Portrait of Hiroki Naganuma

Hiroki Naganuma

PhD - Université de Montréal
Supervisor
Ioannis Mitliagkas
Research Topics
Deep Learning
Optimization
Out-of-Distribution (OOD) Generalization
Website
Google Scholar
GitHub

Publications

An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration
Hiroki Naganuma
Ryuichiro Hataya
Kotaro Yoshida
Ioannis Mitliagkas
2025-01-01
Trans. Mach. Learn. Res. (published)
openreview.net
openreview.net
Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training
Hiroki Naganuma
Xinzhi Zhang
Man-Chung Yue
Ioannis Mitliagkas
Russell J. Hewett
Philipp Andre Witte
Yin Tat Lee
Recent trends of larger model and larger datasets require huge amounts of computational resources, making distributed deep learning essentia… (see more)l. Data parallelism is a common approach to speed up training, but it often involves frequent communication between workers, which can be a bottleneck. In this work, we propose a method called Pseudo-Asynchronous Local SGD (PALSGD) to improve the efficiency of data-parallel training. PALSGD is a novel extension of LocalSGD (SU Stich, 2018), designed to further reduce communication frequency by introducing a pseudo-synchronization mechanism. PALSGD allows the use of longer synchronization intervals compared to standard LocalSGD. Despite the reduced communication frequency, the pseudo-synchronization approach ensures that model consistency is maintained, leading to performance results comparable to those achieved with more frequent synchronization. Furthermore, we provide a theoretical analysis of PALSGD, establishing its convergence and deriving its convergence rate. This analysis offers insights into the algorithm's behavior and performance guarantees. We evaluated PALSGD on CIFAR-10 using a CNN and GPT-NEO on TinyStories. Our results show that PALSGD achieves better performance in less time compared to existing methods like distributed data parallel (DDP), Local SGD and DiLoCo (Douillard et al. 2023).
2024-10-10
NeurIPS.cc/2024/Workshop/OPT (published)
openreview.net
openreview.net
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
Charles Guille-escuret
Hiroki Naganuma
Kilian FATRAS
Ioannis Mitliagkas
Understanding the optimization dynamics of neural networks is necessary for closing the gap between theory and practice. Stochastic first-or… (see more)der optimization algorithms are known to efficiently locate favorable minima in deep neural networks. This efficiency, however, contrasts with the non-convex and seemingly complex structure of neural loss landscapes. In this study, we delve into the fundamental geometric properties of sampled gradients along optimization paths. We focus on two key quantities, which appear in the restricted secant inequality and error bound. Both hold high significance for first-order optimization. Our analysis reveals that these quantities exhibit predictable, consistent behavior throughout training, despite the stochasticity induced by sampling minibatches. Our findings suggest that not only do optimization trajectories never encounter significant obstacles, but they also maintain stable dynamics during the majority of training. These observed properties are sufficiently expressive to theoretically guarantee linear convergence and prescribe learning rate schedules mirroring empirical practices. We conduct our experiments on image classification, semantic segmentation and language modeling across different batch sizes, network architectures, datasets, optimizers, and initialization seeds. We discuss the impact of each factor. Our work provides novel insights into the properties of neural network loss functions, and opens the door to theoretical frameworks more relevant to prevalent practice.
2024-07-08
Proceedings of the 41st International Conference on Machine Learning (published)
doi.org
openreview.net
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 … (see more)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.
2024-03-05
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.… (see more) 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.
2023-06-16
TMLR (accepted)
doi.org
openreview.net