Natalia Ponomareva

According to our database, Natalia Ponomareva authored at least 24 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation.

Yunjuan Wang, Hussein Hazimeh, Natalia Ponomareva, Alexey Kurakin, Ibrahim Hammoud, Raman Arora

CoRR, 2024

Scaling Laws for Downstream Task Performance of Large Language Models.

Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, Sanmi Koyejo

CoRR, 2024

Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models.

Aldo G. Carranza, Rezsa Farahani, Natalia Ponomareva, Alexey Kurakin, Matthew Jagielski, Milad Nasr

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization.

Xiang Meng, Shibal Ibrahim, Kayhan Behdin, Hussein Hazimeh, Natalia Ponomareva, Rahul Mazumder

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Private prediction for large-scale synthetic text generation.

Kareem Amin, Alex Bie, Weiwei Kong, Alexey Kurakin, Natalia Ponomareva, Umar Syed, Andreas Terzis, Sergei Vassilvitskii

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

2023

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.

Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta

J. Artif. Intell. Res., 2023

Explaining and Adapting Graph Conditional Shift.

Qi Zhu, Yizhu Jiao, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

CoRR, 2023

Harnessing large-language models to generate private synthetic text.

Alexey Kurakin, Natalia Ponomareva, Umar Syed, Liam MacDermed, Andreas Terzis

CoRR, 2023

Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models.

Aldo Gael Carranza, Rezsa Farahani, Natalia Ponomareva, Alex Kurakin, Matthew Jagielski, Milad Nasr

CoRR, 2023

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy.

Natalia Ponomareva, Sergei Vassilvitskii, Zheng Xu, Brendan McMahan, Alexey Kurakin, Chiyaun Zhang

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search.

Shibal Ibrahim, Wenyu Chen, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization.

Riade Benbaki, Wenyu Chen, Xiang Meng, Hussein Hazimeh, Natalia Ponomareva, Zhe Zhao, Rahul Mazumder

Proceedings of the International Conference on Machine Learning, 2023

Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets.

Hussein Hazimeh, Natalia Ponomareva

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance.

Shibal Ibrahim, Natalia Ponomareva, Rahul Mazumder

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Training Text-to-Text Transformers with Privacy Guarantees.

Natalia Ponomareva, Jasmijn Bastings, Sergei Vassilvitskii

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2022, 2022

2021

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data.

Qi Zhu, Natalia Ponomareva, Jiawei Han, Bryan Perozzi

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

2020

The Tree Ensemble Layer: Differentiability meets Conditional Computation.

Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, Rahul Mazumder

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

Accelerating Gradient Boosting Machines.

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni

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

2019

Accelerating Gradient Boosting Machine.

Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni

CoRR, 2019

Agent Prioritization for Autonomous Navigation.

Khaled S. Refaat, Kai Ding, Natalia Ponomareva, Stéphane Ross

Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

2017

TF Boosted Trees: A Scalable TensorFlow Based Framework for Gradient Boosting.

Natalia Ponomareva, Soroush Radpour, Gilbert Hendry, Salem Haykal, Thomas Colthurst, Petr Mitrichev, Alexander Grushetsky

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Text based user comments as a signal for automatic language identification of online videos.

A. Seza Dogruöz, Natalia Ponomareva, Sertan Girgin, Reshu Jain, Christoph Oehler

Proceedings of the 19th ACM International Conference on Multimodal Interaction, 2017

Compact multi-class boosted trees.

Natalia Ponomareva, Thomas Colthurst, Gilbert Hendry, Salem Haykal, Soroush Radpour

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2015

A Classifier for the Latency-CPU Behaviors of Serving Jobs in Distributed Environments.

Christophe Restif, Natalia Ponomareva, Krzysztof Ostrowski

Proceedings of the 7th IEEE International Conference on Cloud Computing Technology and Science, 2015