33 citations · 72 across the 8 of their papers we have counts for
9 papers
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations
Charupriya Sharma, Zhenyu A. Liao, James Cussens +1
A Bayesian network is a probabilistic graphical model that consists of a directed acyclic graph (DAG), where each node is a random variable and attached to each node is a condition…
Learning All Credible Bayesian Network Structures for Model Averaging
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…
Precise expressions for random projections: Low-rank approximation and randomized Newton
Michał Dereziński, Feynman Liang, Zhenyu Liao +1
It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-dimensional subspace. Matrix sketching has emerged as a powerful technique for per…
Towards Efficient Training for Neural Network Quantization
Qing Jin, Linjie Yang, Zhenyu Liao
Quantization reduces computation costs of neural networks but suffers from performance degeneration. Is this accuracy drop due to the reduced capacity, or inefficient training duri…
AdaBits: Neural Network Quantization with Adaptive Bit-Widths
Qing Jin, Linjie Yang, Zhenyu Liao
Deep neural networks with adaptive configurations have gained increasing attention due to the instant and flexible deployment of these models on platforms with different resource b…
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
Yingwei Li, Song Bai, Cihang Xie +3
This paper focuses on learning transferable adversarial examples specifically against defense models (models to defense adversarial attacks). In particular, we show that a simple u…
Finding All Bayesian Network Structures within a Factor of Optimal
Zhenyu A. Liao, Charupriya Sharma, James Cussens +1
A Bayesian network is a widely used probabilistic graphical model with applications in knowledge discovery and prediction. Learning a Bayesian network (BN) from data can be cast as…
Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates
Zeyuan Allen-Zhu, Zhenyu Liao, Lorenzo Orecchia
In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required r…
Optimization Algorithms for Faster Computational Geometry
Zeyuan Allen-Zhu, Zhenyu Liao, Yang Yuan
We study two fundamental problems in computational geometry: finding the maximum inscribed ball (MaxIB) inside a bounded polyhedron defined by hyperplanes, and the minimum encl…