Alexander Tong

Orcid: 0000-0002-2031-4096

Affiliations:

Yale University, New Haven, CT, USA (PhD 2021)

According to our database¹, Alexander Tong authored at least 37 papers between 2018 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

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In proceedings

Article

PhD thesis

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Bibliography

2024

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities.

[BibT_eX]

[DOI]

CoRR, 2024

Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy.

[BibT_eX]

[DOI]

Danqi Liao

Chen Liu

Benjamin W. Christensen

Proceedings of the 58th Annual Conference on Information Sciences and Systems, 2024

2023

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., December, 2023

Time-Inhomogeneous Diffusion Geometry and Topology.

[BibT_eX]

[DOI]

SIAM J. Math. Data Sci., June, 2023

A Computational Framework for Solving Wasserstein Lagrangian Flows.

[BibT_eX]

[DOI]

CoRR, 2023

Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems.

[BibT_eX]

[DOI]

CoRR, 2023

SE(3)-Stochastic Flow Matching for Protein Backbone Generation.

[BibT_eX]

[DOI]

CoRR, 2023

Simulation-free Schrödinger bridges via score and flow matching.

[BibT_eX]

[DOI]

CoRR, 2023

Graph Fourier MMD for Signals on Graphs.

[BibT_eX]

[DOI]

CoRR, 2023

DynGFN: Bayesian Dynamic Causal Discovery using Generative Flow Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport.

[BibT_eX]

[DOI]

CoRR, 2023

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Geodesic Sinkhorn For Fast and Accurate Optimal Transport on Manifolds.

[BibT_eX]

[DOI]

Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

Neural FIM for learning Fisher information metrics from point cloud data.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators.

[BibT_eX]

[DOI]

Alexander Tong

Guy Wolf

Smita Krishnaswamy

J. Signal Process. Syst., 2022

Geodesic Sinkhorn: optimal transport for high-dimensional datasets.

[BibT_eX]

[DOI]

CoRR, 2022

Learnable Filters for Geometric Scattering Modules.

[BibT_eX]

[DOI]

CoRR, 2022

Manifold Interpolating Optimal-Transport Flows for Trajectory Inference.

[BibT_eX]

[DOI]

Guillaume Huguet

Daniel Sumner Magruder

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

POT: Python Optimal Transport.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance.

[BibT_eX]

[DOI]

CoRR, 2021

A sandbox for prediction and integration of DNA, RNA, and proteins in single cells.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Data-Driven Learning of Geometric Scattering Modules for GNNs.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021

Multimodal Data Visualization and Denoising with Integrated Diffusion.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021

Diffusion Earth Mover's Distance and Distribution Embeddings.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Data-Driven Learning of Geometric Scattering Networks.

[BibT_eX]

[DOI]

CoRR, 2020

Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings.

[BibT_eX]

[DOI]

CoRR, 2020

Interpretable Neuron Structuring with Graph Spectral Regularization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

A Lipschitz-constrained anomaly discriminator framework.

[BibT_eX]

[DOI]

Alexander Tong

Guy Wolf

Smita Krishnaswamy

CoRR, 2019

Finding Archetypal Spaces for Data Using Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

Finding Archetypal Spaces Using Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Graph Spectral Regularization for Neural Network Interpretability.

[BibT_eX]

[DOI]

CoRR, 2018

Allocate-On-Use Space Complexity of Shared-Memory Algorithms.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Symposium on Distributed Computing, 2018

Alexander Tong

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