His primary areas of investigation include Artificial intelligence, Machine learning, Key, Data science and Benchmark. Ameet Talwalkar combines topics linked to Big data with his work on Artificial intelligence. Machine learning is closely attributed to Distributed algorithm in his study.
As part of his studies on Key, he often connects relevant areas like Ranking. His biological study spans a wide range of topics, including Statistical model and Federated learning. His research in Benchmark intersects with topics in Robustness and Code.
His main research concerns Artificial intelligence, Machine learning, Theoretical computer science, Mathematical optimization and Data mining. His study on Interpretability and Feature selection is often connected to Generalization and Convex optimization as part of broader study in Artificial intelligence. His work in Machine learning covers topics such as Range which are related to areas like Structural variant.
His Theoretical computer science research integrates issues from Representation and Sufficient dimension reduction. His Mathematical optimization research is multidisciplinary, incorporating elements of Artificial neural network, Algorithm and Kernel method. His work on Data analysis as part of general Data mining study is frequently connected to Quality, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Ameet Talwalkar focuses on Artificial intelligence, Theoretical computer science, Mathematical optimization, Machine learning and Generalization. His research on Artificial intelligence frequently links to adjacent areas such as Differential privacy. His Theoretical computer science study combines topics from a wide range of disciplines, such as Artificial neural network and Representation.
His study looks at the intersection of Mathematical optimization and topics like Class with Confusion matrix. His research on Machine learning focuses in particular on Stability. The study incorporates disciplines such as Black box and Domain knowledge in addition to Interpretability.
His primary scientific interests are in Statistical model, Artificial intelligence, Machine learning, Data science and Federated learning. Many of his studies on Statistical model involve topics that are commonly interrelated, such as Theoretical computer science. Ameet Talwalkar undertakes interdisciplinary study in the fields of Artificial intelligence and Quality through his research.
His Machine learning study often links to related topics such as Language model.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Foundations of Machine Learning
Mehryar Mohri;Afshin Rostamizadeh;Afshin Rostamizadeh;Ameet Talwalkar;Ameet Talwalkar.
MLlib: machine learning in apache spark
Xiangrui Meng;Joseph Bradley;Burak Yavuz;Evan Sparks.
Journal of Machine Learning Research (2016)
A large-scale evaluation of computational protein function prediction
Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes.
Nature Methods (2013)
Hyperband: a novel bandit-based approach to hyperparameter optimization
Lisha Li;Kevin Jamieson;Giulia DeSalvo;Afshin Rostamizadeh.
Journal of Machine Learning Research (2017)
MLbase: A Distributed Machine-learning System
Tim Kraska;Ameet Talwalkar;John C. Duchi;Rean Griffith.
conference on innovative data systems research (2013)
Federated Learning: Challenges, Methods, and Future Directions
Tian Li;Anit Kumar Sahu;Ameet Talwalkar;Virginia Smith.
IEEE Signal Processing Magazine (2020)
Federated multi-task learning
Virginia Smith;Chao-Kai Chiang;Maziar Sanjabi;Ameet Talwalkar.
neural information processing systems (2017)
Sampling methods for the Nyström method
Sanjiv Kumar;Mehryar Mohri;Ameet Talwalkar.
Journal of Machine Learning Research (2012)
A scalable bootstrap for massive data
Ariel Kleiner;Ameet Talwalkar;Purnamrita Sarkar;Michael I. Jordan.
Journal of The Royal Statistical Society Series B-statistical Methodology (2014)
The Foundations of Machine Learning
Mehryar Mohri;Afshin Rostamizadeh;Ameet Talwalkar.
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