Siamak Ravanbakhsh
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
27
Venues
5
Active years
2012–2025
Best venue rank
A*
Where they publish
Papers
27 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | AISTATS | On the Identifiability of Causal Abstractions. | Xiusi Li, Skou-Oumar Kaba, Siamak Ravanbakhsh |
| 2025 | ICLR | SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models. | Daniel Levy, Siba Smarak Panigrahi, Skou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh |
| 2024 | AISTATS | Weight-Sharing Regularization. | Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien |
| 2024 | AISTATS | E(3)-Equivariant Mesh Neural Networks. | Thuan Anh Trang, Nhat Khang Ngo, Daniel Levy, Ngoc Thieu Vo, Siamak Ravanbakhsh, Truong Son Hy |
| 2024 | ICLR | On Diffusion Modeling for Anomaly Detection. | Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh |
| 2024 | ICLR | Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. | Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh |
| 2024 | ICML | Iterated Denoising Energy Matching for Sampling from Boltzmann Densities. | Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong |
| 2024 | ICML | Learning to Reach Goals via Diffusion. | Vineet Jain, Siamak Ravanbakhsh |
| 2023 | ICML | Equivariance with Learned Canonicalization Functions. | Skou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh |
| 2022 | ICML | SpeqNets: Sparsity-aware permutation-equivariant graph networks. | Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh |
| 2022 | ICML | EqR: Equivariant Representations for Data-Efficient Reinforcement Learning. | Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh |
| 2022 | ICML | Utility Theory for Sequential Decision Making. | Mehran Shakerinava, Siamak Ravanbakhsh |
| 2021 | ICML | Equivariant Networks for Pixelized Spheres. | Mehran Shakerinava, Siamak Ravanbakhsh |
| 2020 | ICML | Universal Equivariant Multilayer Perceptrons. | Siamak Ravanbakhsh |
| 2019 | AAAI | Improved Knowledge Graph Embedding Using Background Taxonomic Information. | Bahare Fatemi, Siamak Ravanbakhsh, David Poole |
| 2018 | ICLR | Analysis of Cosmic Microwave Background with Deep Learning. | Siyu He, Siamak Ravanbakhsh, Shirley Ho |
| 2018 | ICML | Deep Models of Interactions Across Sets. | Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh |
| 2018 | MICCAI | Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. | Sumedha Singla, Mingming Gong, Siamak Ravanbakhsh, Frank C. Sciurba, Barnabs Pczos, Kayhan N. Batmanghelich |
| 2017 | AAAI | Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. | Siamak Ravanbakhsh, Franois Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabs Pczos |
| 2017 | ICLR | Deep Learning with Sets and Point Clouds. | Siamak Ravanbakhsh, Jeff G. Schneider, Barnabs Pczos |
| 2017 | ICML | Equivariance Through Parameter-Sharing. | Siamak Ravanbakhsh, Jeff G. Schneider, Barnabs Pczos |
| 2016 | AISTATS | Stochastic Neural Networks with Monotonic Activation Functions. | Siamak Ravanbakhsh, Barnabs Pczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner |
| 2016 | AISTATS | Survey Propagation beyond Constraint Satisfaction Problems. | Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey |
| 2016 | ICML | Estimating Cosmological Parameters from the Dark Matter Distribution. | Siamak Ravanbakhsh, Junier B. Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff G. Schneider, Barnabs Pczos |
| 2016 | ICML | Boolean Matrix Factorization and Noisy Completion via Message Passing. | Siamak Ravanbakhsh, Barnabs Pczos, Russell Greiner |
| 2014 | ICML | Min-Max Problems on Factor Graphs. | Siamak Ravanbakhsh, Christopher Srinivasa, Brendan J. Frey, Russell Greiner |
| 2012 | ICML | A Generalized Loop Correction Method for Approximate Inference in Graphical Models. | Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner |