Copertina

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Modi, Chirag, Han, Jiequn, Vanden-Eijnden, Eric, Bruna, Joan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Simulation-Free Differential Dynamics through Neural Conservation Laws
di: Hua, Mengjian, et al.
Pubblicazione: (2025)

Multitask Learning with Stochastic Interpolants
di: Negrel, Hugo, et al.
Pubblicazione: (2025)

Lipschitz-Guided Design of Interpolation Schedules in Generative Models
di: Chen, Yifan, et al.
Pubblicazione: (2025)

Training-Free Generative Modeling via Kernelized Stochastic Interpolants
di: Coeurdoux, Florentin, et al.
Pubblicazione: (2026)

Neural Galerkin Schemes with Active Learning for High-Dimensional Evolution Equations
di: Bruna, Joan, et al.
Pubblicazione: (2022)

Posterior Sampling with Denoising Oracles via Tilted Transport
di: Bruna, Joan, et al.
Pubblicazione: (2024)

Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
di: Albergo, Michael S., et al.
Pubblicazione: (2023)

DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction
di: Morel, Rudy, et al.
Pubblicazione: (2025)

Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
di: Chun, Inkook, et al.
Pubblicazione: (2025)

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
di: Chen, Yifan, et al.
Pubblicazione: (2024)

Batch and match: black-box variational inference with a score-based divergence
di: Cai, Diana, et al.
Pubblicazione: (2024)

Test-time scaling of diffusions with flow maps
di: Sabour, Amirmojtaba, et al.
Pubblicazione: (2025)

From Black-box to Causal-box: Towards Building More Interpretable Models
di: Hwang, Inwoo, et al.
Pubblicazione: (2025)

Generation Properties of Stochastic Interpolation under Finite Training Set
di: Li, Yunchen, et al.
Pubblicazione: (2025)

Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting
di: Wynn, Andrea, et al.
Pubblicazione: (2025)

Scale-Adaptive Generative Flows for Multiscale Scientific Data
di: Chen, Yifan, et al.
Pubblicazione: (2025)

An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
di: Hua, Mengjian, et al.
Pubblicazione: (2024)

Tackling Data Corruption in Offline Reinforcement Learning via Sequence Modeling
di: Xu, Jiawei, et al.
Pubblicazione: (2024)

Variational Optimality of Föllmer Processes in Generative Diffusions
di: Chen, Yifan, et al.
Pubblicazione: (2026)

Preference Models assume Proportional Hazards of Utilities
di: Nagpal, Chirag
Pubblicazione: (2025)

ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training
di: Liang, Yu, et al.
Pubblicazione: (2026)

DREAM: Domain-agnostic Reverse Engineering Attributes of Black-box Model
di: Li, Rongqing, et al.
Pubblicazione: (2024)

Active Policy Improvement from Multiple Black-box Oracles
di: Liu, Xuefeng, et al.
Pubblicazione: (2023)

Deeper Understanding of Black-box Predictions via Generalized Influence Functions
di: Lyu, Hyeonsu, et al.
Pubblicazione: (2023)

Distribution Matching via Generalized Consistency Models
di: Shrestha, Sagar, et al.
Pubblicazione: (2025)

Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property
di: Yoshikawa, Yuya, et al.
Pubblicazione: (2024)

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation
di: Tan, Kim Yong, et al.
Pubblicazione: (2025)

A Generative Approach to Surrogate-based Black-box Attacks
di: Moraffah, Raha, et al.
Pubblicazione: (2024)

Optimizing Noise Schedules of Generative Models in High Dimensionss
di: Aranguri, Santiago, et al.
Pubblicazione: (2025)

Knowledge Editing on Black-box Large Language Models
di: Song, Xiaoshuai, et al.
Pubblicazione: (2024)

SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
di: Ma, Nanye, et al.
Pubblicazione: (2024)

Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation
di: Sun, Peiran
Pubblicazione: (2025)

Fast Sampling for Flows and Diffusions with Lazy and Point Mass Stochastic Interpolants
di: Damsholt, Gabriel, et al.
Pubblicazione: (2026)

Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking
di: Boffi, Nicholas M., et al.
Pubblicazione: (2024)

On Transfer-based Universal Attacks in Pure Black-box Setting
di: Jalwana, Mohammad A. A. K., et al.
Pubblicazione: (2025)

Black-box Uncertainty Quantification Method for LLM-as-a-Judge
di: Wagner, Nico, et al.
Pubblicazione: (2024)

Hyperband-based Bayesian Optimization for Black-box Prompt Selection
di: Schneider, Lennart, et al.
Pubblicazione: (2024)

BELLA: Black box model Explanations by Local Linear Approximations
di: Radulovic, Nedeljko, et al.
Pubblicazione: (2023)

Distributional Multi-objective Black-box Optimization for Diffusion-model Inference-time Multi-Target Generation
di: Tan, Kim Yong, et al.
Pubblicazione: (2025)

Rethinking Explainability in the Era of Multimodal AI
di: Agarwal, Chirag
Pubblicazione: (2025)