21 citations · 45 across the 11 of their papers we have counts for
11 papers
Kan Extensions in Data Science and Machine Learning
Dan Shiebler
A common problem in data science is "use this function defined over this small set to generate predictions over that larger set." Extrapolation, interpolation, statistical inferenc…
Generalized Optimization: A First Step Towards Category Theoretic Learning Theory
Dan Shiebler
The Cartesian reverse derivative is a categorical generalization of reverse-mode automatic differentiation. We use this operator to generalize several optimization algorithms, incl…
Category Theory in Machine Learning
Dan Shiebler, Bruno Gavranović, Paul Wilson
Over the past two decades machine learning has permeated almost every realm of technology. At the same time, many researchers have begun using category theory as a unifying languag…
Lessons Learned Addressing Dataset Bias in Model-Based Candidate Generation at Twitter
Alim Virani, Jay Baxter, Dan Shiebler +7
Traditionally, heuristic methods are used to generate candidates for large scale recommender systems. Model-based candidate generation promises multiple potential advantages, prima…
Flattening Multiparameter Hierarchical Clustering Functors
Dan Shiebler
We bring together topological data analysis, applied category theory, and machine learning to study multiparameter hierarchical clustering. We begin by introducing a procedure for…
Functorial Manifold Learning
Dan Shiebler
We adapt previous research on category theory and topological unsupervised learning to develop a functorial perspective on manifold learning, also known as nonlinear dimensionality…
Tuning Word2vec for Large Scale Recommendation Systems
Benjamin P. Chamberlain, Emanuele Rossi, Dan Shiebler +2
Word2vec is a powerful machine learning tool that emerged from Natural Lan-guage Processing (NLP) and is now applied in multiple domains, including recom-mender systems, forecastin…
Categorical Stochastic Processes and Likelihood
Dan Shiebler
In this work we take a Category Theoretic perspective on the relationship between probabilistic modeling and function approximation. We begin by defining two extensions of function…
Incremental Monoidal Grammars
Dan Shiebler, Alexis Toumi, Mehrnoosh Sadrzadeh
In this work we define formal grammars in terms of free monoidal categories, along with a functor from the category of formal grammars to the category of automata. Generalising fro…
Fighting Redundancy and Model Decay with Embeddings
Dan Shiebler, Luca Belli, Jay Baxter +2
Every day, hundreds of millions of new Tweets containing over 40 languages of ever-shifting vernacular flow through Twitter. Models that attempt to extract insight from this fireho…
A Correlation Maximization Approach for Cross Domain Co-Embeddings
Dan Shiebler
Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit.…