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06/14/2023
Contrastive Loss is All You Need to Recover Analogies as Parallel Lines
While static word embedding models are known to represent linguistic ana...
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09/09/2022
Improving Model Training via Self-learned Label Representations
Modern neural network architectures have shown remarkable success in sev...
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11/27/2021
An analysis of document graph construction methods for AMR summarization
Meaning Representation (AMR) is a graph-based semantic representation fo...
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11/16/2021
Solving Linear Algebra by Program Synthesis
We solve MIT's Linear Algebra 18.06 course and Columbia University's Com...
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12/04/2020
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics
Why do biased predictions arise? What interventions can prevent them? We...
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10/30/2019
Meta-Learning to Cluster
Clustering is one of the most fundamental and wide-spread techniques in ...
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10/16/2019
Model-Agnostic Meta-Learning using Runge-Kutta Methods
Meta-learning has emerged as an important framework for learning new tas...
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02/05/2019
Metric Learning on Manifolds
Recent literature has shown that symbolic data, such as text and graphs,...
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01/30/2019
Noise-tolerant fair classification
Fair machine learning concerns the analysis and design of learning algor...
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11/03/2018
Stochastic Neighbor Embedding under f-divergences
The t-distributed Stochastic Neighbor Embedding (t-SNE) is a powerful an...
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05/11/2015
Sample complexity of learning Mahalanobis distance metrics
Metric learning seeks a transformation of the feature space that enhance...
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06/27/2012
A concentration theorem for projections
X in R^D has mean zero and finite second moments. We show that there is ...
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05/09/2012