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  1. 1
    Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours

    Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms during High-Demand Hours by Nanda, Vedant, Xu, Pan, Sankararaman, Karthik Abhinav, Dickerson, John, Srinivasan, Aravind

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  2. 2
    Understanding the Role of Invariance in Transfer Learning

    Understanding the Role of Invariance in Transfer Learning by Speicher, Till, Nanda, Vedant, Gummadi, Krishna P

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  3. 3
    Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual

    Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual by Esmaeili, Seyed, Duppala, Sharmila, Cheng, Davidson, Nanda, Vedant, Srinivasan, Aravind, Dickerson, John P.

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  4. 4
    Do Invariances in Deep Neural Networks Align with Human Perception?

    Do Invariances in Deep Neural Networks Align with Human Perception? by Nanda, Vedant, Majumdar, Ayan, Kolling, Camila, Dickerson, John P., Gummadi, Krishna P., Love, Bradley C., Weller, Adrian

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  5. 5
    The Impact of Inference Acceleration Strategies on Bias of LLMs

    The Impact of Inference Acceleration Strategies on Bias of LLMs by Kirsten, Elisabeth, Habernal, Ivan, Nanda, Vedant, Muhammad Bilal Zafar

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  6. 6
    What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation

    What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation by Merlin, Gabriele, Nanda, Vedant, Rawal, Ruchit, Toneva, Mariya

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  7. 7
    Diffused Redundancy in Pre-trained Representations

    Diffused Redundancy in Pre-trained Representations by Nanda, Vedant, Speicher, Till, Dickerson, John P, Feizi, Soheil, Gummadi, Krishna P, Weller, Adrian

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  8. 8
    Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications

    Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications by Speicher, Till, Khan, Mohammad Aflah, Wu, Qinyuan, Nanda, Vedant, Das, Soumi, Ghosh, Bishwamittra, Gummadi, Krishna P, Terzi, Evimaria

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  9. 9
    Lawma: The Power of Specialization for Legal Tasks

    Lawma: The Power of Specialization for Legal Tasks by Dominguez-Olmedo, Ricardo, Nanda, Vedant, Abebe, Rediet, Bechtold, Stefan, Engel, Christoph, Frankenreiter, Jens, Gummadi, Krishna, Hardt, Moritz, Livermore, Michael

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  10. 10
    Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual

    Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual by Esmaeili, Seyed A, Duppala, Sharmila, Davidson, Cheng, Nanda, Vedant, Srinivasan, Aravind, Dickerson, John P

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  11. 11
    Pointwise Representational Similarity

    Pointwise Representational Similarity by Kolling, Camila, Speicher, Till, Nanda, Vedant, Toneva, Mariya, Gummadi, Krishna P

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  12. 12
    An Empirical Analysis of Facebook's Free Basics

    An Empirical Analysis of Facebook's Free Basics by Singh, Siddharth, Nanda, Vedant, Sen, Rijurekha, Ahmad, Sohaib, Sengupta, Satadal, Phokeer, Amreesh, Farooq, Zaid Ahmed, Khan, Taslim Arefin, Kumaragaguru, Ponnurangam, Qazi, Ihsan Ayyub, Choffnes, David, Gummadi, Krishna P.

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  13. 13
    On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning

    On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning by Heidari, Hoda, Nanda, Vedant, Gummadi, Krishna P

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  14. 14
    Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction

    Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction by Wu, Qinyuan, Khan, Mohammad Aflah, Das, Soumi, Nanda, Vedant, Ghosh, Bishwamittra, Kolling, Camila, Speicher, Till, Bindschaedler, Laurent, Gummadi, Krishna P, Terzi, Evimaria

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  15. 15
    Measuring Representational Robustness of Neural Networks Through Shared Invariances

    Measuring Representational Robustness of Neural Networks Through Shared Invariances by Nanda, Vedant, Speicher, Till, Kolling, Camila, Dickerson, John P, Gummadi, Krishna P, Weller, Adrian

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  16. 16
    Do Invariances in Deep Neural Networks Align with Human Perception?

    Do Invariances in Deep Neural Networks Align with Human Perception? by Nanda, Vedant, Majumdar, Ayan, Kolling, Camila, Dickerson, John P, Gummadi, Krishna P, Love, Bradley C, Weller, Adrian

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  17. 17
    Technical Challenges for Training Fair Neural Networks

    Technical Challenges for Training Fair Neural Networks by Cherepanova, Valeriia, Nanda, Vedant, Goldblum, Micah, Dickerson, John P, Goldstein, Tom

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  18. 18
    Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning

    Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning by Nanda, Vedant, Dooley, Samuel, Singla, Sahil, Feizi, Soheil, Dickerson, John P

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  19. 19
    Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

    Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours by Nanda, Vedant, Pan, Xu, Sankararaman, Karthik Abinav, Dickerson, John P, Srinivasan, Aravind

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  20. 20
    Unifying Model Explainability and Robustness via Machine-Checkable Concepts

    Unifying Model Explainability and Robustness via Machine-Checkable Concepts by Nanda, Vedant, Speicher, Till, Dickerson, John P, Gummadi, Krishna P, Muhammad Bilal Zafar

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