2 citations · 3 across the 7 of their papers we have counts for
7 papers
Causal Influence Maximization with Steady-State Guarantees
Renjie Cao, Zhuoxin Yan, Zichen Wang +2
Classical influence maximization optimizes expected reach, which can be misaligned with welfare when exposures have heterogeneous, saturating, or adverse effects. We study seed sel…
Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data
Jiacan Gao, Xinyan Su, Mingyuan Ma +7
Estimating heterogeneous treatment effects is central to data-driven decision-making, yet industrial applications often face a fundamental tension between limited randomized contro…
Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
Xinyan Su, Jiacan Gao, Mingyuan Ma +6
We study uplift estimation for combinatorial treatments. Uplift measures the pure incremental causal effect of an intervention (e.g., sending a coupon or a marketing message) on us…
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation
Jialin Liu, Xinyan Su, Zeyu He +2
Rewards serve as a measure of user satisfaction and act as a limiting factor in interactive recommender systems. In this research, we focus on the problem of learning to reward (LT…
A General Neural Causal Model for Interactive Recommendation
Jialin Liu, Xinyan Su, Peng Zhou +2
Survivor bias in observational data leads the optimization of recommender systems towards local optima. Currently most solutions re-mines existing human-system collaboration patter…
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization
Xinyan Su, Zhiheng Zhang, Jiyan Qiu
Influence Maximization (IM) is to identify the seed set to maximize information dissemination in a network. Elegant IM algorithms could naturally extend to cases where each node is…
Partial Identification with Proxy of Latent Confoundings via Sum-of-ratios Fractional Programming
Zhiheng Zhang
Due to the unobservability of confoundings, there has been widespread concern about how to compute causality quantitatively. To address this challenge, proxy-based negative control…