Multiscale Anomaly Detection Using Diffusion Maps. - Gal Mishne; Israel Cohen (IEEE J. Sel. Top. Signal Process., 2013)

Multiscale anomaly detection using diffusion maps and saliency score. - Gal Mishne; Israel Cohen (ICASSP, 2014)

Diffusion Nets. - Gal Mishne; Uri Shaham 001; Alexander Cloninger; Israel Cohen (CoRR, 2015)

Graph-Based Supervised Automatic Target Detection. - Gal Mishne; Ronen Talmon; Israel Cohen (IEEE Trans. Geosci. Remote. Sens., 2015)

Improving resolution in supervised patch-based target detection. - Ron Amit; Gal Mishne; Ronen Talmon (ICASSP, 2016)

Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery. - Gal Mishne; Ronen Talmon; Ron Meir; Jackie Schiller; Maria Lavzin; Uri Dubin; Ronald R. Coifman (IEEE J. Sel. Top. Signal Process., 2016)

Iterative diffusion-based anomaly detection. - Gal Mishne; Israel Cohen (ICASSP, 2017)

Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science. - George C. Linderman; Gal Mishne; Yuval Kluger; Stefan Steinerberger (CoRR, 2017)

Data-Driven Tree Transforms and Metrics. - Gal Mishne; Ronen Talmon; Israel Cohen; Ronald R. Coifman; Yuval Kluger (IEEE Trans. Signal Inf. Process. over Networks, 2018)

Learning Spatially-correlated Temporal Dictionaries for Calcium Imaging. - Gal Mishne; Adam S. Charles (ICASSP, 2019)

Co-manifold learning with missing data. - Gal Mishne; Eric C. Chi; Ronald R. Coifman (ICML, 2019)

A Hierarchical Manifold Learning Framework for High-Dimensional Neuroimaging Data. - Siyuan Gao; Gal Mishne; Dustin Scheinost (IPMI, 2019)

Visualizing the PHATE of Neural Networks. - Scott Gigante; Adam S. Charles; Smita Krishnaswamy; Gal Mishne (NeurIPS, 2019)

Poincaré Embedding Reveals Edge-Based Functional Networks of the Brain. - Siyuan Gao; Gal Mishne; Dustin Scheinost (MICCAI (7), 2020)

Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries. - Jay S. Stanley III; Eric C. Chi; Gal Mishne (CoRR, 2020)

Kernel-based parameter estimation of dynamical systems with unknown observation functions. - Ofir Lindenbaum; Amir Sagiv; Gal Mishne; Ronen Talmon (CoRR, 2020)

Randomized near-neighbor graphs, giant components and applications in data science. - Ariel Jaffe; Yuval Kluger; George C. Linderman; Gal Mishne; Stefan Steinerberger (J. Appl. Probab., 2020)

Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian. - Xiuyuan Cheng; Gal Mishne (SIAM J. Imaging Sci., 2020)

Multiway Graph Signal Processing on Tensors: Integrative Analysis of Irregular Geometries. - Jay S. Stanley III; Eric C. Chi; Gal Mishne (IEEE Signal Process. Mag., 2020)

Online Adversarial Purification based on Self-supervised Learning. - Changhao Shi; Chester Holtz; Gal Mishne (ICLR, 2021)

Learning Disentangled Behavior Embeddings. - Changhao Shi; Sivan Schwartz; Shahar Levy; Shay Achvat; Maisan Abboud; Amir Ghanayim; Jackie Schiller; Gal Mishne (NeurIPS, 2021)

COBRAC: a fast implementation of convex biclustering with compression. - Haidong Yi; Le Huang; Gal Mishne; Eric C. Chi (Bioinform., 2021)

Online Adversarial Purification based on Self-Supervision. - Changhao Shi; Chester Holtz; Gal Mishne (CoRR, 2021)

LDLE: Low Distortion Local Eigenmaps. - Dhruv Kohli; Alexander Cloninger; Gal Mishne (J. Mach. Learn. Res., 2021)

Smooth graph learning for functional connectivity estimation. - Siyuan Gao; Xinyue Xia; Dustin Scheinost; Gal Mishne (NeuroImage, 2021)

DiSC: Differential Spectral Clustering of Features. - Ram Dyuthi Sristi; Gal Mishne; Ariel Jaffe (NeurIPS, 2022)

Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors. - Chester Holtz; Gal Mishne; Alexander Cloninger (TAG-ML, 2022)

Learning Sample Reweighting for Accuracy and Adversarial Robustness. - Chester Holtz; Tsui-Wei Weng; Gal Mishne (CoRR, 2022)

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data. - George Armstrong; Gibraan Rahman; Cameron Martino; Daniel McDonald; Antonio González; Gal Mishne; Rob Knight 001 (Frontiers Bioinform., 2022)

Multi-scale affinities with missing data: Estimation and applications. - Min Zhang; Gal Mishne; Eric C. Chi (Stat. Anal. Data Min., 2022)

GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging. - Adam S. Charles; Nathan Cermak; Rifqi O. Affan; Benjamin B. Scott; Jackie Schiller; Gal Mishne (IEEE Trans. Image Process., 2022)

Implicit Graphon Neural Representation. - Xinyue Xia; Gal Mishne; Yusu Wang 001 (AISTATS, 2023)

Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning. - Ya-Wei Eileen Lin; Ronald R. Coifman; Gal Mishne; Ronen Talmon (ICML, 2023)

The Numerical Stability of Hyperbolic Representation Learning. - Gal Mishne; Zhengchao Wan; Yusu Wang 001; Sheng Yang 004 (ICML, 2023)

Product Manifold Learning with Independent Coordinate Selection. - Jesse He; Tristan Brugère; Gal Mishne (TAG-ML, 2023)

Non-degenerate Rigid Alignment in a Patch Framework. - Dhruv Kohli; Gal Mishne; Alexander Cloninger (CoRR, 2023)

Semi-Supervised Laplacian Learning on Stiefel Manifolds. - Chester Holtz; Pengwen Chen; Alexander Cloninger; Chung-Kuan Cheng; Gal Mishne (CoRR, 2023)

On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs. - Sawyer Jack Robertson; Dhruv Kohli; Gal Mishne; Alexander Cloninger (CoRR, 2023)

Contextual Feature Selection with Conditional Stochastic Gates. - Ram Dyuthi Sristi; Ofir Lindenbaum; Maria Lavzin; Jackie Schiller; Gal Mishne; Hadas Benisty (CoRR, 2023)

Learning Cartesian Product Graphs with Laplacian Constraints. - Changhao Shi; Gal Mishne (AISTATS, 2024)

Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error. - Jingya Huang; Pablo Tostado-Marcos; Sowmya Manojna Narasimha; Aashish N. Patel; Ezequiel M. Arneodo; Timothy Q. Gentner; Gal Mishne; Vikash Gilja (EMBC, 2024)

Comparing Graph Transformers via Positional Encodings. - Mitchell Black 002; Zhengchao Wan; Gal Mishne; Amir Nayyeri; Yusu Wang 001 (ICML, 2024)

SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States. - Noga Mudrik; Gal Mishne; Adam S. Charles (ICML, 2024)

Contextual Feature Selection with Conditional Stochastic Gates. - Ram Dyuthi Sristi; Ofir Lindenbaum; Shira Lifshitz; Maria Lavzin; Jackie Schiller; Gal Mishne; Hadas Benisty (ICML, 2024)

Continuous Partitioning for Graph-Based Semi-Supervised Learning. - Chester Holtz; Pengwen Chen; Zhengchao Wan; Chung-Kuan Cheng; Gal Mishne (NeurIPS, 2024)

Deep and shallow data science for multi-scale optical neuroscience. - Gal Mishne; Adam S. Charles (CoRR, 2024)

Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training. - Jiancheng Xie; Lou C. Kohler Voinov; Noga Mudrik; Gal Mishne; Adam S. Charles (CoRR, 2024)

Elucidating Flow Matching ODE Dynamics with respect to Data Geometries. - Gal Mishne; Zhengchao Wan; Qingsong Wang; Yusu Wang 001 (CoRR, 2024)

Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122). - Dmitry Kobak; Fred A. Hamprecht; Smita Krishnaswamy; Gal Mishne; Sebastian Damrich (Dagstuhl Reports, 2024)

Random Walks, Conductance, and Resistance for the Connection Graph Laplacian. - Alexander Cloninger; Gal Mishne; Andreas Oslandsbotn; Sawyer Jack Robertson; Zhengchao Wan; Yusu Wang 001 (SIAM J. Matrix Anal. Appl., 2024)

Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy. - Ya-Wei Eileen Lin; Ronald R. Coifman; Gal Mishne; Ronen Talmon (ICLR, 2025)

Graph Laplacian Learning With Exponential Family Noise. - Changhao Shi; Gal Mishne (IEEE Trans. Signal Inf. Process. over Networks, 2025)

Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance. - Ya-Wei Eileen Lin; Ronald R. Coifman; Gal Mishne; Ronen Talmon (CoRR, 2025)

Robust Graph-Based Semi-Supervised Learning via p-Conductances. - Sawyer Jack Robertson; Chester Holtz; Zhengchao Wan; Gal Mishne; Alexander Cloninger (CoRR, 2025)

Learning Kronecker-Structured Graphs from Smooth Signals. - Changhao Shi; Gal Mishne (CoRR, 2025)

Explaining GNN Explanations with Edge Gradients. - Jesse He; Akbar Rafiey; Gal Mishne; Yusu Wang 001 (CoRR, 2025)