A Classifier for the Latency-CPU Behaviors of Serving Jobs in Distributed Environments. - Christophe Restif; Natalia Ponomareva 001; Krzysztof Ostrowski (CloudCom, 2015)

Compact multi-class boosted trees. - Natalia Ponomareva 001; Thomas Colthurst; Gilbert Hendry; Salem Haykal; Soroush Radpour (IEEE BigData, 2017)

Text based user comments as a signal for automatic language identification of online videos. - A. Seza Dogruöz; Natalia Ponomareva 001; Sertan Girgin; Reshu Jain; Christoph Oehler (ICMI, 2017)

TF Boosted Trees: A Scalable TensorFlow Based Framework for Gradient Boosting. - Natalia Ponomareva 001; Soroush Radpour; Gilbert Hendry; Salem Haykal; Thomas Colthurst; Petr Mitrichev; Alexander Grushetsky (ECML/PKDD (3), 2017)

Agent Prioritization for Autonomous Navigation. - Khaled S. Refaat; Kai Ding; Natalia Ponomareva 001; Stéphane Ross (IROS, 2019)

Accelerating Gradient Boosting Machine. - Haihao Lu; Sai Praneeth Karimireddy; Natalia Ponomareva 001; Vahab S. Mirrokni (CoRR, 2019)

Accelerating Gradient Boosting Machines. - Haihao Lu; Sai Praneeth Karimireddy; Natalia Ponomareva 001; Vahab S. Mirrokni (AISTATS, 2020)

The Tree Ensemble Layer: Differentiability meets Conditional Computation. - Hussein Hazimeh 001; Natalia Ponomareva 001; Petros Mol; Zhenyu Tan; Rahul Mazumder (ICML, 2020)

Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data. - Qi Zhu 008; Natalia Ponomareva 001; Jiawei Han 001; Bryan Perozzi (NeurIPS, 2021)

Training Text-to-Text Transformers with Privacy Guarantees. - Natalia Ponomareva 001; Jasmijn Bastings; Sergei Vassilvitskii (ACL (Findings), 2022)

Newer is Not Always Better: Rethinking Transferability Metrics, Their Peculiarities, Stability and Performance. - Shibal Ibrahim; Natalia Ponomareva 001; Rahul Mazumder (ECML/PKDD (1), 2022)

Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets. - Hussein Hazimeh 001; Natalia Ponomareva 001 (AISTATS, 2023)

Fast as CHITA: Neural Network Pruning with Combinatorial Optimization. - Riade Benbaki; Wenyu Chen 003; Xiang Meng 004; Hussein Hazimeh 001; Natalia Ponomareva 001; Zhe Zhao 001; Rahul Mazumder (ICML, 2023)

COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search. - Shibal Ibrahim; Wenyu Chen 003; Hussein Hazimeh 001; Natalia Ponomareva 001; Zhe Zhao 001; Rahul Mazumder (KDD, 2023)

How to DP-fy ML: A Practical Tutorial to Machine Learning with Differential Privacy. - Natalia Ponomareva 001; Sergei Vassilvitskii; Zheng Xu 002; Brendan McMahan; Alexey Kurakin; Chiyaun Zhang (KDD, 2023)

Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models. - Aldo Gael Carranza; Rezsa Farahani; Natalia Ponomareva 001; Alex Kurakin; Matthew Jagielski; Milad Nasr (CoRR, 2023)

Harnessing large-language models to generate private synthetic text. - Alexey Kurakin; Natalia Ponomareva 001; Umar Syed; Liam MacDermed; Andreas Terzis (CoRR, 2023)

Explaining and Adapting Graph Conditional Shift. - Qi Zhu 008; Yizhu Jiao; Natalia Ponomareva 001; Jiawei Han 001; Bryan Perozzi (CoRR, 2023)

How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy. - Natalia Ponomareva 001; Hussein Hazimeh 001; Alex Kurakin; Zheng Xu 002; Carson Denison; H. Brendan McMahan; Sergei Vassilvitskii; Steve Chien; Abhradeep Guha Thakurta (J. Artif. Intell. Res., 2023)

Private prediction for large-scale synthetic text generation. - Kareem Amin 002; Alex Bie; Weiwei Kong; Alexey Kurakin; Natalia Ponomareva 001; Umar Syed; Andreas Terzis; Sergei Vassilvitskii (EMNLP (Findings), 2024)

OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization. - Xiang Meng 004; Shibal Ibrahim; Kayhan Behdin; Hussein Hazimeh 001; Natalia Ponomareva 001; Rahul Mazumder (ICML, 2024)

Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models. - Aldo G. Carranza; Rezsa Farahani; Natalia Ponomareva 001; Alexey Kurakin; Matthew Jagielski; Milad Nasr (NAACL-HLT, 2024)

Scaling Laws for Downstream Task Performance of Large Language Models. - Berivan Isik; Natalia Ponomareva 001; Hussein Hazimeh 001; Dimitris Paparas; Sergei Vassilvitskii; Sanmi Koyejo (CoRR, 2024)

A unified framework for Sparse plus Low-Rank Matrix Decomposition for LLMs. - Mehdi Makni; Kayhan Behdin; Zheng Xu 002; Natalia Ponomareva 001; Rahul Mazumder (CPAL, 2025)

Scaling Laws for Downstream Task Performance in Machine Translation. - Berivan Isik; Natalia Ponomareva 001; Hussein Hazimeh 001; Dimitris Paparas; Sergei Vassilvitskii; Sanmi Koyejo (ICLR, 2025)

DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation. - Yunjuan Wang; Hussein Hazimeh 001; Natalia Ponomareva 001; Alexey Kurakin; Ibrahim Hammoud; Raman Arora (SaTML, 2025)

HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs. - Mehdi Makni; Kayhan Behdin; Zheng Xu 002; Natalia Ponomareva 001; Rahul Mazumder (CoRR, 2025)

An Optimization Framework for Differentially Private Sparse Fine-Tuning. - Mehdi Makni; Kayhan Behdin; Gabriel Afriat; Zheng Xu 002; Sergei Vassilvitskii; Natalia Ponomareva 001; Hussein Hazimeh 001; Rahul Mazumder (CoRR, 2025)

Clustering and Median Aggregation Improve Differentially Private Inference. - Kareem Amin 002; Salman Avestimehr; Sara Babakniya; Alex Bie; Weiwei Kong; Natalia Ponomareva 001; Umar Syed (CoRR, 2025)