2 citations · 5 across the 10 of their papers we have counts for
10 papers
On the robustness of double-word addition algorithms
Yuanyuan Yang, XinYu Lyu, Sida He +3
We demonstrate that, even when there are moderate overlaps in the inputs of sloppy or accurate double-word addition algorithms in the QD library, these algorithms still guarantee e…
Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training
Lianke Qin, Saayan Mitra, Zhao Song +2
In this paper, we consider a heavy inner product identification problem, which generalizes the Light Bulb problem~(\cite{prr89}): Given two sets and $B \sub…
Dueling Optimization with a Monotone Adversary
Avrim Blum, Meghal Gupta, Gene Li +3
We introduce and study the problem of dueling optimization with a monotone adversary, which is a generalization of (noiseless) dueling convex optimization. The goal is to design an…
Efficient SGD Neural Network Training via Sublinear Activated Neuron Identification
Lianke Qin, Zhao Song, Yuanyuan Yang
Deep learning has been widely used in many fields, but the model training process usually consumes massive computational resources and time. Therefore, designing an efficient neura…
A Nearly Optimal Size Coreset Algorithm with Nearly Linear Time
Yichuan Deng, Zhao Song, Yitan Wang +1
A coreset is a point set containing information about geometric properties of a larger point set. A series of previous works show that in many machine learning problems, especially…
Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance
Zhao Song, Xin Yang, Yuanyuan Yang +1
Projection maintenance is one of the core data structure tasks. Efficient data structures for projection maintenance have led to recent breakthroughs in many convex programming alg…
Accelerating Frank-Wolfe Algorithm using Low-Dimensional and Adaptive Data Structures
Zhao Song, Zhaozhuo Xu, Yuanyuan Yang +1
In this paper, we study the problem of speeding up a type of optimization algorithms called Frank-Wolfe, a conditional gradient method. We develop and employ two novel inner produc…
Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension
Yuling Jiao, Dingwei Li, Min Liu +2
In this paper, we consider recovering dimensional signals from binary measurements corrupted by noises and sign flips under the assumption that the target signals have low…
Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class
Yuling Jiao, Yanming Lai, Xiliang Lu +3
In this paper, we construct neural networks with ReLU, sine and as activation functions. For general continuous defined on with continuity modulus ,…
On Newton Screening
Jian Huang, Yuling Jiao, Lican Kang +4
Screening and working set techniques are important approaches to reducing the size of an optimization problem. They have been widely used in accelerating first-order methods for so…