16 citations · 30 across the 4 of their papers we have counts for
6 papers
Detecting Anomalies within Time Series using Local Neural Transformations
Tim Schneider, Chen Qiu, Marius Kloft +4
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical d…
Variational Dynamic Mixtures
Chen Qiu, Stephan Mandt, Maja Rudolph
Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence…
Extending Machine Language Models toward Human-Level Language Understanding
James L. McClelland, Felix Hill, Maja Rudolph +2
Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. The human…
Structured Embedding Models for Grouped Data
Maja Rudolph, Francisco Ruiz, Susan Athey +1
Word embeddings are a powerful approach for analyzing language, and exponential family embeddings (EFE) extend them to other types of data. Here we develop structured exponential f…
Dynamic Bernoulli Embeddings for Language Evolution
Maja Rudolph, David Blei
Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings i…
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve
Maja R. Rudolph, Joseph G. Ellis, David M. Blei
Many online companies sell advertisement space in second-price auctions with reserve. In this paper, we develop a probabilistic method to learn a profitable strategy to set the res…