Copertina

Provable Acceleration for Diffusion Models under Minimal Assumptions

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Gen, Cai, Changxiao
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Accelerating Convergence of Score-Based Diffusion Models, Provably
di: Li, Gen, et al.
Pubblicazione: (2024)

Confidence-Based Decoding is Provably Efficient for Diffusion Language Models
di: Cai, Changxiao, et al.
Pubblicazione: (2026)

Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
di: Liu, Xin, et al.
Pubblicazione: (2022)

A Novel Unified Parametric Assumption for Nonconvex Optimization
di: Riabinin, Artem, et al.
Pubblicazione: (2025)

One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
di: Li, Zihao, et al.
Pubblicazione: (2024)

Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization
di: Huang, Yu, et al.
Pubblicazione: (2025)

Agentic Transformers Provably Learn to Search via Reinforcement Learning
di: Yang, Tong, et al.
Pubblicazione: (2026)

Provably Safe Generative Sampling with Constricting Barrier Functions
di: Gadginmath, Darshan, et al.
Pubblicazione: (2026)

SMiLE: Provably Enforcing Global Relational Properties in Neural Networks
di: Francobaldi, Matteo, et al.
Pubblicazione: (2025)

Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
di: Xiao, Minheng, et al.
Pubblicazione: (2024)

Provable Offline Reinforcement Learning for Structured Cyclic MDPs
di: Lee, Kyungbok, et al.
Pubblicazione: (2026)

The Sharpness Disparity Principle in Transformers for Accelerating Language Model Pre-Training
di: Wang, Jinbo, et al.
Pubblicazione: (2025)

Federated Majorize-Minimization: Beyond Parameter Aggregation
di: Dieuleveut, Aymeric, et al.
Pubblicazione: (2025)

Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
di: Tsukamoto, Hiroyasu, et al.
Pubblicazione: (2022)

O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions
di: Li, Gen, et al.
Pubblicazione: (2024)

Breaking AR's Sampling Bottleneck: Provable Acceleration via Diffusion Language Models
di: Li, Gen, et al.
Pubblicazione: (2025)

When and How Unlabeled Data Provably Improve In-Context Learning
di: Li, Yingcong, et al.
Pubblicazione: (2025)

Understanding Sampler Stochasticity in Training Diffusion Models for RLHF
di: Sheng, Jiayuan, et al.
Pubblicazione: (2025)

Reward-Directed Score-Based Diffusion Models via q-Learning
di: Gao, Xuefeng, et al.
Pubblicazione: (2024)

Accelerating RLHF Training with Reward Variance Increase
di: Yang, Zonglin, et al.
Pubblicazione: (2025)

First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions
di: Shulgin, Egor, et al.
Pubblicazione: (2025)

Unsupervised Training of Diffusion Models for Feasible Solution Generation in Neural Combinatorial Optimization
di: Hong, Seong-Hyun, et al.
Pubblicazione: (2024)

YuriiFormer: A Suite of Nesterov-Accelerated Transformers
di: Zimin, Aleksandr, et al.
Pubblicazione: (2026)

Stability of Transformers under Layer Normalization
di: Kan, Kelvin, et al.
Pubblicazione: (2025)

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning
di: Zhao, Hanyang, et al.
Pubblicazione: (2025)

PID Accelerated Temporal Difference Algorithms
di: Bedaywi, Mark, et al.
Pubblicazione: (2024)

Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
di: Mana, Kyle, et al.
Pubblicazione: (2023)

Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling
di: Hao, Yongchang, et al.
Pubblicazione: (2026)

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling
di: Meterez, Alexandru, et al.
Pubblicazione: (2025)

Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
di: Chen, Siyu, et al.
Pubblicazione: (2024)

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
di: Choquette-Choo, Christopher A., et al.
Pubblicazione: (2023)

Optimal and Diffusion Transports in Machine Learning
di: Peyré, Gabriel
Pubblicazione: (2025)

Achieving $ε^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions
di: Hamza, Ishaq, et al.
Pubblicazione: (2026)

Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence
di: Deng, Yichuan, et al.
Pubblicazione: (2024)

Q-Learning under Finite Model Uncertainty
di: Sester, Julian, et al.
Pubblicazione: (2024)

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
di: Li, Gen, et al.
Pubblicazione: (2021)

Multi-head Transformers Provably Learn Symbolic Multi-step Reasoning via Gradient Descent
di: Yang, Tong, et al.
Pubblicazione: (2025)

Adaptively Robust LLM Inference Optimization under Prediction Uncertainty
di: Chen, Zixi, et al.
Pubblicazione: (2025)

Mean-Field Path-Integral Diffusion: From Samples to Interacting Agents
di: Chertkov, Michael
Pubblicazione: (2026)

Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning
di: Cai, Junyang, et al.
Pubblicazione: (2024)