Portrait de Katie Everett n'est pas disponible

Katie Everett

Alumni

Publications

Dynamics of Stochastic Momentum with Sparse Updates in High Dimensions
Katie Everett
Elliot Paquette
Existing theory of momentum assumes that gradients arrive at every parameter at a roughly constant rate, an assumption violated in practice … (voir plus)by heavy-tailed data distributions and modern architectures. We theoretically analyze the dynamics of two tractable models of momentum under sparse updates: a least squares model with sparse inputs and a logistic regression model with a rare class. Both admit exact closed-form second-moment dynamics whose high-dimensional limits we characterize across three scaling exponents for sparsity, batch size, and momentum decay. The phase structure on both problems is governed by the ratio of two intrinsic timescales: a momentum retention timescale (how many active updates the buffer survives) and a learning timescale (how many active updates it takes to reduce the squared error). When learning is much slower than retention, the limit matches SGD; when learning is faster, the system is unstable; where the timescales coincide, we recover classical heavy-ball dynamics. The oscillatory dynamics occur at different momentum values for different token sparsity, creating a spectral conflict for global momentum across token frequencies.
2026-05-26
arXiv (prépublication)
doi.org
arxiv.org
Logarithmic-time Schedules for Scaling Language Models with Momentum
Damien Ferbach
Courtney Paquette
Gauthier Gidel
Katie Everett
Elliot Paquette
In practice, the hyperparameters …
2026-02-04
arXiv (prépublication)
doi.org
arxiv.org
Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Gauthier Gidel
Elliot Paquette
Courtney Paquette
We investigate scaling laws for stochastic momentum algorithms with small batch on the power law random features model, parameterized by dat… (voir plus)a complexity, target complexity, and model size. When trained with a stochastic momentum algorithm, our analysis reveals four distinct loss curve shapes determined by varying data-target complexities. While traditional stochastic gradient descent with momentum (SGD-M) yields identical scaling law exponents to SGD, dimension-adapted Nesterov acceleration (DANA) improves these exponents by scaling momentum hyperparameters based on model size and data complexity. This outscaling phenomenon, which also improves compute-optimal scaling behavior, is achieved by DANA across a broad range of data and target complexities, while traditional methods fall short. Extensive experiments on high-dimensional synthetic quadratics validate our theoretical predictions and large-scale text experiments with LSTMs show DANA's improved loss exponents over SGD hold in a practical setting.
2025-09-17
NeurIPS.cc/2025/Conference (spotlight)
doi.org
openreview.net
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
Sébastien Lachapelle
Pau Rodríguez
Yash Sharma
Katie Everett
Rémi Le Priol
Alexandre Lacoste
Simon Lacoste-Julien
2024-01-09
ArXiv (prépublication)
doi.org
arxiv.org
GFlowNet-EM for Learning Compositional Latent Variable Models
Edward J. Hu
Nikolay Malkin
Moksh Jain
Katie Everett
Alexandros Graikos
Yoshua Bengio
Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large nu… (voir plus)mber of possible configurations of the latents. A key tradeoff in modeling the posteriors over latents is between expressivity and tractable optimization. For algorithms based on expectation-maximization (EM), the E-step is often intractable without restrictive approximations to the posterior. We propose the use of GFlowNets, algorithms for sampling from an unnormalized density by learning a stochastic policy for sequential construction of samples, for this intractable E-step. By training GFlowNets to sample from the posterior over latents, we take advantage of their strengths as amortized variational inference algorithms for complex distributions over discrete structures. Our approach, GFlowNet-EM, enables the training of expressive LVMs with discrete compositional latents, as shown by experiments on non-context-free grammar induction and on images using discrete variational autoencoders (VAEs) without conditional independence enforced in the encoder.
2023-07-02
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
GFlowNets and Variational Inference
Nikolay Malkin
Salem Lahlou
Tristan Deleu
Xu Ji
Edward Hu
Katie Everett
Dinghuai Zhang
Yoshua Bengio
This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically us… (voir plus)ed to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demonstrate that, in certain cases, VI algorithms are equivalent to special cases of GFlowNets in the sense of equality of expected gradients of their learning objectives. We then point out the differences between the two families and show how these differences emerge experimentally. Notably, GFlowNets, which borrow ideas from reinforcement learning, are more amenable than VI to off-policy training without the cost of high gradient variance induced by importance sampling. We argue that this property of GFlowNets can provide advantages for capturing diversity in multimodal target distributions.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net