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Found 11 papers, 5 papers with code
Towards Reliable Latent Knowledge Estimation in LLMs: In-Context Learning vs. Prompting Based Factual Knowledge Extraction
We propose an approach for estimating the latent knowledge embedded inside large language models (LLMs).
What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation
The pretrain-finetune paradigm usually improves downstream performance over training a model from scratch on the same task, becoming commonplace across many areas of machine learning.
Pointwise Representational Similarity
Concretely, we show how PNKA can be leveraged to develop a deeper understanding of (a) the input examples that are likely to be misclassified, (b) the concepts encoded by (individual) neurons in a layer, and (c) the effects of fairness interventions on learned representations.
Measuring Representational Robustness of Neural Networks Through Shared Invariances
Our work offers a new view on robustness by using another reference NN to define the set of perturbations a given NN should be invariant to, thus generalizing the reliance on a reference ``human NN'' to any NN.
Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual
Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator's profit.
Do Invariances in Deep Neural Networks Align with Human Perception?
One necessary criterion for a network's invariances to align with human perception is for its IRIs look 'similar' to humans.
Technical Challenges for Training Fair Neural Networks
As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging).
Unifying Model Explainability and Robustness via Machine-Checkable Concepts
Our framework defines a large number of concepts that the DNN explanations could be based on and performs the explanation-conformity check at test time to assess prediction robustness.
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks.
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e. g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver.
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population.
