matrix Factorization
Department of Electrical and Computer Engineering and the Ken Kennedy Institute, Rice University · Department of Computational and Applied Mathematics, Rice University
Partially deterministic sampling for compressed sensing with denoising guarantees
Apr 6, 2026Yaniv Plan, Matthew S. Scott, Ozgur YilmazCompressed Sensing Sparse Recovery
Department of Mathematics, University of British Columbia, Vancouver, BC, Canada. · CNRS – PIMS International Research Laboratory
Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methods
Apr 4, 2026Hao XiaoSparse Recovery Linear Regression
ATLAS AI Lab
Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning using Neural Networks
Apr 2, 2026Rushabha Balaji, Kuan-Lin Chen, Danijela Cabric +1Sparse Recovery Learning to Optimize
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA · Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
DSCSNet: A Dynamic Sparse Compression Sensing Network for Closely-Spaced Infrared Small Target Unmixing
Mar 22, 2026Zhiyang Tang, Yiming Zhu, Ruimin Huang +4Infrared Small Target Detection Sparse Recovery
High-dimensional online learning via asynchronous decomposition: Non-divergent results, dynamic regularization, and beyond
Mar 21, 2026Shixiang Liu, Zhifan Li, Hanming Yang +1Statistical Learning Theory Streaming Algorithms
School of Statistics, Renmin University of China · School of Statistics and Mathematics, Zhongnan University of Economics and Law · Institute of Statistics and Big Data, Renmin University of China +1
Recovering Sparse Neural Connectivity from Partial Measurements: A Covariance-Based Approach with Granger-Causality Refinement
Mar 19, 2026Quilee SimeonBrain Network Analysis Sparse Recovery
Massachusetts Institute of Technology

Jul 18, 2026J/K move · Enter open · S save

Honest Physical-Support Inference after Latent Dictionary Learning: Collision Singularities and Minimax Resolution

Guan-Ju Peng

Institute of Data Science and Information Computing, National Chung Hsing University.

Sparse Recovery Dictionary Learning

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Sparse-support uncertainty is usually quantified by treating the dictionary as known, an assumption that can produce overconfident, label-dependent conclusions when the dictionary is learned from latent sparse mixtures. Near collisions of coherent atoms, a test signal may identify the active physical group even though the training data cannot distinguish the physical rays within it. We develop inference for active physical rays, unit atoms modulo sign, after latent dictionary learning. In a fixed-dimensional Gaussian train-test experiment, we retain all dictionaries compatible with a robust training-moment region, profile the test representation over them, and project surviving configurations onto a permutation-invariant support space. The resulting confidence correspondence can report cross-sheet inconclusiveness, group resolution with child ambiguity, or fine-support resolution. We characterize both its statistical cost and decision-theoretic benefit. Residual block orientation first affects the latent training density at cubic order, yielding information of order

s^6

, where

s

is the within-block collision scale. The correspondence provides high-probability-over-training conditional test coverage, with resolution governed separately by parent detectability, test-time support separation, and learned-dictionary orientation. In the resolved fixed-shell regime, its projective Hausdorff diameter contracts at the minimax-optimal rate

s \wedge (\sqrt{N}s^2)^{-1}

, up to constants. A restricted-task theorem further determines when coefficient asymmetry allows test replication to supplement training information and when calibration uncertainty remains irreducible. The framework thus yields honest, resolution-adaptive support statements and guides the allocation of training versus test measurements.