223 citations · 396 across the 17 of their papers we have counts for
17 papers
LoRA ensembles for large language model fine-tuning
Xi Wang, Laurence Aitchison, Maja Rudolph
Finetuned LLMs often exhibit poor uncertainty quantification, manifesting as overconfidence, poor calibration, and unreliable prediction results on test data or out-of-distribution…
Deep Anomaly Detection on Tennessee Eastman Process Data
Fabian Hartung, Billy Joe Franks, Tobias Michels +15
This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tenn…
Deep Anomaly Detection under Labeling Budget Constraints
Aodong Li, Chen Qiu, Marius Kloft +3
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud…
Raising the Bar in Graph-level Anomaly Detection
Chen Qiu, Marius Kloft, Stephan Mandt +1
Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks. While most rese…
Complex-Valued Autoencoders for Object Discovery
Sindy Löwe, Phillip Lippe, Maja Rudolph +1
Object-centric representations form the basis of human perception, and enable us to reason about the world and to systematically generalize to new settings. Currently, most works o…
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…
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
Chen Qiu, Aodong Li, Marius Kloft +2
Anomaly detection aims at identifying data points that show systematic deviations from the majority of data in an unlabeled dataset. A common assumption is that clean training data…
Switching Recurrent Kalman Networks
Giao Nguyen-Quynh, Philipp Becker, Chen Qiu +2
Forecasting driving behavior or other sensor measurements is an essential component of autonomous driving systems. Often real-world multivariate time series data is hard to model b…
Modeling Irregular Time Series with Continuous Recurrent Units
Mona Schirmer, Mazin Eltayeb, Stefan Lessmann +1
Recurrent neural networks (RNNs) are a popular choice for modeling sequential data. Modern RNN architectures assume constant time-intervals between observations. However, in many d…
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Chen Qiu, Timo Pfrommer, Marius Kloft +2
Data transformations (e.g. rotations, reflections, and cropping) play an important role in self-supervised learning. Typically, images are transformed into different views, and neu…
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…
Edward: A library for probabilistic modeling, inference, and criticism
Dustin Tran, Alp Kucukelbir, Adji B. Dieng +3
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative p…
Exponential Family Embeddings
Maja R. Rudolph, Francisco J. R. Ruiz, Stephan Mandt +1
Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, a class of methods t…
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…