152 citations · 154 across the 14 of their papers we have counts for
14 papers
Unsupervised Causal Abstractions Discovery
Théo Saulus, Simon Lacoste-Julien, Dhanya Sridhar
Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM. Existing applications of this notion largel…
Accelerated and Stable Convergence with Anchored Generalized Optimistic Method
Motahareh Sohrabi, Jianxin You, Simon Lacoste-Julien +2
We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the extragradient method rely on two gradie…
The Role of Causal Features in Strategic Classification for Robustness and Alignment
Antonio Gois, Sophia Gunluk, Nir Rosenfeld +3
In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility in a classification task (e.g., loan repa…
Reparametrizing Shampoo and SOAP for Subspace Basis Updates and BFloat16 Storage
Alan Milligan, Zikun Xu, Simon Lacoste-Julien +2
Shampoo-based methods, such as KL-Shampoo and SOAP, have demonstrated strong performance in training neural networks and rely on QR decomposition. Because existing QR implementatio…
Layerwise LQR for Geometry-Aware Optimization of Deep Networks
Simon Dufort-Labbé, Pierre-Luc Bacon, Razvan Pascanu +2
Geometry-aware optimizers such as Newton and natural gradient can improve conditioning in deep learning, but scalable variants such as K-FAC, Shampoo, and related preconditioners u…
Dual Optimistic Ascent (PI Control) is the Augmented Lagrangian Method in Disguise
Juan Ramirez, Simon Lacoste-Julien
Constrained optimization is a powerful framework for enforcing requirements on neural networks. These constrained deep learning problems are typically solved using first-order meth…