Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation
No confident code implementation found for this paper. 2 possible matches can be shown below.
No confident code implementation found for this paper. 2 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. 2 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!
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
REMEDY: Recipe Merging Dynamics in Large Vision-Language Models
Be Confident: Uncovering Overfitting in MLLM Multi-Task Tuning
Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion
G2fR: Frequency Regularization in Grid-Based Feature Encoding Neural Radiance Fields
Adaptive-Gradient Policy Optimization: Enhancing Policy Learning in Non-Smooth Differentiable Simulations
@article{hu2026pseudocalibration,
title = {Pseudo-Calibration: Improving Predictive Uncertainty Estimation in Unsupervised Domain Adaptation},
author = {Dapeng Hu and Jian Liang and Xinchao Wang and Chuan-Sheng Foo},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:63ec7c542e571bc3d22e3457e7f67409c0495cda},
journal = {ICML 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/63ec7c542e571bc3d22e3457e7f67409c0495cda)