Portrait de Hiroki Naganuma

Hiroki Naganuma

naganuma.hiroki@mila.quebec
Doctorat - UdeM
Superviseur⋅e principal⋅e
Ioannis Mitliagkas
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Généralisation hors distribution (OOD)
Modèles de fondation
Optimisation
Paysages de perte
Robustesse
Systèmes distribués
Site web
Google Scholar
GitHub

Publications

Smoothness-Adaptive Sharpness-Aware Minimization for Finding Flatter Minima
Hiroki Naganuma
Junhyung Lyle Kim
Anastasios Kyrillidis
Ioannis Mitliagkas
The sharpness-aware minimization (SAM) procedure recently gained increasing attention due to its favorable generalization ability to unseen … (voir plus)data. SAM aims to find flatter (local) minima, utilizing a minimax objective. An immediate challenge in the application of SAM is the adjustment of two pivotal step sizes, which significantly influence its effectiveness. We introduce a novel, straightforward approach for adjusting step sizes that adapts to the smoothness of the objective function, thereby reducing the necessity for manual tuning. This method, termed Smoothness-Adaptive SAM (SA-SAM), not only simplifies the optimization process but also promotes the method's inherent tendency to converge towards flatter minima, enhancing performance in specific models.
2024-03-04
ICLR.cc/2024/Workshop/PML4LRS (poster)
openreview.net
openreview.net
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Hiroki Naganuma
Kartik Ahuja
Ioannis Mitliagkas
Shiro Takagi
Tetsuya Motokawa
Rio Yokota
Kohta Ishikawa
Ikuro Sato
Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution.… (voir plus) While much promising work has been accomplished to address this fragility, a systematic study of the role of optimizers and their out-of-distribution generalization performance has not been undertaken. In this study, we examine the performance of popular first-order optimizers for different classes of distributional shift under empirical risk minimization and invariant risk minimization. We address this question for image and text classification using DomainBed, WILDS, and Backgrounds Challenge as testbeds for studying different types of shifts---namely correlation and diversity shift. We search over a wide range of hyperparameters and examine classification accuracy (in-distribution and out-of-distribution) for over 20,000 models. We arrive at the following findings, which we expect to be helpful for practitioners: i) adaptive optimizers (e.g., Adam) perform worse than non-adaptive optimizers (e.g., SGD, momentum SGD) on out-of-distribution performance. In particular, even though there is no significant difference in in-distribution performance, we show a measurable difference in out-of-distribution performance. ii) in-distribution performance and out-of-distribution performance exhibit three types of behavior depending on the dataset---linear returns, increasing returns, and diminishing returns. For example, in the training of natural language data using Adam, fine-tuning the performance of in-distribution performance does not significantly contribute to the out-of-distribution generalization performance.
2023-06-15
TMLR (accepté)
doi.org
openreview.net