13 research outputs found
Confounding-Robust Policy Improvement with Human-AI Teams
Human-AI collaboration has the potential to transform various domains by
leveraging the complementary strengths of human experts and Artificial
Intelligence (AI) systems. However, unobserved confounding can undermine the
effectiveness of this collaboration, leading to biased and unreliable outcomes.
In this paper, we propose a novel solution to address unobserved confounding in
human-AI collaboration by employing the marginal sensitivity model (MSM). Our
approach combines domain expertise with AI-driven statistical modeling to
account for potential confounders that may otherwise remain hidden. We present
a deferral collaboration framework for incorporating the MSM into policy
learning from observational data, enabling the system to control for the
influence of unobserved confounding factors. In addition, we propose a
personalized deferral collaboration system to leverage the diverse expertise of
different human decision-makers. By adjusting for potential biases, our
proposed solution enhances the robustness and reliability of collaborative
outcomes. The empirical and theoretical analyses demonstrate the efficacy of
our approach in mitigating unobserved confounding and improving the overall
performance of human-AI collaborations.Comment: 24 page
Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design
Designing products to meet consumers' preferences is essential for a
business's success. We propose the Gradient-based Survey (GBS), a discrete
choice experiment for multiattribute product design. The experiment elicits
consumer preferences through a sequence of paired comparisons for partial
profiles. GBS adaptively constructs paired comparison questions based on the
respondents' previous choices. Unlike the traditional random utility
maximization paradigm, GBS is robust to model misspecification by not requiring
a parametric utility model. 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. We demonstrate
the advantage of GBS in accuracy and sample efficiency compared to the existing
parametric and nonparametric methods in simulations
Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons
Conditional generative adversarial networks have shown exceptional generation
performance over the past few years. However, they require large numbers of
annotations. To address this problem, we propose a novel generative adversarial
network utilizing weak supervision in the form of pairwise comparisons (PC-GAN)
for image attribute editing. In the light of Bayesian uncertainty estimation
and noise-tolerant adversarial training, PC-GAN can estimate attribute rating
efficiently and demonstrate robust performance in noise resistance. Through
extensive experiments, we show both qualitatively and quantitatively that
PC-GAN performs comparably with fully-supervised methods and outperforms
unsupervised baselines.Comment: Accepted for spotlight at AAAI-2
Improving Negative-Prompt Inversion via Proximal Guidance
DDIM inversion has revealed the remarkable potential of real image editing
within diffusion-based methods. However, the accuracy of DDIM reconstruction
degrades as larger classifier-free guidance (CFG) scales being used for
enhanced editing. 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. Negative-prompt inversion
(NPI) further offers a training-free closed-form solution of NTI. However, it
may introduce artifacts and is still constrained by DDIM reconstruction
quality. To overcome these limitations, we propose Proximal Negative-Prompt
Inversion (ProxNPI), extending the concepts of NTI and NPI. We enhance NPI with
a regularization term and reconstruction guidance, which reduces artifacts
while capitalizing on its training-free nature. Our method provides an
efficient and straightforward approach, effectively addressing real image
editing tasks with minimal computational overhead.Comment: Code at https://github.com/phymhan/prompt-to-promp
Cost-Accuracy Aware Adaptive Labeling for Active Learning
Conventional active learning algorithms assume a single labeler that produces noiseless label at a given, fixed cost, and aim to achieve the best generalization performance for given classifier under a budget constraint. However, in many real settings, different labelers have different labeling costs and can yield different labeling accuracies. Moreover, a given labeler may exhibit different labeling accuracies for different instances. This setting can be referred to as active learning with diverse labelers with varying costs and accuracies, and it arises in many important real settings. It is therefore beneficial to understand how to effectively trade-off between labeling accuracy for different instances, labeling costs, as well as the informativeness of training instances, so as to achieve the best generalization performance at the lowest labeling cost. In this paper, we propose a new algorithm for selecting instances, labelers (and their corresponding costs and labeling accuracies), that employs generalization bound of learning with label noise to select informative instances and labelers so as to achieve higher generalization accuracy at a lower cost. Our proposed algorithm demonstrates state-of-the-art performance on five UCI and a real crowdsourcing dataset
Preparation and Physicochemical Properties of Biochar from the Pyrolysis of Pruning Waste of Typical Fruit Tree in North China
Routine maintenance of fruit trees generates a substantial quantity of pruning waste each year. This waste is potential feedstock for producing energy, materials, and other products. The feasibility of making biochar from the waste via pyrolysis was evaluated. The effects of seven tree species, different pruning sites, and temperature on the pyrolysis process, and the physicochemical properties of the biochar were studied. Pyrolysis of different tree species at 500 °C yielded 27.5 to 33.3% biochar, with a high calorific value (approximately 30 MJ/kg), low ash content (approximately 4%), and capturing up to 60% of the carbon element present. Simultaneously, when the temperature was increased from 400 to 700 °C, the yield of biochar decreased from 35.8% to 24.3%, but the properties improved with the higher heating value rising from 29.2 to 31.3 MJ/kg and the iodine value from 234 to 252 mg/g. The biochar has a good pore structure with a specific surface area of 237 m2/g, total pore volume of 0.175 cm3/g, and average pore size of 2.96 nm. In general, biochar from the pyrolysis of fruitwood pruning waste generated here could be an ideal feedstock to produce high-value-added products, such as solid fuels, activated carbon, and electrode materials
Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT.
In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods