Stabilizing black-box algorithms through task-oriented randomization

arXiv:2606.25269 · stat.ML, cs.AI, cs.LG · Submitted 2026-06-24 · Read on arXiv

Yali Wang, Zhaojun Wang

stat.ML, cs.AI, cs.LG

Submitted: 2026-06-24

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Process — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Diffusion Model — 5× in this paper · explained in 10 episodes
• Differential Privacy — 4× in this paper · explained in 5 episodes
• Diffusion Models — 4× in this paper · explained in 9 episodes
• Theoretical Guarantees — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 14 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Papers — 2× in this paper · explained in 3 episodes
• Transformer Architecture — 2× in this paper · explained in 4 episodes
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Convolutional Neural Network — 1× in this paper
• Convolutional Neural Network (CNN) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Differential Privacy (DP) — 1× in this paper · explained in 12 episodes
• Diffusion Processes — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Language Models (LLMs) — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Latent Diffusion Model — 1× in this paper · explained in 3 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Local Differential Privacy — 1× in this paper
• Local Differential Privacy (LDP) — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Scoring Function — 1× in this paper · explained in 2 episodes
• Unsupervised Learning — 1× in this paper · explained in 2 episodes

Sources

• Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
• Assumption-free stability for ranking problems
• Stability via resampling: statistical problems beyond the real line
• Denoising Diffusion Implicit Models
• Score-Based Generative Modeling through Stochastic Differential Equations

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