Talwalkar, Ameet

Federated Optimization in Heterogeneous Networks

Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 4,023

Foundations of Machine Learning

Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · The MIT Press eBooks

Machine Learning and AlgorithmsStochastic Gradient Optimization Techniques

Cited by 1,122

A large-scale evaluation of computational protein function prediction

Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al. (2013) · Nature Methods

Bioinformatics and Genomic NetworksMachine Learning in Bioinformatics

Cited by 1,028

LEAF: A Benchmark for Federated Settings

Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., & Talwalkar, A. (2018) · arXiv

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 894

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements

Caldas, S., Konečny, J., McMahan, H. B., & Talwalkar, A. (2018) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 312

Model Agnostic Supervised Local Explanations

Plumb, G., Molitor, D., & Talwalkar, A. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 149

Differentially Private Meta-Learning

Li, J., Khodak, M., Caldas, S., & Talwalkar, A. (2019) · arXiv

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 82

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning

Cabrera, Á. A., Fu, E., Bertucci, D., Holstein, K., Talwalkar, A., Hong, J. I., & Perer, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 51

Best practices for interpretable machine learning in computational biology

Chen, V., Yang, M., Cui, W., Kim, J. S., Talwalkar, A., & Ma, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory)

Explainable Artificial Intelligence (XAI)Scientific Computing and Data Management

Cited by 14

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods

Amarasinghe, K., Rodolfa, K. T., Jesus, S., Chen, V., Balayan, V., Saleiro, P., Bizarro, P., Talwalkar, A., & Ghani, R. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 13

Assisting Human Decisions in Document Matching

Kim, J. S., Chen, V., Pruthi, D., Shah, N. B., & Talwalkar, A. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Recommender Systems and Techniques

Cited by 3

When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback

Pan, J., Shar, R., Pfau, J., Talwalkar, A., He, H., & Chen, V. (2025) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 0