7 papers
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…
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…
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…
OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions
Yu-Shin Huang, Peter Just, Hanyun Yin +3
We consider coverless steganography where a Large Language Model (LLM) is used to generate stego-texts in combination with arithmetic coding. An efficient method should embed secre…
Partial Information Decomposition via Normalizing Flows in Latent Gaussian Distributions
Wenyuan Zhao, Adithya Balachandran, Chao Tian +1
The study of multimodality has garnered significant interest in fields where the analysis of interactions among multiple information sources can enhance predictive modeling, data f…
Relatively-Secure LLM-Based Steganography via Constrained Markov Decision Processes
Yu-Shin Huang, Chao Tian, Krishna Narayanan +1
Linguistic steganography aims to conceal information within natural language text without being detected. An effective steganography approach should encode the secret message into…