:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Shah, Kulin, Kalavasis, Alkis, Klivans, Adam R., Daras, Giannis
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2502.21278
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Ambient Diffusion Omni: Training Good Models with Bad Data
by: Daras, Giannis, et al.
Published: (2025)

Ambient Dataloops: Generative Models for Dataset Refinement
by: Rodríguez-Muñoz, Adrián, et al.
Published: (2026)

Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
by: Daras, Giannis, et al.
Published: (2024)

How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion
by: Daras, Giannis, et al.
Published: (2024)

Align & Invert: Solving Inverse Problems with Diffusion and Flow-based Models via Representation Alignment
by: Sfountouris, Loukas, et al.
Published: (2025)

Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
by: Aali, Asad, et al.
Published: (2024)

Masked Diffusion for Generative Recommendation
by: Shah, Kulin, et al.
Published: (2025)

On Characterizations for Language Generation: Interplay of Hallucinations, Breadth, and Stability
by: Kalavasis, Alkis, et al.
Published: (2024)

On the Limits of Language Generation: Trade-Offs Between Hallucination and Mode Collapse
by: Kalavasis, Alkis, et al.
Published: (2024)

Smaller Confidence Intervals From IPW Estimators via Data-Dependent Coarsening
by: Kalavasis, Alkis, et al.
Published: (2024)

Computational Lower Bounds for Regret Minimization in Normal-Form Games
by: Anagnostides, Ioannis, et al.
Published: (2024)

Barriers to Welfare Maximization with No-Regret Learning
by: Anagnostides, Ioannis, et al.
Published: (2024)

Ambient Physics: Training Neural PDE Solvers with Partial Observations
by: Majid, Harris Abdul, et al.
Published: (2026)

Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond
by: Kalavasis, Alkis, et al.
Published: (2025)

Transfer Learning Beyond Bounded Density Ratios
by: Kalavasis, Alkis, et al.
Published: (2024)

Efficient Parameter Estimation of Truncated Boolean Product Distributions
by: Fotakis, Dimitris, et al.
Published: (2020)

DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
by: Hosseintabar, Danial, et al.
Published: (2025)

On the Computational Landscape of Replicable Learning
by: Kalavasis, Alkis, et al.
Published: (2024)

Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
by: Daras, Giannis, et al.
Published: (2024)

Learning Mixture Models via Efficient High-dimensional Sparse Fourier Transforms
by: Kalavasis, Alkis, et al.
Published: (2026)

ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems
by: Karan, Aayush, et al.
Published: (2025)

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions
by: Kim, Jaeyeon, et al.
Published: (2025)

DDPM Score Matching and Distribution Learning
by: Chewi, Sinho, et al.
Published: (2025)

Linear Regression with Unknown Truncation Beyond Gaussian Features
by: Kouridakis, Alexandros, et al.
Published: (2026)

On the Learning Curves of Revenue Maximization
by: Hanneke, Steve, et al.
Published: (2026)

Optimal Learners for Realizable Regression: PAC Learning and Online Learning
by: Attias, Idan, et al.
Published: (2023)

Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models
by: Kalavasis, Alkis, et al.
Published: (2024)

Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?
by: Rodriguez, Marta Aparicio, et al.
Published: (2026)

A Survey on Diffusion Models for Inverse Problems
by: Daras, Giannis, et al.
Published: (2024)

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
by: Cai, Yang, et al.
Published: (2025)

Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms
by: Kalavasis, Alkis, et al.
Published: (2026)

On the Edge of Memorization in Diffusion Models
by: Buchanan, Sam, et al.
Published: (2025)

Memorization and Regularization in Generative Diffusion Models
by: Baptista, Ricardo, et al.
Published: (2025)

Replicable Learning of Large-Margin Halfspaces
by: Kalavasis, Alkis, et al.
Published: (2024)

Provable Separations between Memorization and Generalization in Diffusion Models
by: Ye, Zeqi, et al.
Published: (2025)

Manifold Generalization Provably Proceeds Memorization in Diffusion Models
by: Shen, Zebang, et al.
Published: (2026)

Learning the Sherrington-Kirkpatrick Model Even at Low Temperature
by: Chandrasekaran, Gautam, et al.
Published: (2024)

On Memorization in Diffusion Models
by: Gu, Xiangming, et al.
Published: (2023)

Learning general Gaussian mixtures with efficient score matching
by: Chen, Sitan, et al.
Published: (2024)

Learning Juntas under Markov Random Fields
by: Chandrasekaran, Gautam, et al.
Published: (2025)