Copertina

Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Goyal, Saumya, Rongali, Rohith, Ray, Ritabrata, Póczos, Barnabás
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention
di: Liao, Tongzhou, et al.
Pubblicazione: (2024)

GraphBPE: Molecular Graphs Meet Byte-Pair Encoding
di: Shen, Yuchen, et al.
Pubblicazione: (2024)

Recovering Time-Varying Networks From Single-Cell Data
di: Hasanaj, Euxhen, et al.
Pubblicazione: (2024)

Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design
di: Alakhdar, Amira, et al.
Pubblicazione: (2025)

Diffusion Models in $\textit{De Novo}$ Drug Design
di: Alakhdar, Amira, et al.
Pubblicazione: (2024)

Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks
di: Balcan, Maria-Florina, et al.
Pubblicazione: (2025)

Controllable Text Generation in the Instruction-Tuning Era
di: Ashok, Dhananjay, et al.
Pubblicazione: (2024)

Generalization Guarantees of Gradient Descent for Multi-Layer Neural Networks
di: Wang, Puyu, et al.
Pubblicazione: (2023)

Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation
di: Das, Adrita, et al.
Pubblicazione: (2026)

Statistical Guarantees for High-Dimensional Stochastic Gradient Descent
di: Li, Jiaqi, et al.
Pubblicazione: (2025)

Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
di: Chien, Eli, et al.
Pubblicazione: (2024)

Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling
di: Hsu, Alan, et al.
Pubblicazione: (2024)

Chemistry-Inspired Diffusion with Non-Differentiable Guidance
di: Shen, Yuchen, et al.
Pubblicazione: (2024)

Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
di: Buskulic, Nathan, et al.
Pubblicazione: (2024)

Gradient Descent with Provably Tuned Learning-rate Schedules
di: Sharma, Dravyansh
Pubblicazione: (2025)

Dynamic Scaled Gradient Descent for Stable Fine-Tuning for Classifications
di: Bui, Nghia, et al.
Pubblicazione: (2026)

Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization
di: Feng, Hanqi, et al.
Pubblicazione: (2025)

Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent
di: Wu, Tong
Pubblicazione: (2025)

On the Generalization of Stochastic Gradient Descent with Momentum
di: Ramezani-Kebrya, Ali, et al.
Pubblicazione: (2018)

The Implicit Bias of Gradient Descent on Separable Data
di: Soudry, Daniel, et al.
Pubblicazione: (2017)

Gradient Manipulation in Distributed Stochastic Gradient Descent with Strategic Agents: Truthful Incentives with Convergence Guarantees
di: Chen, Ziqin, et al.
Pubblicazione: (2026)

Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
di: Chen, Hao, et al.
Pubblicazione: (2021)

Stochastic Gradient Descent with Adaptive Data
di: Che, Ethan, et al.
Pubblicazione: (2024)

Data-Driven Target Localization: Benchmarking Gradient Descent Using the Cramer-Rao Bound
di: Venkatasubramanian, Shyam, et al.
Pubblicazione: (2024)

The Implicit Bias of Gradient Descent on Separable Multiclass Data
di: Ravi, Hrithik, et al.
Pubblicazione: (2024)

Occam Gradient Descent
di: Kausik, B. N.
Pubblicazione: (2024)

Improved Guarantees for Langevin Monte Carlo with Average Smoothness
di: Dalalyan, Arnak S., et al.
Pubblicazione: (2026)

Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees
di: Armacki, Aleksandar, et al.
Pubblicazione: (2024)

Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
di: Birrell, Jeremiah, et al.
Pubblicazione: (2024)

Generalized Gradient Descent is a Hypergraph Functor
di: Hanks, Tyler, et al.
Pubblicazione: (2024)

Stacking as Accelerated Gradient Descent
di: Agarwal, Naman, et al.
Pubblicazione: (2024)

Generalization Bounds of Stochastic Gradient Descent in Homogeneous Neural Networks
di: Ma, Wenquan, et al.
Pubblicazione: (2026)

On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
di: Li, Bingrui, et al.
Pubblicazione: (2024)

Gradient Transformer: Learning to Generate Updates for LLMs
di: Nguyen, Binh-Nguyen, et al.
Pubblicazione: (2026)

Spiral Model Technique For Data Science & Machine Learning Lifecycle
di: Mahadevan, Rohith
Pubblicazione: (2025)

Multi-Step Alignment as Markov Games: An Optimistic Online Gradient Descent Approach with Convergence Guarantees
di: Wu, Yongtao, et al.
Pubblicazione: (2025)

Stochastic Adaptive Gradient Descent Without Descent
di: Aujol, Jean-François, et al.
Pubblicazione: (2025)

Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
di: Laus, Hannah, et al.
Pubblicazione: (2025)

Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
di: Zhou, Han, et al.
Pubblicazione: (2025)

Corner Gradient Descent
di: Yarotsky, Dmitry
Pubblicazione: (2025)