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07/12/2023
What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation
The pretrain-finetune paradigm usually improves downstream performance o...
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05/31/2023
Diffused Redundancy in Pre-trained Representations
Representations learned by pre-training a neural network on a large data...
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05/30/2023
Pointwise Representational Similarity
With the increasing reliance on deep neural networks, it is important to...
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06/23/2022
Measuring Representational Robustness of Neural Networks Through Shared Invariances
A major challenge in studying robustness in deep learning is defining th...
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01/16/2022
Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual
Online bipartite-matching platforms are ubiquitous and find applications...
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11/29/2021
Exploring Alignment of Representations with Human Perception
We argue that a valuable perspective on when a model learns good represe...
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02/12/2021
Technical Challenges for Training Fair Neural Networks
As machine learning algorithms have been widely deployed across applicat...
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07/01/2020
Unifying Model Explainability and Robustness via Machine-Checkable Concepts
As deep neural networks (DNNs) get adopted in an ever-increasing number ...
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06/17/2020
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
Deep neural networks are being increasingly used in real world applicati...
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12/18/2019
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Rideshare platforms, when assigning requests to drivers, tend to maximiz...
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03/04/2019
