9 citations · 18 across the 23 of their papers we have counts for
5 papers · 1 filter
Weak Supervision Performance Evaluation via Partial Identification
Felipe Maia Polo, Subha Maity, Mikhail Yurochkin +2
Programmatic Weak Supervision (PWS) enables supervised model training without direct access to ground truth labels, utilizing weak labels from heuristics, crowdsourcing, or pre-tra…
An Investigation of Representation and Allocation Harms in Contrastive Learning
Subha Maity, Mayank Agarwal, Mikhail Yurochkin +1
The effect of underrepresentation on the performance of minority groups is known to be a serious problem in supervised learning settings; however, it has been underexplored so far…
Fusing Models with Complementary Expertise
Hongyi Wang, Felipe Maia Polo, Yuekai Sun +3
Training AI models that generalize across tasks and domains has long been among the open problems driving AI research. The emergence of Foundation Models made it easier to obtain e…
Large Language Model Routing with Benchmark Datasets
Tal Shnitzer, Anthony Ou, Mírian Silva +5
There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them. While some models dominate these benchmarks, no single model t…
Conditional independence testing under misspecified inductive biases
Felipe Maia Polo, Yuekai Sun, Moulinath Banerjee
Conditional independence (CI) testing is a fundamental and challenging task in modern statistics and machine learning. Many modern methods for CI testing rely on powerful supervise…