49 citations · 70 across the 12 of their papers we have counts for
12 papers
Semantic Alignment of AI Models: Concept Collapse, Checkpoint Dynamics, and Cross-Lingual Transfer
Tyler Ashoff, Jordan Rodu
Language model benchmarking is a difficult task. Outcome reasoning alone does not test the model's conceptualization of language and popular open-source benchmarks are quickly satu…
Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization
Tyler Ashoff, Jordan Rodu
Modern opaque AI models prize performance over interpretability, which makes testing difficult. However, formal statistical tests conducted on a model's embedding space can provide…
Synthetic Data, Information, and Prior Knowledge: Why Synthetic Data Augmentation to Boost Sample Doesn't Work for Statistical Inference
Reid Dale, Jordan Rodu, Mike Baiocchi
The use of synthetic data to deidentify data and to improve predictive models is well-attested to. The augmentation of datasets using synthetically generated data is an alluring pr…
Data Gluttony: Epistemic Risks, Dependent Testing and Data Reuse in Large Datasets
Reid Dale, Jordan Rodu, Maria E. Currie +1
Large-scale registries have collected vast amounts of data which has enabled investigators to efficiently conduct studies of observational data. Common practice is for investigator…
Nonlinear Permuted Granger Causality
Noah D. Gade, Jordan Rodu
Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience. The original definition addresses the notion of causality in…
Change Point Detection with Conceptors
Noah D. Gade, Jordan Rodu
Offline change point detection retrospectively locates change points in a time series. Many nonparametric methods that target i.i.d. mean and variance changes fail in the presence…
Bridging the Usability Gap: Theoretical and Methodological Advances for Spectral Learning of Hidden Markov Models
Xiaoyuan Ma, Jordan Rodu
The Baum-Welch (B-W) algorithm is the most widely accepted method for inferring hidden Markov models (HMM). However, it is prone to getting stuck in local optima, and can be too sl…
Trees in transformers: a theoretical analysis of the Transformer's ability to represent trees
Qi He, João Sedoc, Jordan Rodu
Transformer networks are the de facto standard architecture in natural language processing. To date, there are no theoretical analyses of the Transformer's ability to capture tree…
When black box algorithms are (not) appropriate: a principled prediction-problem ontology
Jordan Rodu, Michael Baiocchi
In the 1980s a new, extraordinarily productive way of reasoning about algorithms emerged. In this paper, we introduce the term "outcome reasoning" to refer to this form of reasonin…
Locating recombination hot spots in genomic sequences through the singular value decomposition
Jordan Rodu, Shane T. Jensen
Locating recombination hotspots in genomic data is an important but difficult task. Current methods frequently rely on estimating complicated models at high computational cost. In…
Two Step CCA: A new spectral method for estimating vector models of words
Paramveer Dhillon, Jordan Rodu, Dean Foster +1
Unlabeled data is often used to learn representations which can be used to supplement baseline features in a supervised learner. For example, for text applications where the words…
Spectral dimensionality reduction for HMMs
Dean P. Foster, Jordan Rodu, Lyle H. Ungar
Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the u…