Adam Dziedzic

Venues

Frequent coauthors

Papers

1. Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images
   AAAI 2026 ·

   Aditya Kumar

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2. Demystifying Foreground-Background Memorization in Diffusion Models
   AAAI 2026 ·

   Jimmy Z. Di

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3. On Stealing Graph Neural Network Models
   AAAI 2026 ·

   Marcin Podhajski

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4. BitMark: Watermarking Bitwise Autoregressive Image Generative Models
   NeurIPS 2025 ·

   Louis Kerner

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5. CDI: Copyrighted Data Identification in Diffusion Models
   CVPR 2025 ·

   Jan Dubinski

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6. Captured by Captions: On Memorization and its Mitigation in CLIP Models
   ICLR 2025 ·

   Wenhao Wang

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7. Differentially Private Federated Learning with Time-Adaptive Privacy Spending
   ICLR 2025 ·

   Shahrzad Kiani

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8. Differentially Private Prototypes for Imbalanced Transfer Learning
   AAAI 2025 ·

   Dariush Wahdany

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9. Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
   ICML 2025 ·

   Xun Wang

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10. Exploring the limits of strong membership inference attacks on large language models
    NeurIPS 2025 ·

    Jamie Hayes

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11. Memorization in Graph Neural Networks
    NeurIPS 2025 ·

    Adarsh Jamadandi

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12. Precise Parameter Localization for Textual Generation in Diffusion Models
    ICLR 2025 ·

    Lukasz Staniszewski

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13. Privacy Attacks on Image AutoRegressive Models
    ICML 2025 ·

    Antoni Kowalczuk

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14. Unlocking Post-hoc Dataset Inference with Synthetic Data
    ICML 2025 ·

    Bihe Zhao

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15. Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
    ECAI 2024 ·

    Marcin Podhajski

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16. Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
    NeurIPS 2024 ·

    Dominik Hintersdorf

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17. LLM Dataset Inference: Did you train on my dataset?
    NeurIPS 2024 ·

    Pratyush Maini

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18. Localizing Memorization in SSL Vision Encoders
    NeurIPS 2024 ·

    Wenhao Wang

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19. Memorization in Self-Supervised Learning Improves Downstream Generalization
    ICLR 2024 ·

    Wenhao Wang

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20. Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
    NeurIPS 2024 ·

    Vincent Hanke

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