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Found 11 papers, 2 papers with code
Efficient Learning with Arbitrary Covariate Shift
We give an efficient algorithm for learning a binary function in a given class C of bounded VC dimension, with training data distributed according to P and test data according to Q, where P and Q may be arbitrary distributions over X.
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Domain generalization is the problem of machine learning when the training data and the test data come from different data domains.
Actively Avoiding Nonsense in Generative Models
A generative model may generate utter nonsense when it is fit to maximize the likelihood of observed data.
Supervising Unsupervised Learning
We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets.
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
We present a multi-view Bayesian non-parametric algorithm which improves multi-sense word embeddings by (a) using multilingual (i. e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner.
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Geometrically, gender bias is first shown to be captured by a direction in the word embedding.
Quantifying and Reducing Stereotypes in Word Embeddings
Machine learning algorithms are optimized to model statistical properties of the training data.
Feature Multi-Selection among Subjective Features
When dealing with subjective, noisy, or otherwise nebulous features, the "wisdom of crowds" suggests that one may benefit from multiple judgments of the same feature on the same object.
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
In this paper, we provide algorithms for learning GLMs and SIMs, which are both computationally and statistically efficient.
Potential-Based Agnostic Boosting
We prove strong noise-tolerance properties of a potential-based boosting algorithm, similar to MadaBoost (Domingo and Watanabe, 2000) and SmoothBoost (Servedio, 2003).
Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model
Hence this natural extension to the statistical query model does not increase the set of weakly learnable functions.
