Adrian Weller

Blind Justice: Fairness with Encrypted Sensitive Attributes

2018 · arXiv (Cornell University)

Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive …

CWY Parametrization for Scalable Learning of Orthogonal and Stiefel Matrices

2020 · arXiv (Cornell University)

We introduce an efficient approach for optimization over orthogonal groups on highly parallel computation units such as GPUs or TPUs. As in earlier work, we parametrize an orthogonal matrix as a product of Householder reflections. …

Rethinking Attention with Performers

2020 · arXiv (Cornell University)

We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity …

Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off

2022 · arXiv (Cornell University)

Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy. Concept bottleneck models promote trustworthiness by conditioning classification tasks on an intermediate level of human-like concepts. This enables human interventions …

In-Context In-Context Learning with Transformer Neural Processes

2024 · arXiv (Cornell University)

Neural processes (NPs) are a powerful family of meta-learning models that seek to approximate the posterior predictive map of the ground-truth stochastic process from which each dataset in a meta-dataset is sampled. There are many …