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Found 14 papers, 8 papers with code
Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design
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
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
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
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
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set.
Enhancing Counterfactual Classification via Self-Training
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).
Loss Functions for Discrete Contextual Pricing with Observational Data
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.
Identifying Best Fair Intervention
We study the problem of best arm identification with a fairness constraint in a given causal model.
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders
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
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.
Cost-Accuracy Aware Adaptive Labeling for Active Learning
Moreover, a given labeler may exhibit different labeling accuracies for different instances.
Counterfactual Self-Training
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.
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Conditional generative adversarial networks have shown exceptional generation performance over the past few years.
Unsupervised Domain Adaptation via Calibrating Uncertainties
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset.
