Amy Du

Ally Yalei Du, Eric Huang, Dravyansh Sharma

Graph-based semi-supervised learning is a powerful paradigm in machine learning for modeling and exploiting the underlying graph structure that captures the relationship between labeled and unlabeled data. A large number of classical as well as modern deep learning based algorithms have been proposed for this problem, often having tunable hyperparameters. We initiate a formal study of tuning algorithm hyperparameters from parameterized algorithm families for this problem. We obtain novel $O(\log n)$ pseudo-dimension upper bounds for hyperparameter selection in three classical label propagation-based algorithm families, where $n$ is the number of nodes, implying bounds on the amount of data needed for learning provably good parameters. We further provide matching $Ω(\log n)$ pseudo-dimension lower bounds, thus asymptotically characterizing the learning-theoretic complexity of the parameter tuning problem. We extend our study to selecting architectural hyperparameters in modern graph neural networks. We bound the Rademacher complexity for tuning the self-loop weighting in recently proposed Simplified Graph Convolution (SGC) networks. We further propose a tunable architecture that interpolates graph convolutional neural networks (GCN) and graph attention networks (GAT) in every layer, and provide Rademacher complexity bounds for tuning the interpolation coefficient.

Alexander Du, Yingwu Zhu

Estimating 6D object poses from RGB images is challenging because the lack of depth information requires inferring a three dimensional structure from 2D projections. Traditional methods often rely on deep learning with grid based data structures but struggle to capture complex dependencies among extracted features. To overcome this, we introduce a graph based representation derived directly from images, where spatial temporal features of each pixel serve as nodes, and relationships between them are defined through node connectivity and spatial interactions. We also employ feature selection mechanisms that use spatial attention and self attention distillation, along with a Legendre convolution layer leveraging the orthogonality of Legendre polynomials for numerical stability. Experiments on the LINEMOD, Occluded LINEMOD, and YCB Video datasets demonstrate that our method outperforms nine existing approaches and achieves state of the art benchmark in object pose estimation.

Sepideh Emam, Amy X. Du, Philip Surmanowicz et al.

Background. Real-world data show that approximately 50% of psoriasis patients treated with a biologic agent will discontinue the drug because of loss of efficacy. History of previous therapy with another biologic, female sex and obesity were identified as predictors of drug discontinuations, but their individual predictive value is low. Objectives. To determine whether machine learning algorithms can produce models that can accurately predict outcomes of biologic therapy in psoriasis on individual patient level. Results. All tested machine learning algorithms could accurately predict the risk of drug discontinuation and its cause (e.g. lack of efficacy vs adverse event). The learned generalized linear model achieved diagnostic accuracy of 82%, requiring under 2 seconds per patient using the psoriasis patients dataset. Input optimization analysis established a profile of a patient who has best chances of long-term treatment success: biologic-naive patient under 49 years, early-onset plaque psoriasis without psoriatic arthritis, weight < 100 kg, and moderate-to-severe psoriasis activity (DLQI $\geq$ 16; PASI $\geq$ 10). Moreover, a different generalized linear model is used to predict the length of treatment for each patient with mean absolute error (MAE) of 4.5 months. However Pearson Correlation Coefficient indicates 0.935 linear dependencies between the actual treatment lengths and predicted ones. Conclusions. Machine learning algorithms predict the risk of drug discontinuation and treatment duration with accuracy exceeding 80%, based on a small set of predictive variables. This approach can be used as a decision-making tool, communicating expected outcomes to the patient, and development of evidence-based guidelines.