Utility Proportional Optimization Flow Control for Overlay Multicast. - Ali Jafari; Hosein Shafiei; Baharan Mirzasoleiman; Ghodrat Sepidnam (ISPA, 2009)

Reuse-Attack Mitigation in Wireless Sensor Networks. - Hosein Shafiei; Ahmad Khonsari; Baharan Mirzasoleiman; Mohamed Ould-Khaoua (ICC, 2011)

Distributed Submodular Maximization: Identifying Representative Elements in Massive Data. - Baharan Mirzasoleiman; Amin Karbasi; Rik Sarkar; Andreas Krause 001 (NIPS, 2013)

Revenue maximization in social networks through discounting. - Mahmoudreza Babaei; Baharan Mirzasoleiman; Mahdi Jalili; Mohammad Ali Safari (Soc. Netw. Anal. Min., 2013)

Streaming submodular maximization: massive data summarization on the fly. - Ashwinkumar Badanidiyuru; Baharan Mirzasoleiman; Amin Karbasi; Andreas Krause 001 (KDD, 2014)

Lazier Than Lazy Greedy. - Baharan Mirzasoleiman; Ashwinkumar Badanidiyuru; Amin Karbasi; Jan Vondrák; Andreas Krause 001 (AAAI, 2015)

Distributed Submodular Cover: Succinctly Summarizing Massive Data. - Baharan Mirzasoleiman; Amin Karbasi; Ashwinkumar Badanidiyuru; Andreas Krause 001 (NIPS, 2015)

Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization. - Eric Balkanski; Baharan Mirzasoleiman; Andreas Krause 001; Yaron Singer (ICML, 2016)

Fast Constrained Submodular Maximization: Personalized Data Summarization. - Baharan Mirzasoleiman; Ashwinkumar Badanidiyuru; Amin Karbasi (ICML, 2016)

Fast Distributed Submodular Cover: Public-Private Data Summarization. - Baharan Mirzasoleiman; Morteza Zadimoghaddam; Amin Karbasi (NIPS, 2016)

Distributed Submodular Maximization. - Baharan Mirzasoleiman; Amin Karbasi; Rik Sarkar; Andreas Krause 001 (J. Mach. Learn. Res., 2016)

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains. - Andrew An Bian; Baharan Mirzasoleiman; Joachim M. Buhmann; Andreas Krause 001 (AISTATS, 2017)

Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten". - Baharan Mirzasoleiman; Amin Karbasi; Andreas Krause 001 (ICML, 2017)

Big Data Summarization Using Submodular Functions. - Baharan Mirzasoleiman (2017)

Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly. - Baharan Mirzasoleiman; Stefanie Jegelka; Andreas Krause 001 (AAAI, 2018)

Dynamic Network Model from Partial Observations. - Elahe Ghalebi; Baharan Mirzasoleiman; Radu Grosu; Jure Leskovec (NeurIPS, 2018)

Data Sampling for Graph Based Unsupervised Learning: Convex and Greedy Optimization. - Saeed Vahidian; Alexander Cloninger; Baharan Mirzasoleiman (CoRR, 2019)

Data Sketching for Faster Training of Machine Learning Models. - Baharan Mirzasoleiman; Jeff A. Bilmes; Jure Leskovec (CoRR, 2019)

Selection via Proxy: Efficient Data Selection for Deep Learning. - Cody Coleman; Christopher Yeh; Stephen Mussmann; Baharan Mirzasoleiman; Peter Bailis; Percy Liang; Jure Leskovec; Matei Zaharia (ICLR, 2020)

Coresets for Data-efficient Training of Machine Learning Models. - Baharan Mirzasoleiman; Jeff A. Bilmes; Jure Leskovec (ICML, 2020)

Coresets for Robust Training of Deep Neural Networks against Noisy Labels. - Baharan Mirzasoleiman; Kaidi Cao; Jure Leskovec (NeurIPS, 2020)

Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples. - Saeed Vahidian; Baharan Mirzasoleiman; Alexander Cloninger (UAI, 2020)

Coresets for Robust Training of Neural Networks against Noisy Labels. - Baharan Mirzasoleiman; Kaidi Cao; Jure Leskovec (CoRR, 2020)

GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning. - KrishnaTeja Killamsetty; Durga Sivasubramanian; Baharan Mirzasoleiman; Ganesh Ramakrishnan; Abir De; Rishabh K. Iyer (CoRR, 2021)

Analytical Models for Motifs in Temporal Networks: Discovering Trends and Anomalies. - Alexandra M. Porter; Baharan Mirzasoleiman; Jure Leskovec (CoRR, 2021)

CrossWalk: Fairness-Enhanced Node Representation Learning. - Ahmad Khajehnejad; Moein Khajehnejad; Mahmoudreza Babaei; Krishna P. Gummadi; Adrian Weller; Baharan Mirzasoleiman (AAAI, 2022)

Passive Monitoring of Physiological Precursors of Stress Leveraging Smartwatch Data. - Shayan Fazeli; Lionel M. Levine; Mehrab Beikzadeh; Baharan Mirzasoleiman; Bita Zadeh; Tara Peris; Majid Sarrafzadeh (BIBM, 2022)

On the Fairness of Time-Critical Influence Maximization in Social Networks (Extended Abstract). - Junaid Ali 001; Mahmoudreza Babaei; Abhijnan Chakraborty; Baharan Mirzasoleiman; Krishna P. Gummadi; Adish Singla (ICDE, 2022)

Adaptive Second Order Coresets for Data-efficient Machine Learning. - Omead Pooladzandi; David Davini; Baharan Mirzasoleiman (ICML, 2022)

Investigating Why Contrastive Learning Benefits Robustness against Label Noise. - Yihao Xue; Kyle Whitecross; Baharan Mirzasoleiman (ICML, 2022)

Not All Poisons are Created Equal: Robust Training against Data Poisoning. - Yu Yang 007; Tian Yu Liu; Baharan Mirzasoleiman (ICML, 2022)

Data-Efficient Augmentation for Training Neural Networks. - Tian Yu Liu; Baharan Mirzasoleiman (NeurIPS, 2022)

Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack. - Tian Yu Liu; Yu Yang 007; Baharan Mirzasoleiman (NeurIPS, 2022)

Analytical Models for Motifs in Temporal Networks. - Alexandra M. Porter; Baharan Mirzasoleiman; Jure Leskovec (WWW (Companion Volume), 2022)

Superior generalization of smaller models in the presence of significant label noise. - Yihao Xue; Kyle Whitecross; Baharan Mirzasoleiman (CoRR, 2022)

Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks. - Tian Yu Liu; Yu Yang 007; Baharan Mirzasoleiman (CoRR, 2022)

High Probability Bounds for Stochastic Continuous Submodular Maximization. - Evan Becker; Jingdong Gao; Ted Zadouri; Baharan Mirzasoleiman (AISTATS, 2023)

NeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training. - Neha Prakriya; Yu Yang 007; Baharan Mirzasoleiman; Cho-Jui Hsieh; Jason Cong (HotStorage, 2023)

A Self-supervised Framework for Improved Data-Driven Monitoring of Stress via Multi-Modal Passive Sensing. - Shayan Fazeli; Lionel M. Levine; Mehrab Beikzadeh; Baharan Mirzasoleiman; Bita Zadeh; Tara Peris; Majid Sarrafzadeh (ICDH, 2023)

Towards Sustainable Learning: Coresets for Data-efficient Deep Learning. - Yu Yang 007; Hao Kang; Baharan Mirzasoleiman (ICML, 2023)

Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning. - Yu Yang 007; Besmira Nushi; Hamid Palangi; Baharan Mirzasoleiman (ICML, 2023)

Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least. - Siddharth Joshi 004; Baharan Mirzasoleiman (ICML, 2023)

Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression. - Yihao Xue; Siddharth Joshi 004; Eric Gan; Pin-Yu Chen; Baharan Mirzasoleiman (ICML, 2023)

Robust Learning with Progressive Data Expansion Against Spurious Correlation. - Yihe Deng; Yu Yang 007; Baharan Mirzasoleiman; Quanquan Gu (NeurIPS, 2023)

Robust Contrastive Language-Image Pretraining against Data Poisoning and Backdoor Attacks. - Wenhan Yang; Jingdong Gao; Baharan Mirzasoleiman (NeurIPS, 2023)

Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization. - Omead Pooladzandi; Pasha Khosravi; Erik Nijkamp; Baharan Mirzasoleiman (CoRR, 2023)

Data-Efficient Contrastive Self-supervised Learning: Easy Examples Contribute the Most. - Siddharth Joshi 004; Baharan Mirzasoleiman (CoRR, 2023)

Contrastive Learning under Heterophily. - Wenhan Yang; Baharan Mirzasoleiman (CoRR, 2023)

Robust Contrastive Language-Image Pretraining against Adversarial Attacks. - Wenhan Yang; Baharan Mirzasoleiman (CoRR, 2023)

Eliminating Spurious Correlations from Pre-trained Models via Data Mixing. - Yihao Xue; Ali Payani; Yu Yang 007; Baharan Mirzasoleiman (CoRR, 2023)

Towards Mitigating Spurious Correlations in the Wild: A Benchmark & a more Realistic Dataset. - Siddharth Joshi 004; Yu Yang 007; Yihao Xue; Wenhan Yang; Baharan Mirzasoleiman (CoRR, 2023)

Ordering for Non-Replacement SGD. - Yuetong Xu; Baharan Mirzasoleiman (CoRR, 2023)

Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent. - Lauren Watson; Eric Gan; Mohan Dantam; Baharan Mirzasoleiman; Rik Sarkar (CoRR, 2023)

On the Fairness of Time-Critical Influence Maximization in Social Networks. - Junaid Ali 001; Mahmoudreza Babaei; Abhijnan Chakraborty; Baharan Mirzasoleiman; Krishna P. Gummadi; Adish Singla (IEEE Trans. Knowl. Data Eng., 2023)

Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. - Yu Yang 007; Eric Gan; Gintare Karolina Dziugaite; Baharan Mirzasoleiman (AISTATS, 2024)

Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity. - Siddharth Joshi 004; Arnav Jain; Ali Payani; Baharan Mirzasoleiman (AISTATS, 2024)

Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality. - Xuxi Chen; Yu Yang 007; Zhangyang Wang; Baharan Mirzasoleiman (ICLR, 2024)

Investigating the Benefits of Projection Head for Representation Learning. - Yihao Xue; Eric Gan; Jiayi Ni; Siddharth Joshi 004; Baharan Mirzasoleiman (ICLR, 2024)

Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift. - Yihao Xue; Siddharth Joshi 004; Dang Nguyen; Baharan Mirzasoleiman (ICLR, 2024)

NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction. - Haofan Lu; Christopher Vattheuer; Baharan Mirzasoleiman; Omid Abari (ICML, 2024)

Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings. - Yihao Xue; Ali Payani; Yu Yang 007; Baharan Mirzasoleiman (ICML, 2024)

Better Safe than Sorry: Pre-training CLIP against Targeted Data Poisoning and Backdoor Attacks. - Wenhan Yang; Jingdong Gao; Baharan Mirzasoleiman (ICML, 2024)

SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models. - Yu Yang 007; Siddhartha Mishra; Jeffrey N. Chiang; Baharan Mirzasoleiman (NeurIPS, 2024)

Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization. - Dang Nguyen; Paymon Haddad; Eric Gan; Baharan Mirzasoleiman (NeurIPS, 2024)

Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise. - Yihao Xue; Kyle Whitecross; Baharan Mirzasoleiman (UAI, 2024)

Graph Contrastive Learning under Heterophily via Graph Filters. - Wenhan Yang; Baharan Mirzasoleiman (UAI, 2024)

A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction. - Haofan Lu; Christopher Vattheuer; Baharan Mirzasoleiman; Omid Abari (CoRR, 2024)

Make the Most of Your Data: Changing the Training Data Distribution to Improve In-distribution Generalization Performance. - Dang Nguyen; Paymon Haddad; Eric Gan; Baharan Mirzasoleiman (CoRR, 2024)

Memory-efficient Training of LLMs with Larger Mini-batches. - Dang Nguyen; Wenhan Yang; Rathul Anand; Yu Yang 007; Baharan Mirzasoleiman (CoRR, 2024)

Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity. - Dang Nguyen; Ali Payani; Baharan Mirzasoleiman (ACL (Findings), 2025)

Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-training of Deep Networks. - Siddharth Joshi 004; Jiayi Ni; Baharan Mirzasoleiman (ICLR, 2025)

Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures. - Dang Nguyen; Wenhan Yang; Rathul Anand; Yu Yang 007; Baharan Mirzasoleiman (ICLR, 2025)

MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation. - Siddharth Joshi 004; Besmira Nushi; Vidhisha Balachandran; Varun Chandrasekaran; Vibhav Vineet; Neel Joshi; Baharan Mirzasoleiman (CoRR, 2025)

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions. - Yihao Xue; Jiping Li; Baharan Mirzasoleiman (CoRR, 2025)

Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection. - Yihao Xue; Kristjan H. Greenewald; Youssef Mroueh; Baharan Mirzasoleiman (CoRR, 2025)

Synthetic Text Generation for Training Large Language Models via Gradient Matching. - Dang Nguyen; Zeman Li; MohammadHossein Bateni; Vahab Mirrokni; Meisam Razaviyayn; Baharan Mirzasoleiman (CoRR, 2025)

DD-Ranking: Rethinking the Evaluation of Dataset Distillation. - Zekai Li; Xinhao Zhong; Samir Khaki; Zhiyuan Liang; Yuhao Zhou; Mingjia Shi; Ziqiao Wang; Xuanlei Zhao; Wangbo Zhao; Ziheng Qin; Mengxuan Wu; Pengfei Zhou; et al. (CoRR, 2025)

Do We Need All the Synthetic Data? Towards Targeted Synthetic Image Augmentation via Diffusion Models. - Dang Nguyen; Jiping Li; Jinghao Zheng; Baharan Mirzasoleiman (CoRR, 2025)

Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap. - Wenhan Yang; Spencer Stice; Ali Payani; Baharan Mirzasoleiman (CoRR, 2025)

LoRA is All You Need for Safety Alignment of Reasoning LLMs. - Yihao Xue; Baharan Mirzasoleiman (CoRR, 2025)