Michael S. Albergo

39 papers A* 5Journal 33Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Monte Carlo estimator of flow fields for sampling and noise problems. CoRR Michael S. Albergo, Gurtej Kanwar
2026	J	`jnl`	Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation. CoRR Michal Balcerak, Suprosana Shit, Chinmay Prabhakar, Sebastian Kaltenbach, Michael S. Albergo, Yilun Du, Bjoern Menze
2026	J	`jnl`	Meta Flow Maps enable scalable reward alignment. CoRR Peter Potaptchik, Adhi Saravanan, Abbas Mammadov, Alvaro Prat, Michael S. Albergo, Yee Whye Teh
2025	J	`jnl`	Any-Order Flexible Length Masked Diffusion. CoRR Jaeyeon Kim, Cheuk Kit Lee, Carles Domingo-Enrich, Yilun Du, Sham M. Kakade, Timothy Ngotiaoco, Sitan Chen, Michael S. Albergo
2025	J	`jnl`	Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy. CoRR Inkook Chun, Seungjae Lee, Michael S. Albergo, Saining Xie, Eric Vanden-Eijnden
2025	J	`jnl`	Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds. CoRR Oscar Davis, Michael S. Albergo, Nicholas M. Boffi, Michael M. Bronstein, Avishek Joey Bose
2025	J	`jnl`	How to build a consistency model: Learning flow maps via self-distillation. CoRR Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
2025	J	`jnl`	LEAPS: A discrete neural sampler via locally equivariant networks. CoRR Peter Holderrieth, Michael S. Albergo, Tommi S. Jaakkola
2025	J	`jnl`	Multitask Learning with Stochastic Interpolants. CoRR Hugo Negrel, Florentin Coeurdoux, Michael S. Albergo, Eric Vanden-Eijnden
2025	J	`jnl`	Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. J. Mach. Learn. Res. Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
2025	J	`jnl`	Test-time scaling of diffusions with flow maps. CoRR Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich, Nicholas M. Boffi, Sanja Fidler, Karsten Kreis, Eric Vanden-Eijnden
2025	J	`jnl`	Tilt Matching for Scalable Sampling and Fine-Tuning. CoRR Peter Potaptchik, Cheuk Kit Lee, Michael S. Albergo
2024	J	`jnl`	Flow Map Matching. CoRR Nicholas M. Boffi, Michael S. Albergo, Eric Vanden-Eijnden
2024	A*	`conf`	Multimarginal Generative Modeling with Stochastic Interpolants. ICLR Michael S. Albergo, Nicholas Matthew Boffi, Michael Lindsey, Eric Vanden-Eijnden
2024	J	`jnl`	NETS: A Non-Equilibrium Transport Sampler. CoRR Michael S. Albergo, Eric Vanden-Eijnden
2024	J	`jnl`	Practical applications of machine-learned flows on gauge fields. CoRR Ryan Abbott, Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
2024	A*	`conf`	Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. ICML Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas Matthew Boffi, Eric Vanden-Eijnden
2024	J	`jnl`	Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. CoRR Yifan Chen, Mark Goldstein, Mengjian Hua, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
2024	—	`conf`	SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers. ECCV (77) Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie
2024	J	`jnl`	SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers. CoRR Nanye Ma, Mark Goldstein, Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden, Saining Xie
2024	A*	`conf`	Stochastic Interpolants with Data-Dependent Couplings. ICML Michael S. Albergo, Mark Goldstein, Nicholas Matthew Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
2023	A*	`conf`	Building Normalizing Flows with Stochastic Interpolants. ICLR Michael S. Albergo, Eric Vanden-Eijnden
2023	J	`jnl`	Learning to Sample Better. CoRR Michael S. Albergo, Eric Vanden-Eijnden
2023	J	`jnl`	Multimarginal generative modeling with stochastic interpolants. CoRR Michael S. Albergo, Nicholas M. Boffi, Michael Lindsey, Eric Vanden-Eijnden
2023	J	`jnl`	Normalizing flows for lattice gauge theory in arbitrary space-time dimension. CoRR Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
2023	J	`jnl`	Stochastic Interpolants: A Unifying Framework for Flows and Diffusions. CoRR Michael S. Albergo, Nicholas M. Boffi, Eric Vanden-Eijnden
2023	J	`jnl`	Stochastic interpolants with data-dependent couplings. CoRR Michael S. Albergo, Mark Goldstein, Nicholas M. Boffi, Rajesh Ranganath, Eric Vanden-Eijnden
2022	J	`jnl`	Aspects of scaling and scalability for flow-based sampling of lattice QCD. CoRR Ryan Abbott, Michael S. Albergo, Aleksandar Botev, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Alexander G. de G. Matthews, Sébastien Racanière, Ali Razavi, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
2022	J	`jnl`	Building Normalizing Flows with Stochastic Interpolants. CoRR Michael S. Albergo, Eric Vanden-Eijnden
2022	J	`jnl`	Flow-based sampling in the lattice Schwinger model at criticality. CoRR Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Julian M. Urban
2022	J	`jnl`	Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions. CoRR Ryan Abbott, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, Betsy Tian, Julian M. Urban
2021	J	`jnl`	Flow-based sampling for fermionic lattice field theories. CoRR Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
2021	J	`jnl`	Flow-based sampling for multimodal distributions in lattice field theory. CoRR Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, Phiala E. Shanahan
2021	J	`jnl`	Introduction to Normalizing Flows for Lattice Field Theory. CoRR Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
2020	J	`jnl`	Equivariant flow-based sampling for lattice gauge theory. CoRR Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan
2020	A*	`conf`	Normalizing Flows on Tori and Spheres. ICML Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
2020	J	`jnl`	Normalizing Flows on Tori and Spheres. CoRR Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière, Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan, Kyle Cranmer
2020	J	`jnl`	Sampling using SU(N) gauge equivariant flows. CoRR Denis Boyda, Gurtej Kanwar, Sébastien Racanière, Danilo Jimenez Rezende, Michael S. Albergo, Kyle Cranmer, Daniel C. Hackett, Phiala E. Shanahan
2019	J	`jnl`	Flow-based generative models for Markov chain Monte Carlo in lattice field theory. CoRR Michael S. Albergo, Gurtej Kanwar, Phiala E. Shanahan