Publications

A growing body of computational studies shows that simple machine learning agents converge to cooperative behaviors in social dilemmas, such… (see more) as collusive price-setting in oligopoly markets, raising questions about what drives this outcome. In this work, we provide theoretical foundations for this phenomenon in the context of self-play multi-agent Q-learners in the iterated prisoner’s dilemma. We characterize broad conditions under which such agents provably learn the cooperative Pavlov (win-stay, lose-shift) policy rather than the Pareto-dominated “always defect” policy. We validate our theoretical results through additional experiments, demonstrating their robustness across a broader class of deep learning algorithms.

2025-05-01

ICML.cc/2025/Conference (poster)

openreview.net

SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting

Boris Oreshkin

Koopman operator theory provides a framework for nonlinear dynamical system analysis and time-series forecasting by mapping dynamics to a sp… (see more)ace of real-valued measurement functions, enabling a linear operator representation. Despite the advantage of linearity, the operator is generally infinite-dimensional. Therefore, the objective is to learn measurement functions that yield a tractable finite-dimensional Koopman operator approximation. In this work, we establish a connection between Koopman operator approximation and linear Recurrent Neural Networks (RNNs), which have recently demonstrated remarkable success in sequence modeling. We show that by considering an extended state consisting of lagged observations, we can establish an equivalence between a structured Koopman operator and linear RNN updates. Building on this connection, we present SKOLR, which integrates a learnable spectral decomposition of the input signal with a multilayer perceptron (MLP) as the measurement functions and implements a structured Koopman operator via a highly parallel linear RNN stack. Numerical experiments on various forecasting benchmarks and dynamical systems show that this streamlined, Koopman-theory-based design delivers exceptional performance. Our code is available at: https://github.com/networkslab/SKOLR.

2025-05-01

ICML.cc/2025/Conference (poster)

doi.org

openreview.net

Structure-Aligned Protein Language Model

Can Chen

David Heurtel-Depeiges

Robert M. Vernon

Christopher J. Langmead

Yoshua Bengio

Quentin Fournier

2025-05-01

arXiv (published)

doi.org

arxiv.org

The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions

Devin Kwok

Gül Sena Altıntaş

Colin Raffel

David Rolnick

Neural network training is inherently sensitive to initialization and the randomness induced by stochastic gradient descent. However, it is … (see more)unclear to what extent such effects lead to meaningfully different networks, either in terms of the models' weights or the underlying functions that were learned. In this work, we show that during the initial "chaotic" phase of training, even extremely small perturbations reliably causes otherwise identical training trajectories to diverge-an effect that diminishes rapidly over training time. We quantify this divergence through (i)

2025-05-01

ICML.cc/2025/Conference (poster)

doi.org

openreview.net

Leveraging AI for a Sustainable Future

Mila AI Policy Fellowship

Hugo Larochelle appointed Scientific Director of Mila

Publications

Leveraging AI for a Sustainable Future

Mila AI Policy Fellowship

Hugo Larochelle appointed Scientific Director of Mila

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Publications