Siamak Ravanbakhsh

2025-01-22

ICLR.cc/2025/Conference (poster)

On the Identifiability of Causal Abstractions

Xiusi Li

Sékou-Oumar Kaba

Causal representation learning methods seek to enhance machine learning models' robustness and generalization capabilities by learning laten… (voir plus)t representations and causal graphs aligned with the data generating process. In many systems, fully recovering the true causal structure is challenging because we cannot intervene on all latent variables individually. We introduce a theoretical framework that calculates the degree to which we can identify a causal structure in the more realistic setting of interventions on arbitrary subsets of latent variables. We find that in that case, we can only identify a causal model up to a \emph{causal abstraction}. These causal abstractions are still meaningful in that they describe the system at a higher level of granularity. Conversely, given a causal abstraction, our framework provides sufficient conditions for its identifiability. Our findings extend existing identifiability results in two areas: those that address abstractions of latent variables without considering graphical structures and those that focus on graphical structures without incorporating their abstractions.

2025-01-22

aistats.org/AISTATS/2025/Conference (poster)

Symmetry-Aware Generative Modeling through Learned Canonicalization

Kusha Sareen

Daniel Levy

Arnab Kumar Mondal

Sékou-Oumar Kaba

Tara Akhound-Sadegh

Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations. The ex… (voir plus)isting generative modeling paradigm for invariant densities combines an invariant prior with an equivariant generative process. However, we observe that this technique is not necessary and has several drawbacks resulting from the limitations of equivariant networks. Instead, we propose to model a learned slice of the density so that only one representative element per orbit is learned. To accomplish this, we learn a group-equivariant canonicalization network that maps training samples to a canonical pose and train a non-equivariant generative model over these canonicalized samples. We implement this idea in the context of diffusion models. Our preliminary experimental results on molecular modeling are promising, demonstrating improved sample quality and faster inference time.

2025-01-14

ArXiv (prépublication)

Symmetry-Aware Generative Modeling through Learned Canonicalization

Kusha Sareen

Daniel Levy

Arnab Kumar Mondal

Sékou-Oumar Kaba

Tara Akhound-Sadegh

2024-10-23

NeurIPS.cc/2024/Workshop/NeurReps (poster)

Sampling from Energy-based Policies using Diffusion

Vineet Jain

Tara Akhound-Sadegh

2024-10-02

ArXiv (prépublication)

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

Gauthier Gidel

Yoshua Bengio

Nikolay Malkin

Alexander Tong

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (voir plus)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient---and no data samples---to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is *simulation-free*, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-05-01

ICML.cc/2024/Conference (poster)

Weight-Sharing Regularization

Mehran Shakerinava

Motahareh Sohrabi

Simon Lacoste-Julien

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)

Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions

Thuan Nguyen Anh Trang

Khang Nhat Ngo

Hugo Sonnery

Thieu Vo

Truong Son Hy

Self-attention models have made great strides toward accurately modeling a wide array of data modalities, including, more recently, graph-st… (voir plus)ructured data. This paper demonstrates that adaptive hierarchical attention can go a long way toward successfully applying transformers to graphs. Our proposed model Sequoia provides a powerful inductive bias towards long-range interaction modeling, leading to better generalization. We propose an end-to-end mechanism for a data-dependent construction of a hierarchy which in turn guides the self-attention mechanism. Using adaptive hierarchy provides a natural pathway toward sparse attention by constraining node-to-node interactions with the immediate family of each node in the hierarchy (e.g., parent, children, and siblings). This in turn dramatically reduces the computational complexity of a self-attention layer from quadratic to log-linear in terms of the input size while maintaining or sometimes even surpassing the standard transformer's ability to model long-range dependencies across the entire input. Experimentally, we report state-of-the-art performance on long-range graph benchmarks while remaining computationally efficient. Moving beyond graphs, we also display competitive performance on long-range sequence modeling, point-clouds classification, and segmentation when using a fixed hierarchy. Our source code is publicly available at https://github.com/HySonLab/HierAttention

2024-04-11

TMLR (accepté)

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

Gauthier Gidel

Yoshua Bengio

Nikolay Malkin

Alexander Tong

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (voir plus)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient -- and no data samples -- to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-02-09

ArXiv (prépublication)

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

Gauthier Gidel

Yoshua Bengio

Nikolay Malkin

Alexander Tong

Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-… (voir plus)body systems, is a foundational problem in science. In this paper, we propose Iterated Denoising Energy Matching (iDEM), an iterative algorithm that uses a novel stochastic score matching objective leveraging solely the energy function and its gradient -- and no data samples -- to train a diffusion-based sampler. Specifically, iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our stochastic matching objective to further improve the sampler. iDEM is scalable to high dimensions as the inner matching objective, is simulation-free, and requires no MCMC samples. Moreover, by leveraging the fast mode mixing behavior of diffusion, iDEM smooths out the energy landscape enabling efficient exploration and learning of an amortized sampler. We evaluate iDEM on a suite of tasks ranging from standard synthetic energy functions to invariant

2024-02-09

ArXiv (prépublication)

E(3)-Equivariant Mesh Neural Networks

Thuan N.a. Trang

Nhat-Khang Ngô

Daniel Levy

Thieu N. Vo

Truong Son Hy

Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have address the need for geometric… (voir plus) deep learning on 3D mesh. However, we observe that the complexities in many of these architectures does not translate to practical performance, and simple deep models for geometric graphs are competitive in practice. Motivated by this observation, we minimally extend the update equations of E(n)-Equivariant Graph Neural Networks (EGNNs) (Satorras et al., 2021) to incorporate mesh face information, and further improve it to account for long-range interactions through hierarchy. The resulting architecture, Equivariant Mesh Neural Network (EMNN), outperforms other, more complicated equivariant methods on mesh tasks, with a fast run-time and no expensive pre-processing. Our implementation is available at https://github.com/HySonLab/EquiMesh

2024-02-07

ArXiv (prépublication)

On Diffusion Modeling for Anomaly Detection

Victor Livernoche

Vineet Jain

Yashar Hezaveh

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detectio… (voir plus)n. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.

2024-01-16

ICLR.cc/2024/Conference (spotlight)