Author name cluster

Margrit Betke

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5 papers

2 author rows

TMLR Journal 2026 Journal Article

Budget-Optimized Crowdworker Allocation

Sha Lai
Prakash Ishwar
Margrit Betke

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze data that are easy to label and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the worker label aggregation can be formulated using a probabilistic approach, and how the allocations of the number of workers to a task can be computed optimally based on task difficulty alone, without using worker profiles. Our representative target task is identifying entailment between sentences. To illustrate the proposed methodology, we conducted simulation experiments that utilize a machine learning system as a proxy for workers and demonstrate its advantages over a state-of-the-art commercial optimizer.

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YNIMG Journal 2025 Journal Article

Aphasia severity prediction using a multi-modal machine learning approach

Xinyi Hu
Maria Varkanitsa
Emerson Kropp
Margrit Betke
Prakash Ishwar
Swathi Kiran

The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in 76 individuals with post-stroke aphasia. We employed Support Vector Regression (SVR) and Random Forest (RF) models with supervised feature selection and a stacked feature prediction approach. The SVR model outperformed RF, achieving an average root mean square error (RMSE) of 16.38±5.57, Pearson's correlation coefficient (r) of 0.70±0.13, and mean absolute error (MAE) of 12.67±3.27, compared to RF's RMSE of 18.41±4.34, r of 0.66±0.15, and MAE of 14.64±3.04. Resting-state neural activity and structural integrity emerged as crucial predictors of aphasia severity, appearing in the top 20% of predictor combinations for both SVR and RF. Finally, the feature selection method revealed that functional connectivity in both hemispheres and between homologous language areas is critical for predicting language outcomes in patients with aphasia. The statistically significant difference in performance between the model using only single modality and the optimal multi-modal SVR/RF model (which included both resting-state connectivity and structural information) underscores that aphasia severity is influenced by factors beyond lesion location and volume. These findings suggest that integrating multiple neuroimaging modalities enhances the prediction of language outcomes in aphasia beyond lesion characteristics alone, offering insights that could inform personalized rehabilitation strategies.

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AAAI Conference 2021 Conference Paper

Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation

Kaihong Wang
Chenhongyi Yang
Margrit Betke

Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixellevel annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source domain so that a model can be trained with annotations from the latter. However, such methods highly rely on an image translator or feature extractor trained in an elaborated mechanism including adversarial training, which brings in extra complexity and instability in the adaptation process. Furthermore, these methods mainly focus on taking advantage of the labeled source dataset, leaving the unlabeled target dataset not fully utilized. In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. BiSIDA aligns domains by not only transferring source images into the style of target images but also transferring target images into the style of source images to perform high-dimensional perturbation on the unlabeled target images, which is crucial to the success in applying consistency regularization in segmentation tasks. Extensive experiments show that our BiSIDA achieves new state-of-theart on two commonly-used synthetic-to-real domain adaptation benchmarks: GTA5-to-CityScapes and SYNTHIA-to- CityScapes. Code and pretrained style transfer model are available at: https: //github. com/wangkaihong/BiSIDA.

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I&C Journal 1999 Journal Article

Piecemeal Graph Exploration by a Mobile Robot

Baruch Awerbuch
Margrit Betke
Ronald L. Rivest
Mona Singh

We study how a mobile robot can learn an unknown environment in a piecemeal manner. The robot's goal is to learn a complete map of its environment, while satisfying the constraint that it must return every so often to its starting position (for refueling, say). The environment is modeled as an arbitrary, undirected graph, which is initially unknown to the robot. We assume that the robot can distinguish vertices and edges that it has already explored. We present a surprisingly efficient algorithm for piecemeal learning an unknown undirected graph G=(V, E) in which the robot explores every vertex and edge in the graph by traversing at most O(E+V 1+o(1)) edges. This nearly linear algorithm improves on the best previous algorithm, in which the robot traverses at most O(E+V 2) edges. We also give an application of piecemeal learning to the problem of searching a graph for a “treasure. ”

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IROS Conference 1994 Conference Paper

Mobile robot localization using landmarks

Margrit Betke
Leonid Gurvits

We describe an efficient algorithm for localizing a mobile robot in an environment with landmarks. We assume that the robot has a camera and maybe other sensors that enable it to both identify landmarks and measure the angles subtended by these landmarks. We show how to estimate the robot's position using a new technique that involves a complex number representation of the landmarks. Our algorithm runs in time linear in the number of landmarks. We present results of our simulations and propose how to use our method for robot navigation. >

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