Balancing traffic load in wireless networks with curveball routing. - Lucian Popa 002; Afshin Rostamizadeh; Richard M. Karp; Christos H. Papadimitriou; Ion Stoica (MobiHoc, 2007)

Stability Bounds for Non-i.i.d. Processes. - Mehryar Mohri; Afshin Rostamizadeh (NIPS, 2007)

Sample Selection Bias Correction Theory. - Corinna Cortes; Mehryar Mohri; Michael Riley 001; Afshin Rostamizadeh (ALT, 2008)

Domain Adaptation with Multiple Sources. - Yishay Mansour; Mehryar Mohri; Afshin Rostamizadeh (NIPS, 2008)

Rademacher Complexity Bounds for Non-I.I.D. Processes. - Mehryar Mohri; Afshin Rostamizadeh (NIPS, 2008)

Stability Bound for Stationary Phi-mixing and Beta-mixing Processes - Mehryar Mohri; Afshin Rostamizadeh (CoRR, 2008)

Domain Adaptation: Learning Bounds and Algorithms. - Yishay Mansour; Mehryar Mohri; Afshin Rostamizadeh (COLT, 2009)

Learning Non-Linear Combinations of Kernels. - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (NIPS, 2009)

L2 Regularization for Learning Kernels. - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (UAI, 2009)

Multiple Source Adaptation and the Rényi Divergence. - Yishay Mansour; Mehryar Mohri; Afshin Rostamizadeh (UAI, 2009)

New Generalization Bounds for Learning Kernels - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (CoRR, 2009)

Two-Stage Learning Kernel Algorithms. - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (ICML, 2010)

Generalization Bounds for Learning Kernels. - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (ICML, 2010)

Matrix Coherence and the Nystrom Method. - Ameet Talwalkar; Afshin Rostamizadeh (UAI, 2010)

Stability Bounds for Stationary phi-mixing and beta-mixing Processes. - Mehryar Mohri; Afshin Rostamizadeh (J. Mach. Learn. Res., 2010)

Theoretical Foundations and Algorithms for Learning with Multiple Kernels. - Afshin Rostamizadeh (2010)

Ensembles of Kernel Predictors. - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (UAI, 2011)

Learning with Missing Features. - Afshin Rostamizadeh; Alekh Agarwal; Peter L. Bartlett (UAI, 2011)

Online and Batch Learning Algorithms for Data with Missing Features - Afshin Rostamizadeh; Alekh Agarwal; Peter L. Bartlett (CoRR, 2011)

Foundations of Machine Learning. - Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar (MIT Press, 2012)

Algorithms for Learning Kernels Based on Centered Alignment. - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (J. Mach. Learn. Res., 2012)

Multi-Class Classification with Maximum Margin Multiple Kernel. - Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh (ICML (3), 2013)

Learning Prices for Repeated Auctions with Strategic Buyers. - Kareem Amin 002; Afshin Rostamizadeh; Umar Syed (NIPS, 2013)

Perceptron Mistake Bounds - Mehryar Mohri; Afshin Rostamizadeh (CoRR, 2013)

Corporate learning at scale: lessons from a large online course at google. - Arthur Asuncion; Jac de Haan; Mehryar Mohri; Kayur Patel; Afshin Rostamizadeh; Umar Syed; Lauren Wong (L@S, 2014)

Repeated Contextual Auctions with Strategic Buyers. - Kareem Amin 002; Afshin Rostamizadeh; Umar Syed (NIPS, 2014)

Scalable and Interpretable Data Representation for High-Dimensional, Complex Data. - Been Kim; Kayur Patel; Afshin Rostamizadeh; Julie A. Shah (AAAI, 2015)

Applying WebTables in Practice. - Sreeram Balakrishnan; Alon Y. Halevy; Boulos Harb; Hongrae Lee; Jayant Madhavan; Afshin Rostamizadeh; Warren Shen; Kenneth Wilder; Fei Wu 003; Cong Yu 001 (CIDR, 2015)

An Optimal Online Algorithm For Retrieving Heavily Perturbed Statistical Databases In The Low-Dimensional Querying Model. - Krzysztof Marcin Choromanski; Afshin Rostamizadeh; Umar Syed (CIKM, 2015)

Generalization Bounds for Supervised Dimensionality Reduction. - Mehryar Mohri; Afshin Rostamizadeh; Dmitry Storcheus (FE@NIPS, 2015)

A Survey of Modern Questions and Challenges in Feature Extraction. - Dmitry Storcheus; Afshin Rostamizadeh; Sanjiv Kumar (FE@NIPS, 2015)

An $ tilde{O}( frac{1}{ sqrt{T}})$-error online algorithm for retrieving heavily perturbated statistical databases in the low-dimensional querying mode. - Krzysztof Choromanski; Afshin Rostamizadeh; Umar Syed (CoRR, 2015)

Foundations of Coupled Nonlinear Dimensionality Reduction. - Dmitry Storcheus; Mehryar Mohri; Afshin Rostamizadeh (CoRR, 2015)

Greedy Column Subset Selection: New Bounds and Distributed Algorithms. - Jason M. Altschuler; Aditya Bhaskara; Gang Fu; Vahab S. Mirrokni; Afshin Rostamizadeh; Morteza Zadimoghaddam (ICML, 2016)

Where to Sell: Simulating Auctions From Learning Algorithms. - Hamid Nazerzadeh; Renato Paes Leme; Afshin Rostamizadeh; Umar Syed (EC, 2016)

Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. - Lisha Li; Kevin G. Jamieson; Giulia DeSalvo; Afshin Rostamizadeh; Ameet Talwalkar (CoRR, 2016)

Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization. - Lisha Li; Kevin G. Jamieson; Giulia DeSalvo; Afshin Rostamizadeh; Ameet Talwalkar (ICLR (Poster), 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)

The Sparse Recovery Autoencoder. - Shanshan Wu; Alexandros G. Dimakis; Sujay Sanghavi; Felix X. Yu; Daniel Niels Holtmann-Rice; Dmitry Storcheus; Afshin Rostamizadeh; Sanjiv Kumar (CoRR, 2018)

Massively Parallel Hyperparameter Tuning. - Liam Li; Kevin G. Jamieson; Afshin Rostamizadeh; Ekaterina Gonina; Moritz Hardt; Benjamin Recht; Ameet Talwalkar (CoRR, 2018)

Categorical Feature Compression via Submodular Optimization. - MohammadHossein Bateni; Lin Chen; Hossein Esfandiari; Thomas Fu; Vahab S. Mirrokni; Afshin Rostamizadeh (ICML, 2019)

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling. - Shanshan Wu; Alex Dimakis; Sujay Sanghavi; Felix X. Yu; Daniel Niels Holtmann-Rice; Dmitry Storcheus; Afshin Rostamizadeh; Sanjiv Kumar (ICML, 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; et al. (CoRR, 2019)

The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation. - Jean-François Kagy; Tolga Kayadelen; Ji Ma; Afshin Rostamizadeh; Jana Strnadová (CoRR, 2019)

Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels. - Shuang Song; David Berthelot; Afshin Rostamizadeh (CoRR, 2019)

Understanding the Effects of Batching in Online Active Learning. - Kareem Amin 002; Corinna Cortes; Giulia DeSalvo; Afshin Rostamizadeh (AISTATS, 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 (MLSys, 2020)

An Analysis of SVD for Deep Rotation Estimation. - Jake Levinson; Carlos Esteves; Kefan Chen; Noah Snavely; Angjoo Kanazawa; Afshin Rostamizadeh; Ameesh Makadia (NeurIPS, 2020)

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. - Maruan Al-Shedivat; Jennifer Gillenwater; Eric P. Xing; Afshin Rostamizadeh (ICLR, 2021)

Active Covering. - Heinrich Jiang; Afshin Rostamizadeh (ICML, 2021)

Learning with Labeling Induced Abstentions. - Kareem Amin 002; Giulia DeSalvo; Afshin Rostamizadeh (NeurIPS, 2021)

Batch Active Learning at Scale. - Gui Citovsky; Giulia DeSalvo; Claudio Gentile; Lazaros Karydas; Anand Rajagopalan; Afshin Rostamizadeh; Sanjiv Kumar (NeurIPS, 2021)

Churn Reduction via Distillation. - Heinrich Jiang; Harikrishna Narasimhan; Dara Bahri; Andrew Cotter; Afshin Rostamizadeh (ICLR, 2022)

Vexation-Aware Active Learning for On-Menu Restaurant Dish Availability. - Jean-François Kagy; Flip Korn; Afshin Rostamizadeh; Chris Welty (KDD, 2022)

Is margin all you need? An extensive empirical study of active learning on tabular data. - Dara Bahri; Heinrich Jiang; Tal Schuster; Afshin Rostamizadeh (CoRR, 2022)

Leveraging Importance Weights in Subset Selection. - Gui Citovsky; Giulia DeSalvo; Sanjiv Kumar; Srikumar Ramalingam; Afshin Rostamizadeh; Yunjuan Wang (ICLR, 2023)

DistillSpec: Improving Speculative Decoding via Knowledge Distillation. - Yongchao Zhou; Kaifeng Lyu; Ankit Singh Rawat; Aditya Krishna Menon; Afshin Rostamizadeh; Sanjiv Kumar; Jean-François Kagy; Rishabh Agarwal (ICLR, 2024)

SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection. - Ke Ye; Heinrich Jiang; Afshin Rostamizadeh; Ayan Chakrabarti; Giulia DeSalvo; Jean-François Kagy; Lazaros Karydas; Gui Citovsky; Sanjiv Kumar (CoRR, 2024)

No more hard prompts: SoftSRV prompting for synthetic data generation. - Giulia DeSalvo; Jean-François Kagy; Lazaros Karydas; Afshin Rostamizadeh; Sanjiv Kumar (CoRR, 2024)

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. - Ankit Singh Rawat; Veeranjaneyulu Sadhanala; Afshin Rostamizadeh; Ayan Chakrabarti; Wittawat Jitkrittum; Vladimir Feinberg; Seungyeon Kim 001; Hrayr Harutyunyan; Nikunj Saunshi; Zachary Nado; Rakesh Shivanna; Sashank J. Reddi; et al. (CoRR, 2024)

Analyzing Similarity Metrics for Data Selection for Language Model Pretraining. - Dylan Sam; Ayan Chakrabarti; Afshin Rostamizadeh; Srikumar Ramalingam; Gui Citovsky; Sanjiv Kumar (CoRR, 2025)

This Time is Different: An Observability Perspective on Time Series Foundation Models. - Ben Cohen; Emaad Khwaja; Youssef Doubli; Salahidine Lemaachi; Chris Lettieri; Charles Masson; Hugo Miccinilli; Elise Ramé; Qiqi Ren; Afshin Rostamizadeh; Jean Ogier du Terrail; Anna-Monica Toon; et al. (CoRR, 2025)