3 citations · 8 across the 8 of their papers we have counts for
8 papers
On the Impossibility of Specification Testing of Interference Models Based on Exposure Mappings
Chao Gao, Christopher Harshaw, Fredrik Sävje +1
Researchers use interference models based on exposure mappings to facilitate estimation of causal effects in randomized experiments with interference. To test the veracity of such…
Toward Highly Efficient and Private Submodular Maximization via Matrix-Based Acceleration
Boyu Liu, Lianke Qin, Zhao Song +2
Submodular function maximization is a critical building block for diverse tasks, such as document summarization, sensor placement, and image segmentation. Yet its practical utility…
A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations
Siyu Chen, Yitan Wang, Zhaoran Wang +1
We study the offline contextual bandit problem, where we aim to acquire an optimal policy using observational data. However, this data usually contains two deficiencies: (i) some v…
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…
A General Design-Based Framework and Estimator for Randomized Experiments
Christopher Harshaw, Fredrik Sävje, Yitan Wang
We describe a widely applicable design-based framework for drawing causal inference in randomized experiments. Causal effects are defined as linear functionals evaluated at unit-le…
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability
Zhao Song, Yitan Wang, Zheng Yu +1
Sketching is one of the most fundamental tools in large-scale machine learning. It enables runtime and memory saving via randomly compressing the original large problem into lower…
A Sublinear Adversarial Training Algorithm
Yeqi Gao, Lianke Qin, Zhao Song +1
Adversarial training is a widely used strategy for making neural networks resistant to adversarial perturbations. For a neural network of width , input training data in …
Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks
Guangzeng Xie, Yitan Wang, Shuchang Zhou +1
In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks. The extrapolation scheme is a classic…