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Found 10 papers, 9 papers with code
NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper presents the NTIRE 2025 image super-resolution ($\times$4) challenge, one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025.
The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR).
Doubly robust identification of treatment effects from multiple environments
Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social sciences.
Efficient Randomized Experiments Using Foundation Models
Empirical results across several randomized experiments show that our estimator offers substantial precision gains, equivalent to a reduction of up to 20% in the sample size needed to match the same precision as the standard estimator based on experimental data alone.
Copyright-Protected Language Generation via Adaptive Model Fusion
In particular, CP-Fuse adaptively aggregates the model outputs to minimize the reproduction of copyrighted content, adhering to a crucial balancing property that prevents the regurgitation of memorized data.
Strong Copyright Protection for Language Models via Adaptive Model Fusion
The risk of language models unintentionally reproducing copyrighted material from their training data has led to the development of various protective measures.
Detecting critical treatment effect bias in small subgroups
Randomized trials are considered the gold standard for making informed decisions in medicine, yet they often lack generalizability to the patient populations in clinical practice.
Privacy-preserving data release leveraging optimal transport and particle gradient descent
We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government.
Hidden yet quantifiable: A lower bound for confounding strength using randomized trials
Further, we show how our lower bound can correctly identify the absence and presence of unobserved confounding in a real-world setting.
Approximating Full Conformal Prediction at Scale via Influence Functions
We prove that our method is a consistent approximation of full CP, and empirically show that the approximation error becomes smaller as the training set increases; e. g., for $10^{3}$ training points the two methods output p-values that are $<10^{-3}$ apart: a negligible error for any practical application.
