Alexandre Drouin

Sai Rajeswar

Hector Palacios

Quentin Cappart

David Vázquez

Maxime Gasse

Alexandre Lacoste

2023-11-06

NeurIPS.cc/2023/Workshop/FMDM (publié)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Biloš

Hena Ghonia

Nadhir Hassen

Anderson Schneider

Sahil Garg

Yuriy Nevmyvaka

Irina Rish

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.

2023-10-31

NeurIPS.cc/2023/Workshop/R0-FoMo (poster)

GEO-Bench: Toward Foundation Models for Earth Monitoring

Alexandre Lacoste

Nils Lehmann

Pau Rodríguez

Evan David Sherwin

Hannah Kerner

Björn Lütjens

Jeremy Irvin

David Dao

Hamed Alemohammad

Mehmet Gunturkun

Dava Newman

Stefano Ermon

Xiao Xiang Zhu

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to subst… (voir plus)antial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed for image data, but their applicability to remote sensing tasks is limited. To stimulate the development of foundation models for Earth monitoring, we propose a benchmark comprised of six classification and six segmentation tasks, which were carefully curated and adapted to be both relevant to the field and well-suited for model evaluation. We accompany this benchmark with a robust methodology for evaluating models and reporting aggregated results to enable a reliable assessment of progress. Finally, we report results for 20 baselines to gain information about the performance of existing models. We believe that this benchmark will be a driver of progress across a variety of Earth monitoring tasks.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

Étienne Marcotte

Valentina Zantedeschi

Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expect… (voir plus)ation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. Through a power analysis, we identify the"region of reliability"of a scoring rule, i.e., the set of practical conditions where it can be relied on to identify forecasting errors. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions, and we gauge the generalizability of our findings to real-world tasks with an application to an electricity production problem. Our results reveal critical shortcomings in the evaluation of multivariate probabilistic forecasts as commonly performed in the literature.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

2023-06-18

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

Causal Discovery with Language Models as Imperfect Experts

Stephanie Long

Alexandre Piché

Valentina Zantedeschi

Tibor Schuster

Understanding the causal relationships that underlie a system is a fundamental prerequisite to accurate decision-making. In this work, we ex… (voir plus)plore how expert knowledge can be used to improve the data-driven identification of causal graphs, beyond Markov equivalence classes. In doing so, we consider a setting where we can query an expert about the orientation of causal relationships between variables, but where the expert may provide erroneous information. We propose strategies for amending such expert knowledge based on consistency properties, e.g., acyclicity and conditional independencies in the equivalence class. We then report a case study, on real data, where a large language model is used as an imperfect expert.

2023-06-18

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

Invariant Causal Set Covering Machines

Thibaud Godon

Baptiste Bauvin

Pascal Germain

J. Corbeil

2023-06-06

ArXiv (prépublication)

arxiv.org

RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data

Thibaud Godon

Pier-Luc Plante

Baptiste Bauvin

Élina Francovic-Fontaine

J. Corbeil

Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensio… (voir plus)nality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.

2022-08-10

ArXiv (prépublication)

arxiv.org

TACTiS: Transformer-Attentional Copulas for Time Series

Étienne Marcotte

The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, t… (voir plus)he practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.

2021-12-31

ICML (publié)

proceedings.mlr.press

Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites

Francois Laviolette

The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant m e… (voir plus)thodological d evelopments f rom t he field of machine learning. Such computational models are essential to help interpret the non-coding portion of human genomes, and to learn more about the regulatory mechanisms controlling gene expression. In parallel, massive genome sequencing efforts have produced assembled genomes for hundred of vertebrate species, but this data is underused. We present PhyloReg, a new semi-supervised learning approach that can be used for a wide variety of sequence-to-function prediction problems, and that takes advantage of hundreds of millions of years of evolution to regularize predictors and improve accuracy. We demonstrate that PhyloReg can be used to better train a previously proposed deep learning model of transcription factor binding. Simulation studies further help delineate the benefits o f t he a pproach. G ains in prediction accuracy are obtained over a broad set of transcription factors and cell types.

2020-12-15

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publié)

Differentiable Causal Discovery from Interventional Data

Alexandre Lacoste

Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solutio… (voir plus)n is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagrangian method. However, most methods based on this idea do not make use of interventional data, which can significantly alleviate identifiability issues. This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data. We illustrate the flexibility of the continuous-constrained framework by taking advantage of expressive neural architectures such as normalizing flows. We show that our approach compares favorably to the state of the art in a variety of settings, including perfect and imperfect interventions for which the targeted nodes may even be unknown.

2019-12-31

NeurIPS (publié)