no code implementations • 18 Oct 2023 • Mingzhang Yin, Ruijiang Gao, Weiran Lin, Steven M. Shugan
Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers.
no code implementations • 13 Oct 2023 • Ruijiang Gao, Mingzhang Yin
In addition, we propose a personalized deferral collaboration system to leverage the diverse expertise of different human decision-makers.
1 code implementation • 8 Jun 2023 • Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Anastasis Stathopoulos, Xiaoxiao He, Yuxiao Chen, Di Liu, Qilong Zhangli, Jindong Jiang, Zhaoyang Xia, Akash Srivastava, Dimitris Metaxas
Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control.
no code implementations • 6 Feb 2023 • Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Wei Sun, Min Kyung Lee, Matthew Lease
We then extend our approach to leverage opportunities and mitigate risks that arise in important contexts in practice: 1) when a team is composed of multiple humans with differential and potentially complementary abilities, 2) when the observational data includes consistent deterministic actions, and 3) when the covariate distribution of future decisions differ from that in the historical data.
1 code implementation • 14 Jun 2022 • Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set.
1 code implementation • 8 Dec 2021 • Ruijiang Gao, Max Biggs, Wei Sun, Ligong Han
We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes with categorical values for finite unseen actions in the observational data to simulate a randomized trial through pseudolabeling, which we refer to as Counterfactual Self-Training (CST).
no code implementations • 18 Nov 2021 • Max Biggs, Ruijiang Gao, Wei Sun
The goal of this paper is to formulate loss functions that can be used for evaluating pricing policies directly from observational data, rather than going through an intermediate demand estimation stage, which may suffer from bias.
no code implementations • 8 Nov 2021 • Ruijiang Gao, Han Feng
We study the problem of best arm identification with a fairness constraint in a given causal model.
1 code implementation • 17 Oct 2021 • Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min, Ruijiang Gao, Yu Tian, Dimitris Metaxas
StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space.
1 code implementation • ICCV 2021 • Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos, Yu Tian, Ruijiang Gao, Asim Kadav, Dimitris Metaxas
We then propose an improved cGAN model with Auxiliary Classification that directly aligns the fake and real conditionals $P(\text{class}|\text{image})$ by minimizing their $f$-divergence.
1 code implementation • 24 May 2021 • Ruijiang Gao, Maytal Saar-Tsechansky
Moreover, a given labeler may exhibit different labeling accuracies for different instances.
no code implementations • 1 Jan 2021 • Ruijiang Gao, Max Biggs, Wei Sun, Ligong Han
We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes for the unseen actions in the observational data to simulate a randomized trial.
1 code implementation • 21 Nov 2019 • Ligong Han, Ruijiang Gao, Mun Kim, Xin Tao, Bo Liu, Dimitris Metaxas
Conditional generative adversarial networks have shown exceptional generation performance over the past few years.
1 code implementation • 25 Jul 2019 • Ligong Han, Yang Zou, Ruijiang Gao, Lezi Wang, Dimitris Metaxas
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset.