7 papers
Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks
Maria-Florina Balcan, Saumya Goyal, Dravyansh Sharma
Modern regression problems often involve high-dimensional data and a careful tuning of the regularization hyperparameters is crucial to avoid overly complex models that may overfit…
On Learning Verifiers and Implications to Chain-of-Thought Reasoning
Maria-Florina Balcan, Avrim Blum, Zhiyuan Li +1
Chain-of-Thought reasoning has emerged as a powerful approach for solving complex mathematical and logical problems. However, it can often veer off track through incorrect or unsub…
Algorithm Configuration for Structured Pfaffian Settings
Maria-Florina Balcan, Anh Tuan Nguyen, Dravyansh Sharma
Data-driven algorithm design automatically adapts algorithms to specific application domains, achieving better performance. In the context of parameterized algorithms, this approac…
Learning accurate and interpretable tree-based models
Maria-Florina Balcan, Dravyansh Sharma
Decision trees and their ensembles are popular in machine learning as easy-to-understand models. Several techniques have been proposed in the literature for learning tree-based cla…
Sample complexity of data-driven tuning of model hyperparameters in neural networks with structured parameter-dependent dual function
Maria-Florina Balcan, Anh Tuan Nguyen, Dravyansh Sharma
Modern machine learning algorithms, especially deep learning based techniques, typically involve careful hyperparameter tuning to achieve the best performance. Despite the surge of…
Offline-to-online hyperparameter transfer for stochastic bandits
Dravyansh Sharma, Arun Sai Suggala
Classic algorithms for stochastic bandits typically use hyperparameters that govern their critical properties such as the trade-off between exploration and exploitation. Tuning the…