152 citations · 285 across the 23 of their papers we have counts for
29 papers
Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading
Christian Hansen
Sequential modelling entails making sense of sequential data, which naturally occurs in a wide array of domains. One example is systems that interact with users, log user actions a…
Inference for Dependent Data with Learned Clusters
Jianfei Cao, Christian Hansen, Damian Kozbur +1
This paper presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependen…
Inference for Low-Rank Models
Victor Chernozhukov, Christian Hansen, Yuan Liao +1
This paper studies inference in linear models with a high-dimensional parameter matrix that can be well-approximated by a ``spiked low-rank matrix.'' A spiked low-rank matrix has r…
Automatic Fake News Detection: Are Models Learning to Reason?
Casper Hansen, Christian Hansen, Lucas Chaves Lima
Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on t…
Multi-Head Self-Attention with Role-Guided Masks
Dongsheng Wang, Casper Hansen, Lucas Chaves Lima +4
The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms. Simply put, the attention mechanisms learn to a…
Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19
Lucas Chaves Lima, Casper Hansen, Christian Hansen +5
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the…
Instrumental Variable Quantile Regression
Victor Chernozhukov, Christian Hansen, Kaspar Wuthrich
This chapter reviews the instrumental variable quantile regression model of Chernozhukov and Hansen (2005). We discuss the key conditions used for identification of structural quan…
Factuality Checking in News Headlines with Eye Tracking
Christian Hansen, Casper Hansen, Jakob Grue Simonsen +3
We study whether it is possible to infer if a news headline is true or false using only the movement of the human eyes when reading news headlines. Our study with 55 participants w…
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
Isabelle Augenstein, Christina Lioma, Dongsheng Wang +4
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking we…
Modelling End-of-Session Actions in Educational Systems
Christian Hansen, Casper Hansen, Stephen Alstrup +1
In this paper we consider the problem of modelling when students end their session in an online mathematics educational system. Being able to model this accurately will help us opt…
Contextually Propagated Term Weights for Document Representation
Casper Hansen, Christian Hansen, Stephen Alstrup +2
Word embeddings predict a word from its neighbours by learning small, dense embedding vectors. In practice, this prediction corresponds to a semantic score given to the predicted w…
Unsupervised Neural Generative Semantic Hashing
Casper Hansen, Christian Hansen, Jakob Grue Simonsen +2
Fast similarity search is a key component in large-scale information retrieval, where semantic hashing has become a popular strategy for representing documents as binary hash codes…
Neural Speed Reading with Structural-Jump-LSTM
Christian Hansen, Casper Hansen, Stephen Alstrup +2
Recurrent neural networks (RNNs) can model natural language by sequentially 'reading' input tokens and outputting a distributed representation of each token. Due to the sequential…
Modelling Sequential Music Track Skips using a Multi-RNN Approach
Christian Hansen, Casper Hansen, Stephen Alstrup +2
Modelling sequential music skips provides streaming companies the ability to better understand the needs of the user base, resulting in a better user experience by reducing the nee…
Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking
Casper Hansen, Christian Hansen, Stephen Alstrup +2
Automatic fact-checking systems detect misinformation, such as fake news, by (i) selecting check-worthy sentences for fact-checking, (ii) gathering related information to the sente…
High-Dimensional Econometrics and Regularized GMM
Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov +2
This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a…
Simultaneous Confidence Intervals for High-dimensional Linear Models with Many Endogenous Variables
Alexandre Belloni, Christian Hansen, Whitney Newey
High-dimensional linear models with endogenous variables play an increasingly important role in recent econometric literature. In this work we allow for models with many endogenous…
Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care
Casper Hansen, Christian Hansen, Stephen Alstrup +1
We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of eithe…
Sequence Modelling For Analysing Student Interaction with Educational Systems
Christian Hansen, Casper Hansen, Niklas Hjuler +2
The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses…
Targeted Undersmoothing
Christian Hansen, Damian Kozbur, Sanjog Misra
This paper proposes a post-model selection inference procedure, called targeted undersmoothing, designed to construct uniformly valid confidence sets for a broad class of functiona…
Double/Debiased/Neyman Machine Learning of Treatment Effects
Victor Chernozhukov, Denis Chetverikov, Mert Demirer +3
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements ab…
The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications
Christian Hansen, Yuan Liao
We consider inference about coefficients on a small number of variables of interest in a linear panel data model with additive unobserved individual and time specific effects and a…
Double/Debiased Machine Learning for Treatment and Causal Parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer +4
Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these metho…
Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments
Victor Chernozhukov, Christian Hansen, Martin Spindler
In this note, we offer an approach to estimating causal/structural parameters in the presence of many instruments and controls based on methods for estimating sparse high-dimension…