Ameet Talwalkar
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I am an associate professor in the Machine Learning Department at CMU and a Venture Partner at Amplify Partners. I am broadly motivated by the goal of democratizing machine learning, with an interest in ML for science, human-AI interaction, and problems at the intersection of systems and learning. My research leverages ideas from the fields of statistical machine learning, distributed systems, optimization, and computational learning theory. Here is my CV, Google scholar page, and formal bio. |
Selected Work
- Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
(pdf, website, blog)
A. Cabrera, E. Fu, D. Bertucci, K. Hostein, A. Talwalkar, J. Hong, A. Perer
Conference on Human Factors in Computing Systems (CHI), 2023 - Cross-Modal Fine-Tuning: Align then Refine
(pdf)
J. Shen, L. Li, L. Dery, C. Staten, M. Khodak, G. Neubig, A. Talwalkar
International Conference on Machine Learning (ICML), 2023 - Interpretable Machine Learning: Moving From Mythos to Diagnostics
(pdf, longer version, blog)
V. Chen, J. Li, J. Kim, G. Plumb, A. Talwalkar
Communications of the ACM (CACM), 2022 - Federated Learning: Challenges, Methods, and Future Directions
(pdf, blog)
T. Li, A. Sahu, A. Talwalkar, V. Smith
IEEE Signal Processing Magazine, Special Issue on Distributed, Streaming Machine Learning, 2020 - Adaptive Gradient-Based Meta-Learning Methods
(preprint, blog)
M. Khodak, M.F. Balcan, A. Talwalkar
Neural Information Processing Systems (NeurIPS), 2019 - Random Search and Reproducibility for Neural Architecture Search
(preprint, blog, talk)
L. Li, A. Talwalkar
Conference on Uncertainty in Artificial Intelligence (UAI), 2019 - Toward the Jet Age of Machine Learning (link)
A. Talwalkar
O'Reilly Ideas AI Blog, 2018 - Foundations of Machine Learning, 2nd Edition (hardcopy,
pdf,
html)
M. Mohri, A. Rostamizadeh, A. Talwalkar
MIT Press, 2018 - Supervised Local Modeling for Interpretability
(pdf, blog)
G. Plumb, D. Molitor, A. Talwalkar
Neural Information Processing Systems (NeurIPS), 2018 - Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
(pdf)
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, A. Talwalkar
Journal of Machine Learning Research (JMLR), 2018 - Federated Multi-task Learning
(pdf)
V. Smith, C. Chiang, M. Sanjabi, A. Talwalkar
Neural Information Processing Systems (NIPS), 2017 - MLlib: Machine Learning in Apache Spark (pdf)
X. Meng, J. Bradley, B. Yuvaz, E. Sparks, S. Venkataraman, D. Liu, J. Freeman, D. Tsai, M. Amde, S. Owen, D. Xin, R. Xin, M. Franklin, R. Zadeh, M. Zaharia, A. Talwalkar
Journal of Machine Learning Research (JMLR), 2016 - CS120x Distributed Machine Learning with Apache Spark
Massive Open Online Course (MOOC) on EdX Platform - Distributed Matrix Completion and Robust Factorization (pdf)
L. Mackey, A. Talwalkar, M. I. Jordan
Journal of Machine Learning Research (JMLR), 2015 - MLbase: A Distributed Machine Learning System (pdf)
T. Kraska, A. Talwalkar, J. Duchi, R. Griffith, M.J. Franklin, M.I. Jordan
Conference on Innovative Data Systems Research (CIDR), 2013 - A Scalable Bootstrap for Massive Data (pdf)
A. Kleiner, A. Talwalkar, P. Sarkar, M.I. Jordan
Journal of the Royal Statistical Society, Series B (JRSS-B), 2013 - Sampling Methods for the Nyström method (pdf)
S. Kumar, M. Mohri, A. Talwalkar
Journal of Machine Learning Research (JMLR), 2012