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ORCIDAmeet Talwalkar
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- affiliation: Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA, USA
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2020 – today
- 2023
[c54]Ángel Alexander Cabrera
, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer:
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning. CHI 2023: 419:1-419:14- [i61]Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer:
Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning. CoRR abs/2302.04732 (2023) - [i60]Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
Cross-Modal Fine-Tuning: Align then Refine. CoRR abs/2302.05738 (2023) - [i59]Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar:
Assisting Human Decisions in Document Matching. CoRR abs/2302.08450 (2023) - [i58]Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar:
Learning Personalized Decision Support Policies. CoRR abs/2304.06701 (2023) - 2022
- [j11]Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Interpretable machine learning: moving from mythos to diagnostics. Commun. ACM 65(8): 43-50 (2022) - [j10]Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar:
Finding and Fixing Spurious Patterns with Explanations. Trans. Mach. Learn. Res. 2022 (2022) - [c53]Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik R. Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness B. Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying:
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. CIC 2022: 16-25 - [c52]Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig:
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative. ICLR 2022 - [c51]Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Sanity Simulations for Saliency Methods. ICML 2022: 11173-11200 - [c50]Maria-Florina F. Balcan, Misha Khodak, Dravyansh Sharma, Ameet Talwalkar:
Provably tuning the ElasticNet across instances. NeurIPS 2022 - [c49]Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS 2022 - [c48]Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu:
Bayesian Persuasion for Algorithmic Recourse. NeurIPS 2022 - [c47]Misha Khodak, Maria-Florina F. Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. NeurIPS 2022 - [c46]Junhong Shen, Mikhail Khodak, Ameet Talwalkar:
Efficient Architecture Search for Diverse Tasks. NeurIPS 2022 - [c45]Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar:
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks. NeurIPS 2022 - [i57]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii:
Learning Predictions for Algorithms with Predictions. CoRR abs/2202.09312 (2022) - [i56]Junhong Shen, Mikhail Khodak, Ameet Talwalkar:
Efficient Architecture Search for Diverse Tasks. CoRR abs/2204.07554 (2022) - [i55]Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar:
Perspectives on Incorporating Expert Feedback into Model Updates. CoRR abs/2205.06905 (2022) - [i54]Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar:
AANG: Automating Auxiliary Learning. CoRR abs/2205.14082 (2022) - [i53]Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar:
Use-Case-Grounded Simulations for Explanation Evaluation. CoRR abs/2206.02256 (2022) - [i52]Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani:
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. CoRR abs/2206.13503 (2022) - [i51]Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Marco Túlio Ribeiro, Ameet Talwalkar:
Evaluating Systemic Error Detection Methods using Synthetic Images. CoRR abs/2207.04104 (2022) - [i50]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Provably tuning the ElasticNet across instances. CoRR abs/2207.10199 (2022) - [i49]Elias Jääsaari, Michelle Ma, Ameet Talwalkar, Tianqi Chen:
SONAR: Joint Architecture and System Optimization Search. CoRR abs/2208.12218 (2022) - [i48]Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White:
AutoML for Climate Change: A Call to Action. CoRR abs/2210.03324 (2022) - [i47]Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith:
On Noisy Evaluation in Federated Hyperparameter Tuning. CoRR abs/2212.08930 (2022) - 2021
- [j9]Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Interpretable Machine Learning: Moving from mythos to diagnostics. ACM Queue 19(6): 28-56 (2021) - [c44]Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar:
On Data Efficiency of Meta-learning. AISTATS 2021: 1369-1377 - [c43]Jeremy M. Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar:
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability. ICLR 2021 - [c42]Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar:
Geometry-Aware Gradient Algorithms for Neural Architecture Search. ICLR 2021 - [c41]Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar:
A Learning Theoretic Perspective on Local Explainability. ICLR 2021 - [c40]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Learning-to-learn non-convex piecewise-Lipschitz functions. NeurIPS 2021: 15056-15069 - [c39]Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar:
Rethinking Neural Operations for Diverse Tasks. NeurIPS 2021: 15855-15869 - [c38]Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar:
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. NeurIPS 2021: 19184-19197 - [i46]Maruan Al-Shedivat, Liam Li, Eric Po Xing, Ameet Talwalkar:
On Data Efficiency of Meta-learning. CoRR abs/2102.00127 (2021) - [i45]Jeremy M. Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar:
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability. CoRR abs/2103.00065 (2021) - [i44]Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Towards Connecting Use Cases and Methods in Interpretable Machine Learning. CoRR abs/2103.06254 (2021) - [i43]Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar:
Rethinking Neural Operations for Diverse Tasks. CoRR abs/2103.15798 (2021) - [i42]Joon Sik Kim, Gregory Plumb, Ameet Talwalkar:
Sanity Simulations for Saliency Methods. CoRR abs/2105.06506 (2021) - [i41]Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar:
Finding and Fixing Spurious Patterns with Explanations. CoRR abs/2106.02112 (2021) - [i40]Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar:
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. CoRR abs/2106.04502 (2021) - [i39]Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu:
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i38]Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar:
Learning-to-learn non-convex piecewise-Lipschitz functions. CoRR abs/2108.08770 (2021) - [i37]Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig:
Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative. CoRR abs/2109.07437 (2021) - [i36]Renbo Tu, Mikhail Khodak, Nicholas Roberts, Ameet Talwalkar:
NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search. CoRR abs/2110.05668 (2021) - [i35]Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu:
Bayesian Persuasion for Algorithmic Recourse. CoRR abs/2112.06283 (2021) - 2020
- [j8]Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith:
Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Process. Mag. 37(3): 50-60 (2020) - [c37]Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar:
Learning Fair Representations for Kernel Models. AISTATS 2020: 155-166 - [c36]Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar:
Differentially Private Meta-Learning. ICLR 2020 - [c35]Joon Sik Kim, Jiahao Chen, Ameet Talwalkar:
FACT: A Diagnostic for Group Fairness Trade-offs. ICML 2020: 5264-5274 - [c34]Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar:
Explaining Groups of Points in Low-Dimensional Representations. ICML 2020: 7762-7771 - [c33]Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar:
A System for Massively Parallel Hyperparameter Tuning. MLSys 2020 - [c32]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
Federated Optimization in Heterogeneous Networks. MLSys 2020 - [c31]Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability. NeurIPS 2020 - [i34]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. CoRR abs/2001.01920 (2020) - [i33]Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar:
Explaining Groups of Points in Low-Dimensional Representations. CoRR abs/2003.01640 (2020) - [i32]Joon Sik Kim, Jiahao Chen, Ameet Talwalkar:
Model-Agnostic Characterization of Fairness Trade-offs. CoRR abs/2004.03424 (2020) - [i31]Liam Li, Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Geometry-Aware Gradient Algorithms for Neural Architecture Search. CoRR abs/2004.07802 (2020) - [i30]Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar:
A Learning Theoretic Perspective on Local Explainability. CoRR abs/2011.01205 (2020)
2010 – 2019
- 2019
- [c30]Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith:
FedDANE: A Federated Newton-Type Method. ACSSC 2019: 1227-1231 - [c29]Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. ICML 2019: 424-433 - [c28]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. NeurIPS 2019: 5915-5926 - [c27]Liam Li, Ameet Talwalkar:
Random Search and Reproducibility for Neural Architecture Search. UAI 2019: 367-377 - [e1]Ameet Talwalkar, Virginia Smith, Matei Zaharia:
Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019. mlsys.org 2019 [contents] - [i29]Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability. CoRR abs/1902.06787 (2019) - [i28]Liam Li, Ameet Talwalkar:
Random Search and Reproducibility for Neural Architecture Search. CoRR abs/1902.07638 (2019) - [i27]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. CoRR abs/1902.10644 (2019) - [i26]Neel Guha, Ameet Talwalkar, Virginia Smith:
One-Shot Federated Learning. CoRR abs/1902.11175 (2019) - [i25]Liam Li, Evan R. Sparks, Kevin G. Jamieson, Ameet Talwalkar:
Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning. CoRR abs/1903.05176 (2019) - [i24]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i23]Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version). CoRR abs/1906.01431 (2019) - [i22]Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar:
Adaptive Gradient-Based Meta-Learning Methods. CoRR abs/1906.02717 (2019) - [i21]Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar:
Learning Fair Representations for Kernel Models. CoRR abs/1906.11813 (2019) - [i20]Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith:
Federated Learning: Challenges, Methods, and Future Directions. CoRR abs/1908.07873 (2019) - [i19]Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar:
Differentially Private Meta-Learning. CoRR abs/1909.05830 (2019) - 2018
- [c26]Gregory Plumb, Denali Molitor, Ameet Talwalkar:
Model Agnostic Supervised Local Explanations. NeurIPS 2018: 2520-2529 - [i18]Gregory Plumb, Denali Molitor, Ameet Talwalkar:
Supervised Local Modeling for Interpretability. CoRR abs/1807.02910 (2018) - [i17]Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar:
Massively Parallel Hyperparameter Tuning. CoRR abs/1810.05934 (2018) - [i16]Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar:
LEAF: A Benchmark for Federated Settings. CoRR abs/1812.01097 (2018) - [i15]Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith:
On the Convergence of Federated Optimization in Heterogeneous Networks. CoRR abs/1812.06127 (2018) - [i14]Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar:
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements. CoRR abs/1812.07210 (2018) - 2017
- [j7]Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar:
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization. J. Mach. Learn. Res. 18: 185:1-185:52 (2017) - [c25]Ariyam Das, Ishan Upadhyaya, Xiangrui Meng, Ameet Talwalkar:
Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems. CIKM 2017: 969-977 - [c24]Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar:
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. ICLR (Poster) 2017 - [c23]Hang Qi, Evan R. Sparks, Ameet Talwalkar:
Paleo: A Performance Model for Deep Neural Networks. ICLR (Poster) 2017 - [c22]S. Jalil Kazemitabar, Arash A. Amini, Adam Bloniarz, Ameet Talwalkar:
Variable Importance Using Decision Trees. NIPS 2017: 426-435 - [c21]Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar:
Federated Multi-Task Learning. NIPS 2017: 4424-4434 - [i13]Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar:
Federated Multi-Task Learning. CoRR abs/1705.10467 (2017) - [i12]Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar:
Parle: parallelizing stochastic gradient descent. CoRR abs/1707.00424 (2017) - 2016
- [j6]Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan R. Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar:
MLlib: Machine Learning in Apache Spark. J. Mach. Learn. Res. 17: 34:1-34:7 (2016) - [c20]Kevin G. Jamieson, Ameet Talwalkar:
Non-stochastic Best Arm Identification and Hyperparameter Optimization. AISTATS 2016: 240-248 - [c19]Adam Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu:
Supervised Neighborhoods for Distributed Nonparametric Regression. AISTATS 2016: 1450-1459 - [c18]Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet Talwalkar:
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale. NIPS 2016: 3810-3818 - [i11]Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar:
Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. CoRR abs/1603.06560 (2016) - 2015
- [j5]Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Distributed matrix completion and robust factorization. J. Mach. Learn. Res. 16: 913-960 (2015) - [c17]Evan R. Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
Automating model search for large scale machine learning. SoCC 2015: 368-380 - [i10]Evan R. Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries. CoRR abs/1502.00068 (2015) - [i9]Kevin G. Jamieson, Ameet Talwalkar:
Non-stochastic Best Arm Identification and Hyperparameter Optimization. CoRR abs/1502.07943 (2015) - [i8]Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan R. Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar:
MLlib: Machine Learning in Apache Spark. CoRR abs/1505.06807 (2015) - 2014
- [j4]Ameet Talwalkar, Jesse Liptrap, Julie Newcomb, Christopher Hartl, Jonathan Terhorst, Kristal Curtis, Ma'ayan Bresler, Yun S. Song, Michael I. Jordan, David A. Patterson:
SMaSH: a benchmarking toolkit for human genome variant calling. Bioinform. 30(19): 2787-2795 (2014) - [j3]Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song:
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network. ACM Trans. Intell. Syst. Technol. 5(2): 27:1-27:20 (2014) - [c16]Adam Bloniarz, Ameet Talwalkar, Jonathan Terhorst, Michael I. Jordan, David A. Patterson, Bin Yu, Yun S. Song:
Changepoint Analysis for Efficient Variant Calling. RECOMB 2014: 20-34 - [c15]Sameer Agarwal, Henry Milner, Ariel Kleiner, Ameet Talwalkar, Michael I. Jordan, Samuel Madden, Barzan Mozafari, Ion Stoica:
Knowing when you're wrong: building fast and reliable approximate query processing systems. SIGMOD Conference 2014: 481-492 - [i7]Ameet Talwalkar, Afshin Rostamizadeh:
Matrix Coherence and the Nystrom Method. CoRR abs/1408.2044 (2014) - 2013
- [j2]Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry A. Rowley:
Large-scale SVD and manifold learning. J. Mach. Learn. Res. 14(1): 3129-3152 (2013) - [c14]Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan:
MLbase: A Distributed Machine-learning System. CIDR 2013 - [c13]Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan:
Distributed Low-Rank Subspace Segmentation. ICCV 2013: 3543-3550 - [c12]Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
MLI: An API for Distributed Machine Learning. ICDM 2013: 1187-1192 - [c11]Ariel Kleiner, Ameet Talwalkar, Sameer Agarwal, Ion Stoica, Michael I. Jordan:
A general bootstrap performance diagnostic. KDD 2013: 419-427 - [i6]Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan:
Divide-and-Conquer Subspace Segmentation. CoRR abs/1304.5583 (2013) - [i5]Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska:
MLI: An API for Distributed Machine Learning. CoRR abs/1310.5426 (2013) - 2012
- [b2]Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar:
Foundations of Machine Learning. Adaptive computation and machine learning, MIT Press 2012, ISBN 978-0-262-01825-8, pp. I-XII, 1-412 - [j1]Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
Sampling Methods for the Nyström Method. J. Mach. Learn. Res. 13: 981-1006 (2012) - [c10]Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan:
The Big Data Bootstrap. ICML 2012 - 2011
- [c9]Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Divide-and-Conquer Matrix Factorization. NIPS 2011: 1134-1142 - [c8]Mehryar Mohri, Ameet Talwalkar:
Can matrix coherence be efficiently and accurately estimated? AISTATS 2011: 534-542 - [i4]Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Divide-and-Conquer Matrix Factorization. CoRR abs/1107.0789 (2011) - [i3]Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song:
Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN). CoRR abs/1112.3265 (2011) - 2010
- [b1]Ameet Talwalkar:
Matrix Approximation for Large-scale Learning. New York University, USA, 2010 - [c7]Ameet Talwalkar, Afshin Rostamizadeh:
Matrix Coherence and the Nystrom Method. UAI 2010: 572-579 - [c6]Corinna Cortes, Mehryar Mohri, Ameet Talwalkar:
On the Impact of Kernel Approximation on Learning Accuracy. AISTATS 2010: 113-120 - [i2]Ameet Talwalkar, Afshin Rostamizadeh:
Matrix Coherence and the Nystrom Method. CoRR abs/1004.2008 (2010) - [i1]