Daniel Lowd

Orcid: 0000-0002-9501-0361

Affiliations:
  • University of Oregon, Eugene, USA


According to our database1, Daniel Lowd authored at least 65 papers between 2005 and 2024.

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Bibliography

2024
Training data influence analysis and estimation: a survey.
Mach. Learn., May, 2024

MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants.
CoRR, 2024

Provable Robustness against a Union of L_0 Adversarial Attacks.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees.
J. Mach. Learn. Res., 2023

Feature Partition Aggregation: A Fast Certified Defense Against a Union of Sparse Adversarial Attacks.
CoRR, 2023

Reducing Certified Regression to Certified Classification for General Poisoning Attacks.
Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022
TCAB: A Large-Scale Text Classification Attack Benchmark.
CoRR, 2022

Reducing Certified Regression to Certified Classification.
CoRR, 2022

Identifying Adversarial Attacks on Text Classifiers.
CoRR, 2022

Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation.
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022

2021
Machine Unlearning for Random Forests.
Proceedings of the 38th International Conference on Machine Learning, 2021

What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations.
Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2021

2020
DART: Data Addition and Removal Trees.
CoRR, 2020

TREX: Tree-Ensemble Representer-Point Explanations.
CoRR, 2020

EGGS: A Flexible Approach to Relational Modeling of Social Network Spam.
CoRR, 2020

On the Practicality of Learning Models for Network Telemetry.
Proceedings of the 4th Network Traffic Measurement and Analysis Conference, 2020

Learning from Positive and Unlabeled Data with Arbitrary Positive Shift.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
Unifying logical and statistical AI with Markov logic.
Commun. ACM, 2019

2018
On Adversarial Examples for Character-Level Neural Machine Translation.
Proceedings of the 27th International Conference on Computational Linguistics, 2018

HotFlip: White-Box Adversarial Examples for Text Classification.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017
HotFlip: White-Box Adversarial Examples for NLP.
CoRR, 2017

Neural-Symbolic Learning and Reasoning: A Survey and Interpretation.
CoRR, 2017

A Temporal Attentional Model for Rumor Stance Classification.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Collective Classification of Social Network Spam.
Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Ontology Matching with Knowledge Rules.
Trans. Large Scale Data Knowl. Centered Syst., 2016

Unifying Logical and Statistical AI.
Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science, 2016

Weakly Supervised Tweet Stance Classification by Relational Bootstrapping.
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016

Ontology-Based Deep Restricted Boltzmann Machine.
Proceedings of the Database and Expert Systems Applications, 2016

A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets.
Proceedings of the COLING 2016, 2016

Discriminative Structure Learning of Arithmetic Circuits.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

A Probabilistic Approach to Knowledge Translation.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
The Libra toolkit for probabilistic models.
J. Mach. Learn. Res., 2015

Ontology Matching with Knowledge Rules.
Proceedings of the Database and Expert Systems Applications, 2015

Automated Attacks on Compression-Based Classifiers.
Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, 2015

2014
Improving Markov network structure learning using decision trees.
J. Mach. Learn. Res., 2014

Leveraging USB to Establish Host Identity Using Commodity Devices.
Proceedings of the 21st Annual Network and Distributed System Security Symposium, 2014

On Robustness and Regularization of Structural Support Vector Machines.
Proceedings of the 31th International Conference on Machine Learning, 2014

Learning Sum-Product Networks with Direct and Indirect Variable Interactions.
Proceedings of the 31th International Conference on Machine Learning, 2014

Inferring coarse views of connectivity in very large graphs.
Proceedings of the second ACM conference on Online social networks, 2014

Towards Adversarial Reasoning in Statistical Relational Domains.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013
Convex Adversarial Collective Classification.
Proceedings of the 30th International Conference on Machine Learning, 2013

On the hardness of evading combinations of linear classifiers.
Proceedings of the AISec'13, 2013

Learning Markov Networks With Arithmetic Circuits.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

Learning Tractable Graphical Models Using Mixture of Arithmetic Circuits.
Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

2012
Closed-Form Learning of Markov Networks from Dependency Networks.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Using Markov Logic to Refine an Automatically Extracted Knowledge Base.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

2011
Mean Field Inference in Dependency Networks: An Empirical Study.
Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010
Approximate Inference by Compilation to Arithmetic Circuits.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Learning Markov Network Structure with Decision Trees.
Proceedings of the ICDM 2010, 2010

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.
Proceedings of the Statistical Relational Artificial Intelligence, 2010

Markov Logic: A Language and Algorithms for Link Mining.
Proceedings of the Link Mining: Models, Algorithms, and Applications, 2010

2009
Markov Logic: An Interface Layer for Artificial Intelligence
Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01549-6, 2009

Using salience to segment desktop activity into projects.
Proceedings of the 14th International Conference on Intelligent User Interfaces, 2009

2008
Learning Arithmetic Circuits.
Proceedings of the UAI 2008, 2008

Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition.
Proceedings of the Structural, 2008

Just Add Weights: Markov Logic for the Semantic Web.
Proceedings of the Uncertainty Reasoning for the Semantic Web I, 2008

Markov Logic.
Proceedings of the Probabilistic Inductive Logic Programming - Theory and Applications, 2008

2007
Efficient Weight Learning for Markov Logic Networks.
Proceedings of the Knowledge Discovery in Databases: PKDD 2007, 2007

Recursive Random Fields.
Proceedings of the IJCAI 2007, 2007

2005
Adversarial learning.
Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005

Naive Bayes models for probability estimation.
Proceedings of the Machine Learning, 2005

Good Word Attacks on Statistical Spam Filters.
Proceedings of the CEAS 2005, 2005


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