2 citations · 2 across the 5 of their papers we have counts for
6 papers
Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification
Zirui Deng, Jingbo Sun, Deyu Meng +1
Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-co…
Efficient Vector Symbolic Architectures from Histogram Recovery
Zirui Deng, Netanel Raviv
Vector symbolic architectures (VSAs) are a family of information representation techniques which enable composition, i.e., creating complex information structures from atomic vecto…
Individual Confidential Computing of Polynomials over Non-Uniform Information
Saar Tarnopolsky, Zirui, Deng +3
In this paper, we address the problem of secure distributed computation in scenarios where user data is not uniformly distributed, extending existing frameworks that assume uniform…
Perfect Subset Privacy in Polynomial Computation via Reed-Muller Information Super-sets
Zirui Deng, Vinayak Ramkumar, Netanel Raviv
Delegating large-scale computations to service providers is a common practice which raises privacy concerns. This paper studies information-theoretic privacy-preserving delegation…
Private Inference in Quantized Models
Zirui Deng, Vinayak Ramkumar, Rawad Bitar +1
A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding…
Approximate Private Inference in Quantized Models
Zirui Deng, Netanel Raviv
Private inference refers to a two-party setting in which one has a model (e.g., a linear classifier), the other has data, and the model is to be applied over the data while safegua…