Image de couverture de livre

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kazdan, Joshua, Sun, Hao, Han, Jiaqi, Petersen, Felix, Ermon, Stefano
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers
par: Han, Jiaqi, et autres
Publié: (2025)

TrAct: Making First-layer Pre-Activations Trainable
par: Petersen, Felix, et autres
Publié: (2024)

Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration
par: Han, Jiaqi, et autres
Publié: (2026)

Generalizing Stochastic Smoothing for Differentiation and Gradient Estimation
par: Petersen, Felix, et autres
Publié: (2024)

Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models
par: Wang, Austin, et autres
Publié: (2026)

Data Unlearning in Diffusion Models
par: Alberti, Silas, et autres
Publié: (2025)

Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models
par: Schaeffer, Rylan, et autres
Publié: (2025)

Geometric Trajectory Diffusion Models
par: Han, Jiaqi, et autres
Publié: (2024)

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
par: Lou, Aaron, et autres
Publié: (2023)

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement
par: Dang, Meihua, et autres
Publié: (2025)

Divergence Minimization Preference Optimization for Diffusion Model Alignment
par: Li, Binxu, et autres
Publié: (2025)

TFG: Unified Training-Free Guidance for Diffusion Models
par: Ye, Haotian, et autres
Publié: (2024)

Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding
par: Guo, Gabe, et autres
Publié: (2025)

Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
par: Belakaria, Syrine, et autres
Publié: (2025)

GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapters
par: Zhao, Wanjia, et autres
Publié: (2025)

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms
par: Petersen, Felix, et autres
Publié: (2024)

Convolutional Differentiable Logic Gate Networks
par: Petersen, Felix, et autres
Publié: (2024)

GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning
par: Murata, Naoki, et autres
Publié: (2026)

Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics
par: Iyengar, Aniketh, et autres
Publié: (2026)

$f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization
par: Han, Jiaqi, et autres
Publié: (2024)

Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
par: Iyengar, Aniketh, et autres
Publié: (2025)

Discrete Diffusion Trajectory Alignment via Stepwise Decomposition
par: Han, Jiaqi, et autres
Publié: (2025)

TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
par: Shi, Juntong, et autres
Publié: (2024)

Training Data Protection with Compositional Diffusion Models
par: Golatkar, Aditya, et autres
Publié: (2023)

Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations
par: Selvam, Nikil Roashan, et autres
Publié: (2024)

Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space
par: Shen, Yangyi, et autres
Publié: (2026)

Understanding Adversarial Transfer: Why Representation-Space Attacks Fail Where Data-Space Attacks Succeed
par: Gupta, Isha, et autres
Publié: (2025)

SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
par: Cundy, Chris, et autres
Publié: (2023)

RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models
par: Hudovernik, Valter, et autres
Publié: (2025)

Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
par: Ou, Jingyang, et autres
Publié: (2025)

Data-regularized Reinforcement Learning for Diffusion Models at Scale
par: Ye, Haotian, et autres
Publié: (2025)

RFG: Test-Time Scaling for Diffusion Large Language Model Reasoning with Reward-Free Guidance
par: Chen, Tianlang, et autres
Publié: (2025)

The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
par: Allouah, Youssef, et autres
Publié: (2024)

DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling
par: Zhou, Linqi, et autres
Publié: (2023)

Calibrated Probabilistic Forecasts for Arbitrary Sequences
par: Marx, Charles, et autres
Publié: (2024)

ABC: Any-Subset Autoregression via Non-Markovian Diffusion Bridges in Continuous Time and Space
par: Guo, Gabe, et autres
Publié: (2026)

Expanding the Role of Diffusion Models for Robust Classifier Training
par: Huang, Pin-Han, et autres
Publié: (2026)

Position: Model Collapse Does Not Mean What You Think
par: Schaeffer, Rylan, et autres
Publié: (2025)

The Principles of Diffusion Models
par: Lai, Chieh-Hsin, et autres
Publié: (2025)

Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space
par: Li, Yangming, et autres
Publié: (2024)