1 citations · 1 across the 18 of their papers we have counts for
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Transformers as Adaptive Kernel Learners across Tasks for In-Context Learning
Zhaiming Shen, Alexander Cloninger, Rongjie Lai +1
Transformers have emerged as a dominant architecture in modern machine learning, with in-context learning (ICL) enabling models to adapt to new tasks by conditioning on prompts, wi…
Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Data
Tianxiang Wang, Yingtong Ke, Dhananjay Bhaskar +2
Single-cell technologies generate high-dimensional point clouds of cells, enabling detailed characterization of complex patient states and treatment responses. Yet each patient is…
Revisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees
Yiming Zhang, Chester Holtz, Gal Mishne +1
Learning with noisy labels remains challenging because over-parameterized networks memorize corrupted supervision. Meta-learning-based sample reweighting mitigates this by using a…
Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
Dhruv Kohli, Sawyer J. Robertson, Gal Mishne +1
Estimating the tangent spaces of a data manifold is a fundamental problem in geometric data analysis. The standard approach, Local Principal Component Analysis (LPCA), struggles in…
KAIROS: Scalable Model-Agnostic Data Valuation
Jiongli Zhu, Parjanya Prajakta Prashant, Alex Cloninger +1
Training data increasingly shapes not only model accuracy but also regulatory compliance and market valuation of AI assets. Yet existing valuation methods remain inadequate: model-…
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
Zhaiming Shen, Alex Havrilla, Rongjie Lai +2
Transformers serve as the foundational architecture for large language and video generation models, such as GPT, BERT, SORA and their successors. Empirical studies have demonstrate…
Robust Graph-Based Semi-Supervised Learning via -Conductances
Sawyer Jack Robertson, Chester Holtz, Zhengchao Wan +2
We study the problem of semi-supervised learning on graphs in the regime where data labels are scarce or possibly corrupted. We propose an approach called -conductance learning…
OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport
Alireza Pirhadi, Mohammad Hossein Moslemi, Alexander Cloninger +2
Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models. In this paper, we introduce \sys, a framework tha…