65 citations · 82 across the 8 of their papers we have counts for
12 papers
Disentangling Multi-view Representations Beyond Inductive Bias
Guanzhou Ke, Yang Yu, Guoqing Chao +3
Multi-view (or -modality) representation learning aims to understand the relationships between different view representations. Existing methods disentangle multi-view representatio…
Auction Design for Value Maximizers with Budget and Return-on-spend Constraints
Pinyan Lu, Chenyang Xu, Ruilong Zhang
The paper designs revenue-maximizing auction mechanisms for agents who aim to maximize their total obtained values rather than the classical quasi-linear utilities. Several models…
Scheduling with a Limited Testing Budget
Christoph Damerius, Peter Kling, Minming Li +2
Scheduling with testing falls under the umbrella of the research on optimization with explorable uncertainty. In this model, each job has an upper limit on its processing time that…
Online Dynamic Acknowledgement with Learned Predictions
Sungjin Im, Benjamin Moseley, Chenyang Xu +1
We revisit the online dynamic acknowledgment problem. In the problem, a sequence of requests arrive over time to be acknowledged, and all outstanding requests can be satisfied simu…
Polylogarithmic Approximation for Robust s-t Path
Shi Li, Chenyang Xu, Ruilong Zhang
The paper revisits the robust - path problem, one of the most fundamental problems in robust optimization. In the problem, we are given a directed graph with vertices and…
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
Guanzhou Ke, Guoqing Chao, Xiaoli Wang +3
The past two decades have seen increasingly rapid advances in the field of multi-view representation learning due to it extracting useful information from diverse domains to facili…
Min-max Submodular Ranking for Multiple Agents
Qingyun Chen, Sungjin Im, Benjamin Moseley +2
In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions…
Multiagent MST Cover: Pleasing All Optimally via A Simple Voting Rule
Bo Li, Xiaowei Wu, Chenyang Xu +1
Given a connected graph on whose edges we can build roads to connect the nodes, a number of agents hold possibly different perspectives on which edges should be selected by assigni…
Mechanism Design with Predictions
Chenyang Xu, Pinyan Lu
Improving algorithms via predictions is a very active research topic in recent years. This paper initiates the systematic study of mechanism design in this model. In a number of we…
Learning-Augmented Algorithms for Online Steiner Tree
Chenyang Xu, Benjamin Moseley
This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the onli…
Using Predicted Weights for Ad Delivery
Thomas Lavastida, Benjamin Moseley, R. Ravi +1
We study the performance of a proportional weights algorithm for online capacitated bipartite matching modeling the delivery of impression ads. The algorithm uses predictions on th…
Learnable and Instance-Robust Predictions for Online Matching, Flows and Load Balancing
Thomas Lavastida, Benjamin Moseley, R. Ravi +1
We propose a new model for augmenting algorithms with predictions by requiring that they are formally learnable and instance robust. Learnability ensures that predictions can be ef…