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Chris Rohlfs

dblp:202/6663 · also Christopher Rohlfs · DBLP profile ↗
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4ranked-venue papers
4as first author
3since 2021 · last 2025
0000-0001-7714-9231ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 4 first-author · 3 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational finance and economics
financial forecasting
0.212016
The Effects of the Content of FOMC Communications on US Treasury Rates · EMNLP 2016
Computational finance and economics › financial data analysis
financial text analysis
0.212016
The Effects of the Content of FOMC Communications on US Treasury Rates · EMNLP 2016
Natural language and speech › Information extraction and text analysis › topic model
supervised topic model
0.112016
The Effects of the Content of FOMC Communications on US Treasury Rates · EMNLP 2016
Natural language and speech › Information extraction and text analysis
topic model
0.112016
The Effects of the Content of FOMC Communications on US Treasury Rates · EMNLP 2016

Methods — techniques the papers use, named apart from their topics

text classification · 0.5supervised topic modeling · 0.5
YearPublicationVenuePosition
2025 Generalization in neural networks: A broad survey
Chris Rohlfs
Neurocomputing1
2023 A descriptive analysis of olfactory sensation and memory in Drosophila and its relation to artificial neural networks
Chris Rohlfs
Neurocomputing1
2023 Problem-dependent attention and effort in neural networks with applications to image resolution and model selection
Chris Rohlfs
Image Vis. Comput.1
2016 The Effects of the Content of FOMC Communications on US Treasury Rates
abstract
This study measures the effects of Federal Open Market Committee text content on the direction of short-and medium-term interest rate movements.Because the words relevant to short-and medium-term interest rates differ, we apply a supervised approach to learn distinct sets of topics for each dependent variable being examined.We generate predictions with and without controlling for factors relevant to interest rate movements, and our prediction results average across multiple training-test splits.Using data from 1999-2016, we achieve 93% and 64% accuracy in predicting Target and Effective Federal Funds Rate movements and 38%-40% accuracy in predicting longer term Treasury Rate movements.We obtain lower but comparable accuracies after controlling for other macroeconomic and market factors.
Chris Rohlfs, Sunandan Chakraborty, Lakshminarayanan Subramanian
EMNLP1