Ravanbakhsh, Siamak

47 publications

NeurIPS 2025 Beyond Scalar Rewards: An Axiomatic Framework for Lexicographic MDPs Mehran Shakerinava, Siamak Ravanbakhsh, Adam Oberman

NeurIPS 2025 Diffusion Tree Sampling: Scalable Inference‑time Alignment of Diffusion Models Vineet Jain, Kusha Sareen, Mohammad Pedramfar, Siamak Ravanbakhsh

NeurIPS 2025 Energy Loss Functions for Physical Systems Sékou-Oumar Kaba, Kusha Sareen, Daniel Levy, Siamak Ravanbakhsh

AISTATS 2025 On the Identifiability of Causal Abstractions Xiusi Li, Sékou-Oumar Kaba, Siamak Ravanbakhsh

NeurIPS 2025 Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities Tara Akhound-Sadegh, Jungyoon Lee, Joey Bose, Valentin De Bortoli, Arnaud Doucet, Michael M. Bronstein, Dominique Beaini, Siamak Ravanbakhsh, Kirill Neklyudov, Alexander Tong

ICLR 2025 SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh

AISTATS 2024 E(3)-Equivariant Mesh Neural Networks Thuan Anh Trang, Nhat Khang Ngo, Daniel T. Levy, Thieu Ngoc Vo, Siamak Ravanbakhsh, Truong Son Hy

ICLR 2024 Efficient Dynamics Modeling in Interactive Environments with Koopman Theory Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, Siamak Ravanbakhsh

ICML 2024 Iterated Denoising Energy Matching for Sampling from Boltzmann Densities Tara Akhound-Sadegh, Jarrid Rector-Brooks, Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, Alexander Tong

ICML 2024 Learning to Reach Goals via Diffusion Vineet Jain, Siamak Ravanbakhsh

ICLR 2024 On Diffusion Modeling for Anomaly Detection Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh

TMLR 2024 Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions Thuan Nguyen Anh Trang, Khang Nhat Ngo, Hugo Sonnery, Thieu Vo, Siamak Ravanbakhsh, Truong Son Hy

NeurIPSW 2024 SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba, Qiang Zhu, Mikhail Galkin, Santiago Miret, Siamak Ravanbakhsh

NeurIPSW 2024 Symmetry-Aware Generative Modeling Through Learned Canonicalization Kusha Sareen, Daniel Levy, Arnab Kumar Mondal, Sékou-Oumar Kaba, Tara Akhound-Sadegh, Siamak Ravanbakhsh

AISTATS 2024 Weight-Sharing Regularization Mehran Shakerinava, Motahareh MS Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien

ICML 2023 Equivariance with Learned Canonicalization Functions Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh

NeurIPS 2023 Equivariant Adaptation of Large Pretrained Models Arnab Kumar Mondal, Siba Smarak Panigrahi, Oumar Kaba, Sai Rajeswar Mudumba, Siamak Ravanbakhsh

ICMLW 2023 Lie Point Symmetry and Physics Informed Networks Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

NeurIPS 2023 Lie Point Symmetry and Physics-Informed Networks Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter, Max Welling, Siamak Ravanbakhsh

NeurIPSW 2023 Physics-Informed Transformer Networks Fabricio Dos Santos, Tara Akhound-Sadegh, Siamak Ravanbakhsh

NeurIPSW 2023 Symmetry Breaking and Equivariant Neural Networks Sékou-Oumar Kaba, Siamak Ravanbakhsh

ICML 2022 EqR: Equivariant Representations for Data-Efficient Reinforcement Learning Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi, Siamak Ravanbakhsh

NeurIPSW 2022 Equivariance with Learned Canonicalization Functions Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, Siamak Ravanbakhsh

NeurIPS 2022 Equivariant Networks for Crystal Structures Oumar Kaba, Siamak Ravanbakhsh

ICML 2022 SpeqNets: Sparsity-Aware Permutation-Equivariant Graph Networks Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh

ICLRW 2022 SpeqNets: Sparsity-Aware Permutation-Equivariant Graph Networks Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh

NeurIPS 2022 Structuring Representations Using Group Invariants Mehran Shakerinava, Arnab Kumar Mondal, Siamak Ravanbakhsh

ICML 2022 Utility Theory for Sequential Decision Making Mehran Shakerinava, Siamak Ravanbakhsh

ICML 2021 Equivariant Networks for Pixelized Spheres Mehran Shakerinava, Siamak Ravanbakhsh

NeurIPS 2020 Equivariant Networks for Hierarchical Structures Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh

ICML 2020 Universal Equivariant Multilayer Perceptrons Siamak Ravanbakhsh

AAAI 2019 Improved Knowledge Graph Embedding Using Background Taxonomic Information Bahare Fatemi, Siamak Ravanbakhsh, David Poole

ICML 2018 Deep Models of Interactions Across Sets Jason Hartford, Devon Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh

ICLR 2017 Deep Learning with Sets and Point Clouds Siamak Ravanbakhsh, Jeff G. Schneider, Barnabás Póczos

NeurIPS 2017 Deep Sets Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander J Smola

AAAI 2017 Enabling Dark Energy Science with Deep Generative Models of Galaxy Images Siamak Ravanbakhsh, François Lanusse, Rachel Mandelbaum, Jeff G. Schneider, Barnabás Póczos

ICML 2017 Equivariance Through Parameter-Sharing Siamak Ravanbakhsh, Jeff Schneider, Barnabás Póczos

NeurIPS 2017 Min-Max Propagation Christopher Srinivasa, Inmar Givoni, Siamak Ravanbakhsh, Brendan J. Frey

ICML 2016 Boolean Matrix Factorization and Noisy Completion via Message Passing Siamak Ravanbakhsh, Barnabas Poczos, Russell Greiner

ICML 2016 Estimating Cosmological Parameters from the Dark Matter Distribution Siamak Ravanbakhsh, Junier Oliva, Sebastian Fromenteau, Layne Price, Shirley Ho, Jeff Schneider, Barnabas Poczos

AISTATS 2016 Stochastic Neural Networks with Monotonic Activation Functions Siamak Ravanbakhsh, Barnabás Póczos, Jeff G. Schneider, Dale Schuurmans, Russell Greiner

AISTATS 2016 Survey Propagation Beyond Constraint Satisfaction Problems Christopher Srinivasa, Siamak Ravanbakhsh, Brendan J. Frey

NeurIPS 2015 Embedding Inference for Structured Multilabel Prediction Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans

JMLR 2015 Perturbed Message Passing for Constraint Satisfaction Problems Siamak Ravanbakhsh, Russell Greiner

NeurIPS 2014 Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner

ICML 2014 Min-Max Problems on Factor Graphs Siamak Ravanbakhsh, Christopher Srinivasa, Brendan Frey, Russell Greiner

ICML 2012 A Generalized Loop Correction Method for Approximate Inference in Graphical Models Siamak Ravanbakhsh, Chun-Nam Yu, Russell Greiner