Ameet Talwalkar

According to our database, Ameet Talwalkar authored at least 106 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

UPS: Towards Foundation Models for PDE Solving via Cross-Modal Adaptation.

Junhong Shen, Tanya Marwah, Ameet Talwalkar

CoRR, 2024

Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes.

Lucio M. Dery, Steven Kolawole, Jean-François Kagey, Virginia Smith, Graham Neubig, Ameet Talwalkar

CoRR, 2024

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

Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Perspectives on incorporating expert feedback into model updates.

Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar

Patterns, July, 2023

Multitask Learning Can Improve Worst-Group Outcomes.

Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig

CoRR, 2023

Do LLMs exhibit human-like response biases? A case study in survey design.

Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, Graham Neubig

CoRR, 2023

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances.

Mikhail Khodak, Edmond Chow, Maria-Florina Balcan, Ameet Talwalkar

CoRR, 2023

Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms.

Nari Johnson, Ángel Alexander Cabrera, Gregory Plumb, Ameet Talwalkar

CoRR, 2023

Learning Personalized Decision Support Policies.

Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar

CoRR, 2023

Assisting Human Decisions in Document Matching.

Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar

CoRR, 2023

Cross-Modal Fine-Tuning: Align then Refine.

Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar

Proceedings of the International Conference on Machine Learning, 2023

AANG : Automating Auxiliary Learning.

Lucio M. Dery, Paul Michel, Mikhail Khodak, Graham Neubig, Ameet Talwalkar

Proceedings of the Eleventh International Conference on Learning Representations, 2023

FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines.

Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning.

Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I. Hong, Adam Perer

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022

Finding and Fixing Spurious Patterns with Explanations.

Gregory Plumb, Marco Túlio Ribeiro, Ameet Talwalkar

Trans. Mach. Learn. Res., 2022

On Noisy Evaluation in Federated Hyperparameter Tuning.

Kevin Kuo, Pratiksha Thaker, Mikhail Khodak, John Nguyen, Daniel Jiang, Ameet Talwalkar, Virginia Smith

CoRR, 2022

AutoML for Climate Change: A Call to Action.

Renbo Tu, Nicholas Roberts, Vishak Prasad, Sibasis Nayak, Paarth Jain, Frederic Sala, Ganesh Ramakrishnan, Ameet Talwalkar, Willie Neiswanger, Colin White

CoRR, 2022

SONAR: Joint Architecture and System Optimization Search.

Elias Jääsaari, Michelle Ma, Ameet Talwalkar, Tianqi Chen

CoRR, 2022

Provably tuning the ElasticNet across instances.

Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar

CoRR, 2022

Evaluating Systemic Error Detection Methods using Synthetic Images.

Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera, Marco Túlio Ribeiro, Ameet Talwalkar

CoRR, 2022

Learning Predictions for Algorithms with Predictions.

Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii

CoRR, 2022

Interpretable machine learning: moving from mythos to diagnostics.

Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar

Commun. ACM, 2022

NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks.

Renbo Tu, Nicholas Roberts, Mikhail Khodak, Junhong Shen, Frederic Sala, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Efficient Architecture Search for Diverse Tasks.

Junhong Shen, Mikhail Khodak, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Predictions for Algorithms with Predictions.

Misha Khodak, Maria-Florina Balcan, Ameet Talwalkar, Sergei Vassilvitskii

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bayesian Persuasion for Algorithmic Recourse.

Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Use-Case-Grounded Simulations for Explanation Evaluation.

Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Provably tuning the ElasticNet across instances.

Maria-Florina Balcan, Misha Khodak, Dravyansh Sharma, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sanity Simulations for Saliency Methods.

Joon Sik Kim, Gregory Plumb, Ameet Talwalkar

Proceedings of the International Conference on Machine Learning, 2022

Should We Be Pre-training? An Argument for End-task Aware Training as an Alternative.

Lucio M. Dery, Paul Michel, Ameet Talwalkar, Graham Neubig

Proceedings of the Tenth International Conference on Learning Representations, 2022

Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks.

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

Proceedings of the 8th IEEE International Conference on Collaboration and Internet Computing, 2022

2021

NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search.

Renbo Tu, Mikhail Khodak, Nicholas Roberts, Ameet Talwalkar

CoRR, 2021

A Field Guide to Federated Optimization.

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

CoRR, 2021

Towards Connecting Use Cases and Methods in Interpretable Machine Learning.

Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar

CoRR, 2021

Rethinking Neural Operations for Diverse Tasks.

Nicholas Roberts, Mikhail Khodak, Tri Dao, Liam Li, Christopher Ré, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale.

Nicholas Roberts, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, Shuaicheng Niu, Jianyu Heng, Hongyang Qin, Minwen Deng, Johannes Hog, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan R. Sparks

Proceedings of the NeurIPS 2022 Competition Track, 2021

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing.

Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning-to-learn non-convex piecewise-Lipschitz functions.

Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Learning Theoretic Perspective on Local Explainability.

Jeffrey Li, Vaishnavh Nagarajan, Gregory Plumb, Ameet Talwalkar

Proceedings of the 9th International Conference on Learning Representations, 2021

Geometry-Aware Gradient Algorithms for Neural Architecture Search.

Liam Li, Mikhail Khodak, Nina Balcan, Ameet Talwalkar

Proceedings of the 9th International Conference on Learning Representations, 2021

Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability.

Jeremy Cohen, Simran Kaur, Yuanzhi Li, J. Zico Kolter, Ameet Talwalkar

Proceedings of the 9th International Conference on Learning Representations, 2021

On Data Efficiency of Meta-learning.

Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Federated Learning: Challenges, Methods, and Future Directions.

Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith

IEEE Signal Process. Mag., 2020

Model-Agnostic Characterization of Fairness Trade-offs.

Joon Sik Kim, Jiahao Chen, Ameet Talwalkar

CoRR, 2020

Regularizing Black-box Models for Improved Interpretability.

Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Federated Optimization in Heterogeneous Networks.

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Proceedings of Machine Learning and Systems 2020, 2020

A System for Massively Parallel Hyperparameter Tuning.

Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Jonathan Ben-tzur, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

Proceedings of Machine Learning and Systems 2020, 2020

Explaining Groups of Points in Low-Dimensional Representations.

Gregory Plumb, Jonathan Terhorst, Sriram Sankararaman, Ameet Talwalkar

Proceedings of the 37th International Conference on Machine Learning, 2020

FACT: A Diagnostic for Group Fairness Trade-offs.

Joon Sik Kim, Jiahao Chen, Ameet Talwalkar

Proceedings of the 37th International Conference on Machine Learning, 2020

Differentially Private Meta-Learning.

Jeffrey Li, Mikhail Khodak, Sebastian Caldas, Ameet Talwalkar

Proceedings of the 8th International Conference on Learning Representations, 2020

Learning Fair Representations for Kernel Models.

Zilong Tan, Samuel Yeom, Matt Fredrikson, Ameet Talwalkar

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version).

Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar

CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.

Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. 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

CoRR, 2019

Exploiting Reuse in Pipeline-Aware Hyperparameter Tuning.

Liam Li, Evan R. Sparks, Kevin G. Jamieson, Ameet Talwalkar

CoRR, 2019

One-Shot Federated Learning.

Neel Guha, Ameet Talwalkar, Virginia Smith

CoRR, 2019

Regularizing Black-box Models for Improved Interpretability.

Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar

CoRR, 2019

Random Search and Reproducibility for Neural Architecture Search.

Liam Li, Ameet Talwalkar

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Adaptive Gradient-Based Meta-Learning Methods.

Mikhail Khodak, Maria-Florina Balcan, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Provable Guarantees for Gradient-Based Meta-Learning.

Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar

Proceedings of the 36th International Conference on Machine Learning, 2019

FedDANE: A Federated Newton-Type Method.

Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

Expanding the Reach of Federated Learning by Reducing Client Resource Requirements.

Sebastian Caldas, Jakub Konecný, H. Brendan McMahan, Ameet Talwalkar

CoRR, 2018

On the Convergence of Federated Optimization in Heterogeneous Networks.

Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith

CoRR, 2018

LEAF: A Benchmark for Federated Settings.

Sebastian Caldas, Peter Wu, Tian Li, Jakub Konecný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

CoRR, 2018

Massively Parallel Hyperparameter Tuning.

Liam Li, Kevin G. Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, Ameet Talwalkar

CoRR, 2018

Supervised Local Modeling for Interpretability.

Gregory Plumb, Denali Molitor, Ameet Talwalkar

CoRR, 2018

Model Agnostic Supervised Local Explanations.

Gregory Plumb, Denali Molitor, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.

Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

J. Mach. Learn. Res., 2017

Parle: parallelizing stochastic gradient descent.

Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar

CoRR, 2017

Federated Multi-Task Learning.

Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variable Importance Using Decision Trees.

S. Jalil Kazemitabar, Arash A. Amini, Adam Bloniarz, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Paleo: A Performance Model for Deep Neural Networks.

Hang Qi, Evan R. Sparks, Ameet Talwalkar

Proceedings of the 5th International Conference on Learning Representations, 2017

Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization.

Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

Proceedings of the 5th International Conference on Learning Representations, 2017

Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems.

Ariyam Das, Ishan Upadhyaya, Xiangrui Meng, Ameet Talwalkar

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

2016

MLlib: Machine Learning in Apache Spark.

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

J. Mach. Learn. Res., 2016

Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits.

Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, Ameet Talwalkar

CoRR, 2016

Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale.

Firas Abuzaid, Joseph K. Bradley, Feynman T. Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Non-stochastic Best Arm Identification and Hyperparameter Optimization.

Kevin G. Jamieson, Ameet Talwalkar

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Supervised Neighborhoods for Distributed Nonparametric Regression.

Adam Bloniarz, Ameet Talwalkar, Bin Yu, Christopher Wu

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Distributed matrix completion and robust factorization.

Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan

J. Mach. Learn. Res., 2015

TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries.

Evan R. Sparks, Ameet Talwalkar, Michael J. Franklin, Michael I. Jordan, Tim Kraska

CoRR, 2015

Automating model search for large scale machine learning.

Evan R. Sparks, Ameet Talwalkar, Daniel Haas, Michael J. Franklin, Michael I. Jordan, Tim Kraska

Proceedings of the Sixth ACM Symposium on Cloud Computing, 2015

2014

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network.

Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song

ACM Trans. Intell. Syst. Technol., 2014

SMaSH: a benchmarking toolkit for human genome variant calling.

Ameet Talwalkar, Jesse Liptrap, Julie Newcomb, Christopher Hartl, Jonathan Terhorst, Kristal Curtis, Ma'ayan Bresler, Yun S. Song, Michael I. Jordan, David A. Patterson

Bioinform., 2014

Knowing when you're wrong: building fast and reliable approximate query processing systems.

Sameer Agarwal, Henry Milner, Ariel Kleiner, Ameet Talwalkar, Michael I. Jordan, Samuel Madden, Barzan Mozafari, Ion Stoica

Proceedings of the International Conference on Management of Data, 2014

Changepoint Analysis for Efficient Variant Calling.

Adam Bloniarz, Ameet Talwalkar, Jonathan Terhorst, Michael I. Jordan, David A. Patterson, Bin Yu, Yun S. Song

Proceedings of the Research in Computational Molecular Biology, 2014

2013

Large-scale SVD and manifold learning.

Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry A. Rowley

J. Mach. Learn. Res., 2013

Divide-and-Conquer Subspace Segmentation

Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan

CoRR, 2013

A general bootstrap performance diagnostic.

Ariel Kleiner, Ameet Talwalkar, Sameer Agarwal, Ion Stoica, Michael I. Jordan

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

MLI: An API for Distributed Machine Learning.

Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, Tim Kraska

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Distributed Low-Rank Subspace Segmentation.

Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan

Proceedings of the IEEE International Conference on Computer Vision, 2013

MLbase: A Distributed Machine-learning System.

Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, Michael I. Jordan

Proceedings of the Sixth Biennial Conference on Innovative Data Systems Research, 2013

2012

Sampling Methods for the Nyström Method.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

J. Mach. Learn. Res., 2012

The Big Data Bootstrap.

Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan

Proceedings of the 29th International Conference on Machine Learning, 2012

Foundations of Machine Learning.

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Adaptive computation and machine learning, MIT Press, ISBN: 978-0-262-01825-8, 2012

2011

Can matrix coherence be efficiently and accurately estimated?

Mehryar Mohri, Ameet Talwalkar

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)

Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song

CoRR, 2011

Divide-and-Conquer Matrix Factorization.

Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Matrix Approximation for Large-scale Learning.

Ameet Talwalkar

PhD thesis, 2010

On the Impact of Kernel Approximation on Learning Accuracy.

Corinna Cortes, Mehryar Mohri, Ameet Talwalkar

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

On the Estimation of Coherence

Mehryar Mohri, Ameet Talwalkar

CoRR, 2010

Matrix Coherence and the Nystrom Method.

Ameet Talwalkar, Afshin Rostamizadeh

Proceedings of the UAI 2010, 2010

2009

Sampling Techniques for the Nystrom Method.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Ensemble Nystrom Method.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

On sampling-based approximate spectral decomposition.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Sequence kernels for predicting protein essentiality.

Cyril Allauzen, Mehryar Mohri, Ameet Talwalkar

Proceedings of the Machine Learning, 2008

Large-scale manifold learning.

Ameet Talwalkar, Sanjiv Kumar, Henry A. Rowley

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008