Search Results for author: Ruijiang Gao

Found 14 papers, 8 papers with code

Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design

no code implementations18 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.

Confounding-Robust Policy Improvement with Human-AI Teams

no code implementations13 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.

Improving Tuning-Free Real Image Editing with Proximal Guidance

1 code implementation8 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.

Learning Complementary Policies for Human-AI Teams

no code implementations6 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.

Probabilistic Conformal Prediction Using Conditional Random Samples

1 code implementation14 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.

Conformal Prediction Prediction Intervals

Enhancing Counterfactual Classification via Self-Training

1 code implementation8 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).

Classification counterfactual +2

Loss Functions for Discrete Contextual Pricing with Observational Data

no code implementations18 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.

Management Off-policy evaluation

Identifying Best Fair Intervention

no code implementations8 Nov 2021 Ruijiang Gao, Han Feng

We study the problem of best arm identification with a fairness constraint in a given causal model.

counterfactual Fairness

AE-StyleGAN: Improved Training of Style-Based Auto-Encoders

1 code implementation17 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.

Dual Projection Generative Adversarial Networks for Conditional Image Generation

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.

Conditional Image Generation

Cost-Accuracy Aware Adaptive Labeling for Active Learning

1 code implementation24 May 2021 Ruijiang Gao, Maytal Saar-Tsechansky

Moreover, a given labeler may exhibit different labeling accuracies for different instances.

Active Learning Informativeness

Counterfactual Self-Training

no code implementations1 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.

counterfactual Domain Adaptation +1

Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons

1 code implementation21 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.

Attribute Generative Adversarial Network

Unsupervised Domain Adaptation via Calibrating Uncertainties

1 code implementation25 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.

Unsupervised Domain Adaptation

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