SelectFormer in Data Markets: Privacy-Preserving and Efficient Data Selection for Transformers with Multi-Party Computation
No confident code implementation found for this paper. 4 possible matches can be shown below.
No confident code implementation found for this paper. 4 possible matches can be shown below.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 4 weaker matches are hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Global and Hierarchical Geometry Consistency Priors for Few-Shot NeRFs in Indoor Scenes
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context
UnionFormer: Unified-Learning Transformer with Multi-View Representation for Image Manipulation Detection and Localization
Diffusion-ES: Gradient-Free Planning with Diffusion for Autonomous and Instruction-Guided Driving
Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency
@article{xu2026selectformer,
title = {SelectFormer in Data Markets: Privacy-Preserving and Efficient Data Selection for Transformers with Multi-Party Computation},
author = {Ouyang Xu and F. Lin and Yangfeng Ji},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:0a5a2d959aa43025d9abb959003175d3dcf2476c},
journal = {ICLR 2025 2025}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/0a5a2d959aa43025d9abb959003175d3dcf2476c)