54 citations · 83 across the 9 of their papers we have counts for
9 papers
Exploring Bottom-Up Clustering for Creating Semantic IDs
Leah Woldemariam, Sudhanshu Garg, Taha Belkhouja +2
The success of generative retrieval has largely been attributed to the use of Semantic IDs, which improve over arbitrary item-level identifiers such as hashes by capturing the sema…
Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition
Minxue Tang, Yangyang Yu, Aolin Ding +3
Recognizing implicit visual and textual patterns is essential in many real-world applications of modern AI. However, tackling long-tail pattern recognition tasks remains challengin…
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja +2
Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or…
Probabilistically robust conformal prediction
Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja +3
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, C…
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Subhankar Ghosh, Taha Belkhouja, Yan Yan +1
Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled fra…
Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
Taha Belkhouja, Yan Yan, Janardhan Rao Doppa
Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-…
Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features
Taha Belkhouja, Janardhan Rao Doppa
Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, littl…
Dynamic Time Warping based Adversarial Framework for Time-Series Domain
Taha Belkhouja, Yan Yan, Janardhan Rao Doppa
Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data…
Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach
Taha Belkhouja, Yan Yan, Janardhan Rao Doppa
Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data which is not generated from the same distribution as training data.…