| 2026 | AAAI | Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation. | Haoyu Lei, Kaiwen Zhou, Yinchuan Li, Zhitang Chen, Farzan Farnia |
| 2026 | DATE | FastRW: An Efficient Random Walk Method for Steady-State Thermal Analysis. | Zixiao Wang, Tianshu Hou, Chenghan Wang, Zhen Zhuang, Tsung-Yi Ho, Farzan Farnia, Bei Yu |
| 2026 | DATE | DiffResist: Physics-Constrained Diffusion for Photoresist Modeling. | Zixiao Wang, Jieya Zhou, Xinyun Zhang, Shoubo Hu, Farzan Farnia, Bei Yu |
| 2026 | ISIT | On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations. | Haoyu Lei, Mohammad Jalali, Chin Wa Lau, Farzan Farnia |
| 2026 | ISIT | The Maximum von Neumann Entropy Principle: Theory and Applications in Machine Learning. | Youqi Wu, Farzan Farnia |
| 2025 | AISTATS | A Multi-Armed Bandit Approach to Online Selection and Evaluation of Generative Models. | Xiaoyan Hu, Ho-fung Leung, Farzan Farnia |
| 2025 | AISTATS | Gaussian Smoothing in Saliency Maps: The Stability-Fidelity Trade-Off in Neural Network Interpretability. | Zhuorui Ye, Farzan Farnia |
| 2025 | CVPR | Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach. | Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li, Farzan Farnia |
| 2025 | ICCV | An Information-Theoretic Approach to Diversity Evaluation of Prompt-Based Generative Models. | Mohammad Jalali, Azim Ospanov, Amin Gohari, Farzan Farnia |
| 2025 | ICCV | Scendi Score: Prompt-Aware Diversity Evaluation Via Schur Complement of Clip Embeddings. | Azim Ospanov, Mohammad Jalali, Farzan Farnia |
| 2025 | ICCV | On the Distributed Evaluation of Generative Models. | Zixiao Wang, Farzan Farnia, Zhenghao Lin, Yunheng Shen, Bei Yu |
| 2025 | ICLR | Boosting the visual interpretability of CLIP via adversarial fine-tuning. | Shizhan Gong, Haoyu Lei, Qi Dou, Farzan Farnia |
| 2025 | ICLR | Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms. | Parham Rezaei, Farzan Farnia, Cheuk Ting Li |
| 2025 | ICML | Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts. | Amir Najafi, Samin Mahdizadeh Sani, Farzan Farnia |
| 2025 | ICML | PAK-UCB Contextual Bandit: An Online Learning Approach to Prompt-Aware Selection of Generative Models and LLMs. | Xiaoyan Hu, Ho-fung Leung, Farzan Farnia |
| 2025 | ICML | Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models. | Shizhan Gong, Yankai Jiang, Qi Dou, Farzan Farnia |
| 2025 | ICML | Towards an Explainable Comparison and Alignment of Feature Embeddings. | Mohammad Jalali, Bahar Dibaei Nia, Farzan Farnia |
| 2025 | ICML | Multilayer Matrix Factorization via Dimension-Reducing Diffusion Variational Inference. | Junbin Liu, Farzan Farnia, Wing-Kin Ma |
| 2025 | UAI | Do Vendi Scores Converge with Finite Samples? Truncated Vendi Score for Finite-Sample Convergence Guarantees. | Azim Ospanov, Farzan Farnia |
| 2024 | ACCV | Sparse Domain Transfer via Elastic Net Regularization. | Jingwei Zhang, Farzan Farnia |
| 2024 | AISTATS | On Convergence in Wasserstein Distance and f-divergence Minimization Problems. | Cheuk Ting Li, Jingwei Zhang, Farzan Farnia |
| 2024 | BMVC | A Super-pixel-based Approach to the Stable Interpretation of Neural Networks. | Shizhan Gong, Jingwei Zhang, Qi Dou, Farzan Farnia |
| 2024 | CVPR | Structured Gradient-Based Interpretations via Norm-Regularized Adversarial Training. | Shizhan Gong, Qi Dou, Farzan Farnia |
| 2024 | DAC | ChatPattern: Layout Pattern Customization via Natural Language. | Zixiao Wang, Yunheng Shen, Xufeng Yao, Wenqian Zhao, Yang Bai, Farzan Farnia, Bei Yu |
| 2024 | ICLR | Provably Efficient CVaR RL in Low-rank MDPs. | Yulai Zhao, Wenhao Zhan, Xiaoyan Hu, Ho-fung Leung, Farzan Farnia, Wen Sun, Jason D. Lee |
| 2024 | ICML | An Information Theoretic Approach to Interaction-Grounded Learning. | Xiaoyan Hu, Farzan Farnia, Ho-fung Leung |
| 2024 | ICML | An Interpretable Evaluation of Entropy-based Novelty of Generative Models. | Jingwei Zhang, Cheuk Ting Li, Farzan Farnia |
| 2024 | UAI | On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms. | Haoyu Lei, Amin Gohari, Farzan Farnia |
| 2023 | AISTATS | Mode-Seeking Divergences: Theory and Applications to GANs. | Cheuk Ting Li, Farzan Farnia |
| 2023 | DAC | DiffPattern: Layout Pattern Generation via Discrete Diffusion. | Zixiao Wang, Yunheng Shen, Wenqian Zhao, Yang Bai, Guojin Chen, Farzan Farnia, Bei Yu |
| 2023 | ICASSP | Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations. | Haniyeh Ehsani Oskouie, Farzan Farnia |
| 2023 | ICCV | MoreauGrad: Sparse and Robust Interpretation of Neural Networks via Moreau Envelope. | Jingwei Zhang, Farzan Farnia |
| 2023 | UAI | On the Role of Generalization in Transferability of Adversarial Examples. | Yilin Wang, Farzan Farnia |
| 2022 | ICML | On Convergence of Gradient Descent Ascent: A Tight Local Analysis. | Haochuan Li, Farzan Farnia, Subhro Das, Ali Jadbabaie |
| 2021 | ICML | A Wasserstein Minimax Framework for Mixed Linear Regression. | Theo Diamandis, Yonina C. Eldar, Alireza Fallah, Farzan Farnia, Asuman E. Ozdaglar |
| 2021 | ICML | Train simultaneously, generalize better: Stability of gradient-based minimax learners. | Farzan Farnia, Asuman E. Ozdaglar |
| 2020 | ICML | Do GANs always have Nash equilibria? | Farzan Farnia, Asuman E. Ozdaglar |
| 2019 | ICLR | Generalizable Adversarial Training via Spectral Normalization. | Farzan Farnia, Jesse M. Zhang, David Tse |
| 2015 | ISIT | Minimum HGR correlation principle: From marginals to joint distribution. | Farzan Farnia, Meisam Razaviyayn, Sreeram Kannan, David Tse |
| 2014 | ITA | On feedback in Gaussian multi-hop networks. | Farzan Farnia, Ayfer zgr |
| 2013 | WCNC | Asymptotic behavior of network capacity under spatial network coding. | Farzan Farnia, S. Jamaloddin Golestani |