Awesome Statistics — Methodology — curated papers, datasets & benchmarks

Multimodal Alignment Through Joint Kernel Entropic Gromov--Wasserstein Optimal Transport (2026)

Yixuan Florence Wu et al.

5.54

Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds (2026)

Yizhu Wang et al.

5.54

Graph-Based Correlation Matrix Generation: A Convex Optimization Approach (2026)

Ali Fakhar (UGA) et al.

5.49

On Hamming-Lipschitz Type Stability of the Subdominant (Minmax) Ultrametric: Theory and Simple Proofs (2026)

Alokendu Mazumder et al.

4.81

Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computing (2026)

Laha Ale et al.

4.81

MINT: Tensor Decomposition on Stacked Recurrence Matrices for Time Series Data Mining (2026)

Kaamil Kaka et al.

4.81

Random features for Grassmannian kernel approximation with bounded rank-one projections (2026)

R\'emi Delogne et al.

4.81

Geometry-Informed Parameter-Efficient Fine-Tuning of Pre-trained Molecular GNNs for Blood-Brain Barrier Permeability Prediction (2026)

Marco Vieto Vega et al.

4.81

EvtGraph: Event-Adaptive Compression for Sparse Temporal Graph Learning in Multimodal Time Series (2026)

Ziqian Wang et al.

4.81

Towards Trustworthy Hypergraph Neural Networks under Label Noise (2026)

Mengyao Zhou et al.

4.81

Tropical Algebraic Geometry for Neuronal Representations: An Arakelov-Green Measure Based Descriptor for Graph Learning (2026)

Yuyang Zhang et al.

4.81

Variational Bounds for Perceptron Learning from Structured Data (2026)

Francesco Camilli et al.

4.81

SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery (2026)

Shrenik Zinage

4.81

SparseDitto: Customizing GPU Kernels for Different Sparsity Patterns with LLM-Based Agentic System (2026)

Shiyang Li et al.

4.81

Adversarial Causal Intervention Falsification (2026)

Mojtaba Eslami

4.81

Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift (2026)

Hoang Dang et al.

4.81

Newton-Schulz Retraction-Based Inference Enables Hidden Quantum Markov Models to Outperform Classical HMMs (2026)

Ning Ning

4.81

Mixture of Geodesic Factor Analyzers on Riemannian Homogeneous Spaces (2026)

Hengchao Chen et al.

4.81

PriDyG: Privacy-preserving Dynamic Graph Inference with LLM-GNN Collaboration (2026)

Yuyang Xia et al.

3.77

ACA-GS: Adaptive-Capacity Anchored Gaussian Splatting for Compact Dynamic Radiance Fields (2026)

Seunghyeon Song et al.

3.77

Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair (2026)

Jingyu Wu et al.

3.77

Predicting Brain Morphometry with MT-GNN: Mesh Evolution in Continuous Time with Graph-Based Metric Tensor Embeddings (2026)

Hao Ding et al.

3.77

Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics (2026)

Vignesh Tirukkonda et al.

1.94

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation (2026)

Serhii Zabolotnii

1.83

Task Ecologies and the Evolution of World-Tracking Representations in Large Language Models (2026)

Giulio Valentino Dalla Riva

1.78

Action Shapley: A Training Data Selection Metric for World Model in Reinforcement Learning (2026)

Rajat Ghosh et al.

1.61

Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization (2025)

Shaohan Li and Yunpeng Shi and Gilad Lerman

1.50

Integrated Gaussian Processes for Robust and Adaptive Multi-Object Tracking (2025)

Fred Lydeard et al.

1.28

The Space Between Us: A Methodological Framework for Researching Bonding and Proxemics in Situated Group-Agent Interactions (2025)

Ana M\"uller and Anja Richert

1.22

The Multi-Trip Time-Dependent Mix Vehicle Routing Problem for Hybrid Autonomous Shared Delivery Location and Traditional Door-to-Door Delivery Modes (2025)

Jingyi Zhao et al.

1.06

A primer on optimal transport for causal inference with observational data (2025)

Florian F Gunsilius

1.06

Four Years in Review: Statistical Practices of Likert Scales in Human-Robot Interaction Studies (2020)

Mariah L. Schrum et al.

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Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields (2021)

Sunrit Chakraborty et al.

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Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling (2022)

Mansur Arief et al.

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Estimating counterfactual treatment outcomes over time in complex multiagent scenarios (2022)

Keisuke Fujii et al.

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Variational Causal Dynamics: Discovering Modular World Models from Interventions (2022)

Anson Lei et al.

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Seamless Tracking of Group Targets and Ungrouped Targets Using Belief Propagation (2022)

Xuqi Zhang et al.

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Will My Robot Achieve My Goals? Predicting the Probability that an MDP Policy Reaches a User-Specified Behavior Target (2022)

Alexander Guyer et al.

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On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations (2022)

Tim G. J. Rudner and Cong Lu and Michael A. Osborne and Yarin Gal and Yee Whye Teh

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Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach (2023)

Christo Kurisummoottil Thomas et al.

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BISCUIT: Causal Representation Learning from Binary Interactions (2023)

Phillip Lippe et al.

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Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions (2023)

Jordan Lekeufack et al.

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CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process (2024)

Guangyi Chen et al.

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DIGIC: Domain Generalizable Imitation Learning by Causal Discovery (2024)

Yang Chen et al.

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On Predictive planning and counterfactual learning in active inference (2024)

Aswin Paul et al.

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The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology (2024)

Juan L. Gamella et al.

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Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving (2024)

Ehsan Ahmadi et al.

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Data-Driven Approaches for Modelling Target Behaviour (2024)

Isabel Schlangen et al.

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Language Agents Meet Causality -- Bridging LLMs and Causal World Models (2024)

John Gkountouras et al.

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Awesome Statistics — Methodology

Key papers

Statistics — Methodology

Awesome Statistics — Methodology

Key papers