147 citations · 696 across the 57 of their papers we have counts for
9 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…
GeRA: Label-Efficient Geometrically Regularized Alignment
Dustin Klebe, Tal Shnitzer, Mikhail Yurochkin +2
Pretrained unimodal encoders incorporate rich semantic information into embedding space structures. To be similarly informative, multi-modal encoders typically require massive amou…
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…
Risk Aware Benchmarking of Large Language Models
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti +6
We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical…
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…
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
Zahra Ashktorab, Benjamin Hoover, Mayank Agarwal +4
Mitigating algorithmic bias is a critical task in the development and deployment of machine learning models. While several toolkits exist to aid machine learning practitioners in a…
Simple Disentanglement of Style and Content in Visual Representations
Lilian Ngweta, Subha Maity, Alex Gittens +2
Learning visual representations with interpretable features, i.e., disentangled representations, remains a challenging problem. Existing methods demonstrate some success but are ha…
Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
Songkai Xue, Yuekai Sun, Mikhail Yurochkin
We consider the task of training machine learning models with data-dependent constraints. Such constraints often arise as empirical versions of expected value constraints that enfo…