長沼 大樹 / Hiroki Naganuma
Research Engineer (Deep Learning Software Engineer), NVIDIA · Convergence for High-Efficiency Formulations (CHEF)
長沼 大樹 (Hiroki Naganuma) は NVIDIA の Research Engineer (Deep Learning Software Engineer) です。2026 年 10 月より慶應義塾大学の Visiting Assistant Professor を兼任します。大規模最適化、分散深層学習、大規模言語モデルの効率的な学習に取り組んでいます。
博士論文: Toward Efficient and Scalable Optimization: Theoretical Insights and Practical Challenges
hiroki11x@gmail.com · ORCID · Google Scholar · researchmap · J-GLOBAL
研究分野
主要・最新論文
- From Inner Randomness to Outer Stability — Under Review (2026)
- Generalization Measures under Controlled Covariate Shift: A Regime-Aware Benchmark — TMLR 2026 (2026)
- Adaptive Batch Sizes Using Non-Euclidean Gradient Noise Scales for Stochastic Sign and Spectral Descent — ICML 2026 (2026)
- What do near-optimal learning rate schedules look like? — TMLR 2026 (2026)
- Which Geometry on Which Layer? A Principled Criterion for Mixed-Optimizer Training — Under Review (2026)
- Orth-Dion: Eliminating Geometric Mismatch in Distributed Low-Rank Spectral Optimization — Under Review (2026)
- The Geometry of Spectral Gradient Descent: Layerwise Criteria for SignSGD vs SpecSGD — ICLR 2026 Workshop (2026)
- On Fairness of Task Arithmetic: The Role of Task Vectors — ICLR 2026 (2026)
- DiTaC: Conditioning Task Vectors via Distillation for Robust Model Merging — ICLR 2026 (2026)
- Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training — TMLR 2025 (2025)