TY - CONF
TI - Differentiable Causal Computations via Delayed Trace
AU - Sprunger, David
AU - Katsumata, Shin-ya
T2 - 2019 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS)
AB - We investigate causal computations taking sequences of inputs to sequences of outputs where the nth output depends on the ﬁrst n inputs only. We model these in category theory via a construction taking a Cartesian category C to another category St(C) with a novel trace-like operation called “delayed trace”, which misses yanking and dinaturality axioms of the usual trace. The delayed trace operation provides a feedback mechanism in St(C) with an implicit guardedness guarantee.
C1 - Vancouver, BC, Canada
C3 - 2019 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS)
DA - 2019/06//
PY - 2019
DO - 10/ggdf98
DP - Crossref
SP - 1
EP - 12
LA - en
PB - IEEE
SN - 978-1-72813-608-0
UR - https://ieeexplore.ieee.org/document/8785670/
Y2 - 2019/11/23/16:57:38
KW - Categorical ML
KW - Differentiation
ER -
TY - CONF
TI - Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training
AU - Izbicki, Michael
AB - We use abstract algebra to derive new algorithms for fast cross-validation, online learning, and parallel learning. To use these algorithms on a classification model, we must show that the model has appropriate algebraic structure. It is easy to give algebraic structure to some models, and we do this explicitly for Bayesian classifiers and a novel variation of decision stumps called HomStumps. But not all classifiers have an obvious structure, so we introduce the Free HomTrainer. This can be used to give a "generic" algebraic structure to any classifier. We use the Free HomTrainer to give algebraic structure to bagging and boosting. In so doing, we derive novel online and parallel algorithms, and present the first fast cross-validation schemes for these classifiers.
C3 - ICML
DA - 2013///
PY - 2013
DP - Semantic Scholar
ST - Algebraic classifiers
KW - Algebra
KW - Categorical ML
KW - Machine learning
ER -