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WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?

We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on …

Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vazquez, Nicolas Chapados, Alexandre Lacoste

Workshop at the International Conference of Learning Representation (ICLR), 2024.

Towards Disentangled High-level Causal Explanations in Text

In this work, we propose a causal representation learning framework for learning disentangled and intervenable high-level explanations …

Navita Goyal, Hal Daumé III, Alexandre Drouin, Dhanya Sridhar

Mid-Atlantic Student Colloquium on Speech, Language and Learning, 2024.

A Sparsity Principle for Partially Observable Causal

A Sparsity Principle for Partially Observable Causal

Causal representation learning (CRL) aims at identifying high-level causal variables from low-level data, e.g. images. Current methods …

Danru Xu, Dingling Yao, Perouz Taslakian, Sébastien Lachapelle, Julius von Kügelgen, Francesco Locatello, Sara Magliacane

Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

Capture the Flag: Uncovering Data Insights with Large Language Models

Capture the Flag: Uncovering Data Insights with Large Language Models

The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. …

Issam H. Laradji, Perouz Taslakian, Sai Rajeswar Mudumba, Valentina Zantedeschi, Alexandre Lacoste, Nicolas Chapados, David Vazquez, Christopher Pal, Alexandre Drouin

Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting

Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting

In this work, we present Lag-Llama, a general-purpose probabilistic time series forecasting model trained on a large collection of time …

Kashif Rasul, Arjun Ashok, Marin Bilos, Andrew Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Hena Ghonia, Nadhir Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish

Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

Multi-View Causal Representation Learning with Partial Observability

Multi-View Causal Representation Learning with Partial Observability

We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as …

Dingling Yao, Danru Xu, Perouz Taslakian, Sébastien Lachapelle, Sara Magliacane, Julius von Kügelgen, Francesco Locatello

Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism

Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism

Optimizing large memory-intensive neural networks requires distributing its layers across multiple GPUs (referred to as model …

Reza Asad, Reza Babanezhad, Issam H. Laradji, Nicolas Le Roux, Sharan Vaswani

Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

The Unsolved Challenges of LLMs in Open-Ended Web Tasks: A Case Study

In this work, we investigate the challenges associated with developing goal-driven AI agents capable of performing open-ended tasks in …

Rim Assouel, Tom Marty, Massimo Caccia, Issam H. Laradji, Alexandre Drouin, Sai Rajeswar Mudumba, Hector Palacios, Quentin Cappart, David Vazquez, Nicolas Chapados, Maxime Gasse, Alexandre Lacoste

Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory

The accurate modeling of dynamics in interactive environments is critical for suc- cessful long-range prediction. Such a capability …

Arnab Mondal, Sai Rajeswar Mudumba, Siamak Ravanbakhsh, Siba Smarak Panigrahi

European Workshop on Reinforcement Learning (EWRL), 2023.

Invariant Causal Set Covering Machines

Invariant Causal Set Covering Machines

Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms …

Thibaud Godon, Baptiste Bauvin, Pascal Germain, Jacques Corbeil, Alexandre Drouin

Workshop on Spurious Correlations, Invariance, and Stability (ICML), 2023.