11 citations · 21 across the 6 of their papers we have counts for
9 papers
Approximate Conditional Coverage & Calibration via Neural Model Approximations
Allen Schmaltz, Danielle Rasooly
A typical desideratum for quantifying the uncertainty from a classification model as a prediction set is class-conditional singleton set calibration. That is, such sets should map…
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
Allen Schmaltz, Andrew Beam
We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-…
Exemplar Auditing for Multi-Label Biomedical Text Classification
Allen Schmaltz, Andrew Beam
Many practical applications of AI in medicine consist of semi-supervised discovery: The investigator aims to identify features of interest at a resolution more fine-grained than th…
Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition
Allen Schmaltz
We propose a new, more actionable view of neural network interpretability and data analysis by leveraging the remarkable matching effectiveness of representations derived from deep…
Ecological Regression with Partial Identification
Wenxin Jiang, Gary King, Allen Schmaltz +1
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We study a partially identified linear contextual effects model for EI and descr…
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Andrew L. Beam, Benjamin Kompa, Allen Schmaltz +6
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of…
Adapting Sequence Models for Sentence Correction
Allen Schmaltz, Yoon Kim, Alexander M. Rush +1
In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based…
Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
Allen Schmaltz, Yoon Kim, Alexander M. Rush +1
We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AES…
Word Ordering Without Syntax
Allen Schmaltz, Alexander M. Rush, Stuart M. Shieber
Recent work on word ordering has argued that syntactic structure is important, or even required, for effectively recovering the order of a sentence. We find that, in fact, an n-gra…