Kartik Ahuja

Simon Lacoste-Julien

Pascal Vincent

2025-06-09

ICML.cc/2025/Workshop/SIM (poster)

On the Identifiability of Quantized Factors

Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the… (voir plus) theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.

2024-03-15

Proceedings of the Third Conference on Causal Learning and Reasoning (publié)

proceedings.mlr.press

arxiv.org

Reusable Slotwise Mechanisms

Trang Nguyen

Amin Mansouri

Kanika Madan

Khuong N. Nguyen

Nguyen Duy Khuong

Dianbo Liu

Yoshua Bengio

Agents with the ability to comprehend and reason about the dynamics of objects would be expected to exhibit improved robustness and generali… (voir plus)zation in novel scenarios. However, achieving this capability necessitates not only an effective scene representation but also an understanding of the mechanisms governing interactions among object subsets. Recent studies have made significant progress in representing scenes using object slots. In this work, we introduce Reusable Slotwise Mechanisms, or RSM, a framework that models object dynamics by leveraging communication among slots along with a modular architecture capable of dynamically selecting reusable mechanisms for predicting the future states of each object slot. Crucially, RSM leverages the Central Contextual Information (CCI), enabling selected mechanisms to access the remaining slots through a bottleneck, effectively allowing for modeling of higher order and complex interactions that might require a sparse subset of objects. Experimental results demonstrate the superior performance of RSM compared to state-of-the-art methods across various future prediction and related downstream tasks, including Visual Question Answering and action planning. Furthermore, we showcase RSM's Out-of-Distribution generalization ability to handle scenes in intricate scenarios.

Jean-christophe Gagnon-audet

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series

Mohammad Javad Darvishi Bayazi

Pooneh Mousavi

Guillaume Dumas

Irina Rish

2023-09-01

TMLR (accepté)

On the Identifiability of Quantized Factors

Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (voir plus)theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.

2023-06-28

ArXiv (prépublication)

arxiv.org

Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection

Vitória Barin-Pacela

Simon Lacoste-Julien

Pascal Vincent

Disentanglement aims to recover meaningful latent ground-truth factors from only the observed distribution. Identifiability provides the the… (voir plus)oretical grounding for disentanglement to be well-founded. Unfortunately, unsupervised identifiability of independent latent factors is a theoretically proven impossibility in the i.i.d. setting under a general nonlinear smooth map from factors to observations. In this work, we show that, remarkably, it is possible to recover discretized latent coordinates under a highly generic nonlinear smooth mapping (a diffeomorphism) without any additional inductive bias on the mapping. This is, assuming that latent density has axis-aligned discontinuity landmarks, but without making the unrealistic assumption of statistical independence of the factors. We introduce this novel form of identifiability, termed quantized coordinate identifiability , and provide a comprehensive proof of the recovery of discretized coordinates.

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection

Vitória Barin-Pacela

Simon Lacoste-Julien

Pascal Vincent

2023-06-19

ICML.cc/2023/Workshop/SPIGM (poster)

Empirical Study on Optimizer Selection for Out-of-Distribution Generalization

Hiroki Naganuma

Ioannis Mitliagkas

Shiro Takagi

Tetsuya Motokawa

Rio Yokota

Kohta Ishikawa

Ikuro Sato

Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution.… (voir plus) While much promising work has been accomplished to address this fragility, a systematic study of the role of optimizers and their out-of-distribution generalization performance has not been undertaken. In this study, we examine the performance of popular first-order optimizers for different classes of distributional shift under empirical risk minimization and invariant risk minimization. We address this question for image and text classification using DomainBed, WILDS, and Backgrounds Challenge as testbeds for studying different types of shifts---namely correlation and diversity shift. We search over a wide range of hyperparameters and examine classification accuracy (in-distribution and out-of-distribution) for over 20,000 models. We arrive at the following findings, which we expect to be helpful for practitioners: i) adaptive optimizers (e.g., Adam) perform worse than non-adaptive optimizers (e.g., SGD, momentum SGD) on out-of-distribution performance. In particular, even though there is no significant difference in in-distribution performance, we show a measurable difference in out-of-distribution performance. ii) in-distribution performance and out-of-distribution performance exhibit three types of behavior depending on the dataset---linear returns, increasing returns, and diminishing returns. For example, in the training of natural language data using Adam, fine-tuning the performance of in-distribution performance does not significantly contribute to the out-of-distribution generalization performance.

2023-06-16

TMLR (accepté)

Interventional Causal Representation Learning

Yixin Wang

Divyat Mahajan

Yoshua Bengio

Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Most existing methods rely on observa… (voir plus)tional data and structural assumptions (e.g., conditional independence) to identify the latent factors. However, interventional data is prevalent across applications. Can interventional data facilitate causal representation learning? We explore this question in this paper. The key observation is that interventional data often carries geometric signatures of the latent factors' support (i.e. what values each latent can possibly take). For example, when the latent factors are causally connected, interventions can break the dependency between the intervened latents' support and their ancestors'. Leveraging this fact, we prove that the latent causal factors can be identified up to permutation and scaling given data from perfect

2023-04-24

ICML.cc/2023/Conference (poster)

Object-centric causal representation learning

Amin Mansouri

Jason Hartford

Yoshua Bengio

2022-11-07

NeurIPS.cc/2022/Workshop/NeurReps (poster)

FL Games: A federated learning framework for distribution shifts

Niladri Chatterjee

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However… (voir plus), participating clients typically each hold data from a different distribution, whereby predictive models with strong in-distribution generalization can fail catastrophically on unseen domains. In this work, we argue that in order to generalize better across non-i.i.d. clients, it is imperative to only learn correlations that are stable and invariant across domains. We propose FL Games, a game-theoretic framework for federated learning for learning causal features that are invariant across clients. While training to achieve the Nash equilibrium, the traditional best response strategy suffers from high-frequency oscillations. We demonstrate that FL Games effectively resolves this challenge and exhibits smooth performance curves. Further, FL Games scales well in the number of clients, requires significantly fewer communication rounds, and is agnostic to device heterogeneity. Through empirical evaluation, we demonstrate that FL Games achieves high out-of-distribution performance on various benchmarks.

2022-10-20

NeurIPS.cc/2022/Workshop/Federated_Learning (présentation orale)