Reposted by Huy Tran
🔄 Updated Arxiv Paper
Title: Modelling Global Trade with Optimal Transport
Authors: Thomas Gaskin, Guven Demirel, Marie-Therese Wolfram, Andrew Duncan
Read more: https://arxiv.org/abs/2409.06554
Title: Modelling Global Trade with Optimal Transport
Authors: Thomas Gaskin, Guven Demirel, Marie-Therese Wolfram, Andrew Duncan
Read more: https://arxiv.org/abs/2409.06554
November 21, 2025 at 8:05 AM
🔄 Updated Arxiv Paper
Title: Modelling Global Trade with Optimal Transport
Authors: Thomas Gaskin, Guven Demirel, Marie-Therese Wolfram, Andrew Duncan
Read more: https://arxiv.org/abs/2409.06554
Title: Modelling Global Trade with Optimal Transport
Authors: Thomas Gaskin, Guven Demirel, Marie-Therese Wolfram, Andrew Duncan
Read more: https://arxiv.org/abs/2409.06554
Reposted by Huy Tran
A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models
Evelyn Mannix, Howard Bondell
Action editor: Gabriel Loaiza-Ganem
https://openreview.net/forum?id=xpKqnSJtE4
#classifier #detection #classification
Evelyn Mannix, Howard Bondell
Action editor: Gabriel Loaiza-Ganem
https://openreview.net/forum?id=xpKqnSJtE4
#classifier #detection #classification
November 20, 2025 at 5:19 PM
A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models
Evelyn Mannix, Howard Bondell
Action editor: Gabriel Loaiza-Ganem
https://openreview.net/forum?id=xpKqnSJtE4
#classifier #detection #classification
Evelyn Mannix, Howard Bondell
Action editor: Gabriel Loaiza-Ganem
https://openreview.net/forum?id=xpKqnSJtE4
#classifier #detection #classification
Reposted by Huy Tran
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
Action editor: Chicheng Zhang
https://openreview.net/forum?id=HL75La10FN
#detection #deep #feature
Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
Action editor: Chicheng Zhang
https://openreview.net/forum?id=HL75La10FN
#detection #deep #feature
November 19, 2025 at 5:18 AM
A Unified Approach Towards Active Learning and Out-of-Distribution Detection
Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
Action editor: Chicheng Zhang
https://openreview.net/forum?id=HL75La10FN
#detection #deep #feature
Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
Action editor: Chicheng Zhang
https://openreview.net/forum?id=HL75La10FN
#detection #deep #feature
Reposted by Huy Tran
Reading group tomorrow: "How to build a consistency model: Learning flow maps via self-distillation" with Nicholas Boffi! arxiv.org/abs/2505.18825
Join us on zoom at 9am PT, 12pm ET, 6pm CET: portal.valencelabs.com/starklyspeak...
Join us on zoom at 9am PT, 12pm ET, 6pm CET: portal.valencelabs.com/starklyspeak...
November 16, 2025 at 5:06 PM
Reading group tomorrow: "How to build a consistency model: Learning flow maps via self-distillation" with Nicholas Boffi! arxiv.org/abs/2505.18825
Join us on zoom at 9am PT, 12pm ET, 6pm CET: portal.valencelabs.com/starklyspeak...
Join us on zoom at 9am PT, 12pm ET, 6pm CET: portal.valencelabs.com/starklyspeak...
Reposted by Huy Tran
Unifying Self-Supervised Clustering and Energy-Based Models
Emanuele Sansone, Robin Manhaeve
Action editor: Ole Winther
https://openreview.net/forum?id=NW0uKe6IZa
#generative #supervised #models
Emanuele Sansone, Robin Manhaeve
Action editor: Ole Winther
https://openreview.net/forum?id=NW0uKe6IZa
#generative #supervised #models
November 13, 2025 at 1:18 PM
Unifying Self-Supervised Clustering and Energy-Based Models
Emanuele Sansone, Robin Manhaeve
Action editor: Ole Winther
https://openreview.net/forum?id=NW0uKe6IZa
#generative #supervised #models
Emanuele Sansone, Robin Manhaeve
Action editor: Ole Winther
https://openreview.net/forum?id=NW0uKe6IZa
#generative #supervised #models
Reposted by Huy Tran
Entangled Schrödinger Bridge Matching][new]
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
November 11, 2025 at 4:00 AM
Entangled Schrödinger Bridge Matching][new]
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
Models interacting particle dynamics by entangling velocities via coupled bias forces, improving trajectory simulation for systems with evolving interactions.
Reposted by Huy Tran
Does equivariance matter at scale?
Johann Brehmer, Sönke Behrends, Pim De Haan, Taco Cohen
Action editor: Marcus Brubaker
https://openreview.net/forum?id=wilNute8Tn
#models #equivariance #equivariant
Johann Brehmer, Sönke Behrends, Pim De Haan, Taco Cohen
Action editor: Marcus Brubaker
https://openreview.net/forum?id=wilNute8Tn
#models #equivariance #equivariant
November 10, 2025 at 9:18 PM
Does equivariance matter at scale?
Johann Brehmer, Sönke Behrends, Pim De Haan, Taco Cohen
Action editor: Marcus Brubaker
https://openreview.net/forum?id=wilNute8Tn
#models #equivariance #equivariant
Johann Brehmer, Sönke Behrends, Pim De Haan, Taco Cohen
Action editor: Marcus Brubaker
https://openreview.net/forum?id=wilNute8Tn
#models #equivariance #equivariant
Reposted by Huy Tran
"The Principles of Diffusion Models" by Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon. arxiv.org/abs/2510.21890
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
The Principles of Diffusion Models
This monograph presents the core principles that have guided the development of diffusion models, tracing their origins and showing how diverse formulations arise from shared mathematical ideas. Diffu...
arxiv.org
October 28, 2025 at 8:35 AM
"The Principles of Diffusion Models" by Chieh-Hsin Lai, Yang Song, Dongjun Kim, Yuki Mitsufuji, Stefano Ermon. arxiv.org/abs/2510.21890
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
It might not be the easiest intro to diffusion models, but this monograph is an amazing deep dive into the math behind them and all the nuances
Reposted by Huy Tran
New paper on arXiv! And I think it's a good'un 😄
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
August 29, 2025 at 3:07 PM
New paper on arXiv! And I think it's a good'un 😄
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Meet the new Lattice Random Walk (LRW) discretisation for SDEs. It’s radically different from traditional methods like Euler-Maruyama (EM) in that each iteration can only move in discrete steps {-δₓ, 0, δₓ}.
Reposted by Huy Tran
Samuel Duffield, Maxwell Aifer, Denis Melanson, Zach Belateche, Patrick J. Coles
Lattice Random Walk Discretisations of Stochastic Differential Equations
https://arxiv.org/abs/2508.20883
Lattice Random Walk Discretisations of Stochastic Differential Equations
https://arxiv.org/abs/2508.20883
August 29, 2025 at 4:03 AM
Samuel Duffield, Maxwell Aifer, Denis Melanson, Zach Belateche, Patrick J. Coles
Lattice Random Walk Discretisations of Stochastic Differential Equations
https://arxiv.org/abs/2508.20883
Lattice Random Walk Discretisations of Stochastic Differential Equations
https://arxiv.org/abs/2508.20883
Reposted by Huy Tran
August 28, 2025 at 4:19 AM
Reposted by Huy Tran
Great stuff: arxiv.org/abs/2508.18175
Amortized Sampling with Transferable Normalizing Flows
Efficient equilibrium sampling of molecular conformations remains a core challenge in computational chemistry and statistical inference. Classical approaches such as molecular dynamics or Markov chain...
arxiv.org
August 26, 2025 at 3:44 AM
Great stuff: arxiv.org/abs/2508.18175
Reposted by Huy Tran
My memory is that I have a few mostly-written drafts waiting in the wings on HackMD, and I'll try to upload them soon.
I'm also thinking about writing exercises which might be fun for me to explore, e.g. picking some topic from a list and taking <30 mins to write a personal impression / overview.
I'm also thinking about writing exercises which might be fun for me to explore, e.g. picking some topic from a list and taking <30 mins to write a personal impression / overview.
August 17, 2025 at 8:07 PM
Reposted by Huy Tran
It’s a thing! www.microsoft.com/en-us/resear...
www.microsoft.com
August 12, 2025 at 7:59 PM
It’s a thing! www.microsoft.com/en-us/resear...
Reposted by Huy Tran
Struggling to control a system under noise and uncertainty? Willem Esterhuizen's new article dives into Model Predictive Control (MPC), a powerful feedback loop that uses a model to anticipate and correct system behavior. Learn how to handle hard constraints and disturbances.
Model Predictive-Control Basics | Towards Data Science
A hands-on tutorial with Python and CasADi
towardsdatascience.com
August 12, 2025 at 10:30 PM
Struggling to control a system under noise and uncertainty? Willem Esterhuizen's new article dives into Model Predictive Control (MPC), a powerful feedback loop that uses a model to anticipate and correct system behavior. Learn how to handle hard constraints and disturbances.
Reposted by Huy Tran
Eliot Beyler (SIERRA), Francis Bach (SIERRA): Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance https://arxiv.org/abs/2508.03210 https://arxiv.org/pdf/2508.03210 https://arxiv.org/html/2508.03210
August 6, 2025 at 6:33 AM
Eliot Beyler (SIERRA), Francis Bach (SIERRA): Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance https://arxiv.org/abs/2508.03210 https://arxiv.org/pdf/2508.03210 https://arxiv.org/html/2508.03210
Reposted by Huy Tran
Francisco Daunas, I\~naki Esnaola, Samir M. Perlaza
A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
https://arxiv.org/abs/2508.03314
A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
https://arxiv.org/abs/2508.03314
August 6, 2025 at 4:16 AM
Francisco Daunas, I\~naki Esnaola, Samir M. Perlaza
A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
https://arxiv.org/abs/2508.03314
A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
https://arxiv.org/abs/2508.03314
Reposted by Huy Tran
Sergio Calvo-Ordonez, Matthieu Meunier, Alvaro Cartea, Christoph Reisinger, Yarin Gal, Jose Miguel Hernandez-Lobato: Weighted Conditional Flow Matching https://arxiv.org/abs/2507.22270 https://arxiv.org/pdf/2507.22270 https://arxiv.org/html/2507.22270
July 31, 2025 at 6:32 AM
Sergio Calvo-Ordonez, Matthieu Meunier, Alvaro Cartea, Christoph Reisinger, Yarin Gal, Jose Miguel Hernandez-Lobato: Weighted Conditional Flow Matching https://arxiv.org/abs/2507.22270 https://arxiv.org/pdf/2507.22270 https://arxiv.org/html/2507.22270
Reposted by Huy Tran
Alan Jeffares, Liyuan Liu: An Introduction to Discrete Variational Autoencoders https://arxiv.org/abs/2505.10344 https://arxiv.org/pdf/2505.10344 https://arxiv.org/html/2505.10344
May 16, 2025 at 6:09 AM
Alan Jeffares, Liyuan Liu: An Introduction to Discrete Variational Autoencoders https://arxiv.org/abs/2505.10344 https://arxiv.org/pdf/2505.10344 https://arxiv.org/html/2505.10344
Reposted by Huy Tran
Tomorrow we discuss diffusion models for sampling unnormalized densities "Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching"
arxiv.org/abs/2504.11713
Join us on zoom at 9am PT / 12pm ET / 6pm CEST: portal.valencelabs.com/starklyspeak...
arxiv.org/abs/2504.11713
Join us on zoom at 9am PT / 12pm ET / 6pm CEST: portal.valencelabs.com/starklyspeak...
August 3, 2025 at 11:49 PM
Tomorrow we discuss diffusion models for sampling unnormalized densities "Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching"
arxiv.org/abs/2504.11713
Join us on zoom at 9am PT / 12pm ET / 6pm CEST: portal.valencelabs.com/starklyspeak...
arxiv.org/abs/2504.11713
Join us on zoom at 9am PT / 12pm ET / 6pm CEST: portal.valencelabs.com/starklyspeak...
Reposted by Huy Tran
Yaxin Ma, Benjamin Colburn, Jose C. Principe: A Simple and Effective Method for Uncertainty Quantification and OOD Detection https://arxiv.org/abs/2508.00754 https://arxiv.org/pdf/2508.00754 https://arxiv.org/html/2508.00754
August 4, 2025 at 6:33 AM
Yaxin Ma, Benjamin Colburn, Jose C. Principe: A Simple and Effective Method for Uncertainty Quantification and OOD Detection https://arxiv.org/abs/2508.00754 https://arxiv.org/pdf/2508.00754 https://arxiv.org/html/2508.00754
Reposted by Huy Tran
Another very nice paper arxiv.org/abs/2502.02300
Density Ratio Estimation with Conditional Probability Paths
Density ratio estimation in high dimensions can be reframed as integrating a certain quantity, the time score, over probability paths which interpolate between the two densities. In practice, the time...
arxiv.org
July 28, 2025 at 10:19 AM
Another very nice paper arxiv.org/abs/2502.02300
Reposted by Huy Tran
I had a great time presenting "It's Time to Say Goodbye to Hard Constraints" at the Flatiron Institute. In this talk, I describe a philosophy for model construction in machine learning. Video now online! www.youtube.com/watch?v=LxuN...
It's Time to Say Goodbye to Hard (equivariance) Constraints - Andrew Gordon Wilson
YouTube video by LoG Meetup NYC
www.youtube.com
July 22, 2025 at 7:28 PM
I had a great time presenting "It's Time to Say Goodbye to Hard Constraints" at the Flatiron Institute. In this talk, I describe a philosophy for model construction in machine learning. Video now online! www.youtube.com/watch?v=LxuN...
Reposted by Huy Tran
Join Dr. Lianrui Zuo, Postdoctoral Researcher in ECE at VU and AI Scholar with VALIANT, at AI Summer School! 🌟 He'll be presenting "The Shape of Data in Noise: Diffusion Models as a Programmable Prior"
Registration ends on July 31st.
Register here: buff.ly/4fyVotP
Registration ends on July 31st.
Register here: buff.ly/4fyVotP
July 21, 2025 at 1:32 PM
Join Dr. Lianrui Zuo, Postdoctoral Researcher in ECE at VU and AI Scholar with VALIANT, at AI Summer School! 🌟 He'll be presenting "The Shape of Data in Noise: Diffusion Models as a Programmable Prior"
Registration ends on July 31st.
Register here: buff.ly/4fyVotP
Registration ends on July 31st.
Register here: buff.ly/4fyVotP