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Daksh Mittal

2 accepted papers

2025

Architectural and Inferential Inductive Biases for Exchangeable Sequence Modeling

NeurIPS 2025poster

Autoregressive models have emerged as a powerful framework for modeling exchangeable sequences---i.i.d. observations when conditioned on some latent factor---enabling direct modeling of uncertainty from missing data (rather than a latent). Motivated by the critical role posterior inference plays as…

Cited by 0SourcecodeScholar
2024

Adaptive Labeling for Efficient Out-of-distribution Model Evaluation

NeurIPS 2024poster

Datasets often suffer severe selection bias; clinical labels are only available on patients for whom doctors ordered medical exams. To assess model performance outside the support of available data, we present a computational framework for adaptive labeling, providing cost-efficient model evaluation…

Cited by 0SourcePDFScholar