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Found 12 papers, 3 papers with code
Comparing Foundation Models using Data Kernels
Further, we demonstrate how our methodology can be extended to facilitate population level model comparison.
Approximately optimal domain adaptation with Fisher's Linear Discriminant
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation.
Deep Learning with Label Noise: A Hierarchical Approach
Our hierarchical approach improves upon regular deep neural networks in learning with label noise.
Mental State Classification Using Multi-graph Features
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load.
Towards a theory of out-of-distribution learning
This assumption renders these theories inadequate for characterizing 21$^{st}$ century real world data problems, which are typically characterized by evaluation distributions that differ from the training data distributions (referred to as out-of-distribution learning).
Leveraging semantically similar queries for ranking via combining representations
In modern ranking problems, different and disparate representations of the items to be ranked are often available.
Inducing a hierarchy for multi-class classification problems
In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not.
Subgraph nomination: Query by Example Subgraph Retrieval in Networks
This paper introduces the subgraph nomination inference task, in which example subgraphs of interest are used to query a network for similarly interesting subgraphs.
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners.
Distance-based Positive and Unlabeled Learning for Ranking
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.
Omnidirectional Transfer for Quasilinear Lifelong Learning
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.
GraSPy: Graph Statistics in Python
We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations.
