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Gwen Legate

Doctorat - Concordia
Superviseur⋅e principal⋅e
Eugene Belilovsky
Co-supervisor
Irina Rish
Sujets de recherche
Apprentissage profond
Systèmes distribués
Google Scholar
GitHub

Publications

Feature Geometry of Language Models Transfer Across Modalities to Time Series
Prateek Humane
Alexis Roger
Zhenghan Tai
Gwen Legate
Andrei Mircea
Vasilii Feofanov
Irina Rish
Language models transfer to time-series forecasting, but it is unclear whether this reflects reusable internal structure or rapid relearning… (voir plus) under a familiar architecture. We study this transfer directly by comparing pretrained and randomly initialized versions of the same model on a forecasting objective whose inputs have little semantic overlap with text but still require autoregressive sequential structure. Across Qwen3-0.6B finetuning experiments, language initialization gives coherent per-example gradients from the first update, while random initialization first passes through a low-alignment warmup phase. Effective-rank and hidden-state analyses show that finetuning selectively reshapes an existing representation geometry rather than constructing the simpler temporal geometry found by models trained from scratch. Cross-domain sparse features and causal ablations then expose candidate transferred primitives, including a Layer~1 head--MLP circuit whose ablation selectively increases loss on periodic forecasting and repetitive language passages. These results support an account of cross-modal transfer in which autoregressive pretraining creates temporal feature geometry that can be selected and specialized outside language.
2026-06-10
ICML.cc/2026/Workshop/Mech_Interp (poster)
openreview.net
openreview.net
Forecasting Emerges from Auto-Regressive Pretraining: Latent Predictive Structure in Language Models
Alexis Roger
Prateek Humane
Zhenghan Tai
Gwen Legate
Andrei Mircea
Vasilii Feofanov
Irina Rish
Predicting how a sequence will continue is a basic problem for intelligent systems. We show that large language models contain usable foreca… (voir plus)sting structure before any explicit time-series supervision. A single linear readout from frozen Qwen3-0.6B hidden states maps ordinary text sequences to numerical trajectories that resemble real time series, and those trajectories can be used for straightforward forecasts. The distribution over output tokens also gives coherent, non-crossing probabilistic forecasts in a single forward pass. After time-series specialization, pretrained models show aligned gradients and improve immediately, whereas randomly initialized models spend early training in a destructive-interference regime. These findings suggest that auto-regressive pretraining already shapes representations around temporal continuation; and finetuning adapts that structure to numerical forecasting rather than creating it from scratch.
2026-06-10
ICML.cc/2026/Workshop/Forecast (présentation orale)
openreview.net
openreview.net
LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
Alexis Roger
Prateek Humane
Zhenghan Tai
Gwen Legate
Andrei Mircea
Vasilii Feofanov
Irina Rish
Can language-pretrained transformers become effective time-series forecasters, and why? In this paper, we show that cross-modal transfer ari… (voir plus)ses because language pretraining preconditions time series training with a reusable manifold. A linear probe on frozen LLM states decodes realistic time-series trajectories without paired supervision, and retrieval in this projected space yields competitive forecasts, showing that structure and dynamics exist before finetuning. Pretrained initialization also improves optimization, producing coherent gradients and a highly anisotropic loss landscape unlike random initialization. Finetuning then acts as low-dimensional alignment, reusing existing directions rather than learning temporal primitives from scratch, as evidenced by low-rank updates, subspace alignment, and shared features for periodicity, trend, and repetition. Together, these results support a geometric account of LLM-to-time-series transfer: language pretraining builds the manifold, and finetuning projects numerical dynamics onto task-relevant directions.
2026-05-18
arXiv (prépublication)
doi.org
arxiv.org
Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients
Gwen Legate
Irina Rish
Eugene Belilovsky
Federated learning enables collaborative model training across numerous edge devices without requiring participants to share data; however, … (voir plus)memory and communication constraints on these edge devices may preclude their participation in training. We consider a setting in which a subset of edge devices are below a critical memory or communication threshold required to conduct model updates. Under typical federated optimization algorithms, these devices are excluded from training which renders their data inaccessible and increases system induced bias. We are inspired by MeZO, a zeroth-order method used for memory-efficient fine-tuning. The increased variance inherent to zeroth-order gradient approximations has relegated previous zeroth-order optimizers exclusively to the domain of fine tuning; a limitation we seek to correct. We devise a federated, memory-efficient zeroth-order optimizer, ZOWarmUp that permits zeroth-order training from a random initialization. ZOWarmUp leverages differing client capabilities and careful variance reduction techniques to facilitate participation of under-represented, low-resource clients in model training. Like other federated zeroth-order methods, ZOWarmUp eliminates the need for edge devices to transmit their full gradients to the server and instead relies on only a small set of random seeds, rendering the up-link communication cost negligible. We present experiments using various datasets and model architectures to show that ZOWarmUp is a robust algorithm that can can be applied under a wide variety of circumstances. For systems with a high proportion of edge devices that would otherwise be excluded from training, this algorithm provides access to a greater volume and diversity of data, thus improving training outcomes.
2025-09-02
ArXiv (prépublication)
doi.org
arxiv.org
Guiding The Last Layer in Federated Learning with Pre-Trained Models
Gwen Legate
Nicolas Bernier
Lucas Caccia
Edouard Oyallon
Eugene Belilovsky
Federated Learning (FL) is an emerging paradigm that allows a model to be trained across a number of participants without sharing data. Rece… (voir plus)nt works have begun to consider the effects of using pre-trained models as an initialization point for existing FL algorithms; however, these approaches ignore the vast body of efficient transfer learning literature from the centralized learning setting. Here we revisit the problem of FL from a pre-trained model considered in prior work and expand it to a set of computer vision transfer learning problems. We first observe that simply fitting a linear classification head can be efficient and effective in many cases. We then show that in the FL setting, fitting a classifier using the Nearest Class Means (NCM) can be done exactly and orders of magnitude more efficiently than existing proposals, while obtaining strong performance. Finally, we demonstrate that using a two-phase approach of obtaining the classifier and then fine-tuning the model can yield rapid convergence and improved generalization in the federated setting. We demonstrate the potential our method has to reduce communication and compute costs while achieving better model performance.
2023-12-11
Neural Information Processing Systems (Accept (poster))
doi.org
openreview.net
Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning
Gwen Legate
Lucas Caccia
Eugene Belilovsky
In Federated Learning a global model is learned by aggregating model updates computed at a set of independent client nodes. To reduce commun… (voir plus)ication costs, multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives. This can lead clients to overly minimize their own local objective consequently diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client’s label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low.
2022-12-31
CoLLAs (publié)
doi.org
proceedings.mlr.press