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cs.LG2026
SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning
Wenyuan Zhao, Rui Tuo, Chao Tian
Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalability to large datasets. We pr…
cs.CV2026
Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R
Zihao Zhu, Wenyuan Zhao, Nuo Chen +2
Geometric foundation models hold promise for unconstrained dense geometry prediction from uncalibrated images. However, in current feed-forward designs, their predicted confidence…
cs.IT2026
Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints
Yu-Shin Huang, Chao Tian, Krishna Narayanan
This paper considers the problem of multi-bit generative watermarking for large language models under a worst-case false-alarm constraint. Prior work established a lower bound on t…