Daniel Lowd

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

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Training data influence analysis and estimation: a survey.

Zayd Hammoudeh, Daniel Lowd

Mach. Learn., May, 2024

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

John Heibel, Daniel Lowd

CoRR, 2024

Provable Robustness against a Union of L_0 Adversarial Attacks.

Zayd Hammoudeh, Daniel Lowd

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees.

Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd

J. Mach. Learn. Res., 2023

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

Zayd Hammoudeh, Daniel Lowd

CoRR, 2023

Reducing Certified Regression to Certified Classification for General Poisoning Attacks.

Zayd Hammoudeh, Daniel Lowd

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.

Wencong You, Zayd Hammoudeh, Daniel Lowd

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

2022

TCAB: A Large-Scale Text Classification Attack Benchmark.

Kalyani Asthana, Zhouhang Xie, Wencong You, Adam Noack, Jonathan Brophy, Sameer Singh, Daniel Lowd

CoRR, 2022

Reducing Certified Regression to Certified Classification.

Zayd Hammoudeh, Daniel Lowd

CoRR, 2022

Identifying Adversarial Attacks on Text Classifiers.

Zhouhang Xie, Jonathan Brophy, Adam Noack, Wencong You, Kalyani Asthana, Carter Perkins, Sabrina Reis, Sameer Singh, Daniel Lowd

CoRR, 2022

Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees.

Jonathan Brophy, Daniel Lowd

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.

Zayd Hammoudeh, Daniel Lowd

Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022

2021

Machine Unlearning for Random Forests.

Jonathan Brophy, Daniel Lowd

Proceedings of the 38th International Conference on Machine Learning, 2021

What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations.

Zhouhang Xie, Jonathan Brophy, Adam Noack, Wencong You, Kalyani Asthana, Carter Perkins, Sabrina Reis, Zayd Hammoudeh, Daniel Lowd, Sameer Singh

Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2021

2020

DART: Data Addition and Removal Trees.

Jonathan Brophy, Daniel Lowd

CoRR, 2020

TREX: Tree-Ensemble Representer-Point Explanations.

Jonathan Brophy, Daniel Lowd

CoRR, 2020

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

Jonathan Brophy, Daniel Lowd

CoRR, 2020

On the Practicality of Learning Models for Network Telemetry.

Soheil Jamshidi, Zayd Hammoudeh, Ramakrishnan Durairajan, Daniel Lowd, Reza Rejaie, Walter Willinger

Proceedings of the 4th Network Traffic Measurement and Analysis Conference, 2020

Learning from Positive and Unlabeled Data with Arbitrary Positive Shift.

Zayd Hammoudeh, Daniel Lowd

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.

Pedro M. Domingos, Daniel Lowd

Commun. ACM, 2019

2018

On Adversarial Examples for Character-Level Neural Machine Translation.

Javid Ebrahimi, Daniel Lowd, Dejing Dou

Proceedings of the 27th International Conference on Computational Linguistics, 2018

HotFlip: White-Box Adversarial Examples for Text Classification.

Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017

HotFlip: White-Box Adversarial Examples for NLP.

Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou

CoRR, 2017

Neural-Symbolic Learning and Reasoning: A Survey and Interpretation.

Tarek R. Besold, Artur S. d'Avila Garcez, Sebastian Bader, Howard Bowman, Pedro M. Domingos, Pascal Hitzler, Kai-Uwe Kühnberger, Luís C. Lamb, Daniel Lowd, Priscila Machado Vieira Lima, Leo de Penning, Gadi Pinkas, Hoifung Poon, Gerson Zaverucha

CoRR, 2017

A Temporal Attentional Model for Rumor Stance Classification.

Amir Pouran Ben Veyseh, Javid Ebrahimi, Dejing Dou, Daniel Lowd

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Collective Classification of Social Network Spam.

Jonathan Brophy, Daniel Lowd

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Ontology Matching with Knowledge Rules.

Shangpu Jiang, Daniel Lowd, Sabin Kafle, Dejing Dou

Trans. Large Scale Data Knowl. Centered Syst., 2016

Unifying Logical and Statistical AI.

Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla

Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science, 2016

Weakly Supervised Tweet Stance Classification by Relational Bootstrapping.

Javid Ebrahimi, Dejing Dou, Daniel Lowd

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016

Ontology-Based Deep Restricted Boltzmann Machine.

Hao Wang, Dejing Dou, Daniel Lowd

Proceedings of the Database and Expert Systems Applications, 2016

A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets.

Javid Ebrahimi, Dejing Dou, Daniel Lowd

Proceedings of the COLING 2016, 2016

Discriminative Structure Learning of Arithmetic Circuits.

Amirmohammad Rooshenas, Daniel Lowd

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

A Probabilistic Approach to Knowledge Translation.

Shangpu Jiang, Daniel Lowd, Dejing Dou

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

The Libra toolkit for probabilistic models.

Daniel Lowd, Amirmohammad Rooshenas

J. Mach. Learn. Res., 2015

Ontology Matching with Knowledge Rules.

Shangpu Jiang, Daniel Lowd, Dejing Dou

Proceedings of the Database and Expert Systems Applications, 2015

Automated Attacks on Compression-Based Classifiers.

Igor Burago, Daniel Lowd

Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, 2015

2014

Improving Markov network structure learning using decision trees.

Daniel Lowd, Jesse Davis

J. Mach. Learn. Res., 2014

Leveraging USB to Establish Host Identity Using Commodity Devices.

Adam Bates, Ryan Leonard, Hannah Pruse, Daniel Lowd, Kevin R. B. Butler

Proceedings of the 21st Annual Network and Distributed System Security Symposium, 2014

On Robustness and Regularization of Structural Support Vector Machines.

MohamadAli Torkamani, Daniel Lowd

Proceedings of the 31th International Conference on Machine Learning, 2014

Learning Sum-Product Networks with Direct and Indirect Variable Interactions.

Amirmohammad Rooshenas, Daniel Lowd

Proceedings of the 31th International Conference on Machine Learning, 2014

Inferring coarse views of connectivity in very large graphs.

Reza Motamedi, Reza Rejaie, Walter Willinger, Daniel Lowd, Roberto Gonzalez

Proceedings of the second ACM conference on Online social networks, 2014

Towards Adversarial Reasoning in Statistical Relational Domains.

Daniel Lowd, Brenton Lessley, Mino De Raj

Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013

Convex Adversarial Collective Classification.

MohamadAli Torkamani, Daniel Lowd

Proceedings of the 30th International Conference on Machine Learning, 2013

On the hardness of evading combinations of linear classifiers.

David Stevens, Daniel Lowd

Proceedings of the AISec'13, 2013

Learning Markov Networks With Arithmetic Circuits.

Daniel Lowd, Amirmohammad Rooshenas

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

Learning Tractable Graphical Models Using Mixture of Arithmetic Circuits.

Amirmohammad Rooshenas, Daniel Lowd

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

2012

Closed-Form Learning of Markov Networks from Dependency Networks.

Daniel Lowd

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Using Markov Logic to Refine an Automatically Extracted Knowledge Base.

Shangpu Jiang, Daniel Lowd, Dejing Dou

Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic.

Shangpu Jiang, Daniel Lowd, Dejing Dou

Proceedings of the 12th IEEE International Conference on Data Mining, 2012

2011

Mean Field Inference in Dependency Networks: An Empirical Study.

Daniel Lowd, Arash Shamaei

Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010

Approximate Inference by Compilation to Arithmetic Circuits.

Daniel Lowd, Pedro M. Domingos

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.

Daniel Lowd, Jesse Davis

Proceedings of the ICDM 2010, 2010

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.

Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

Proceedings of the Statistical Relational Artificial Intelligence, 2010

Markov Logic: A Language and Algorithms for Link Mining.

Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla

Proceedings of the Link Mining: Models, Algorithms, and Applications, 2010

2009

Markov Logic: An Interface Layer for Artificial Intelligence

Pedro M. Domingos, Daniel Lowd

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.

Daniel Lowd, Nicholas Kushmerick

Proceedings of the 14th International Conference on Intelligent User Interfaces, 2009

2008

Learning Arithmetic Circuits.

Daniel Lowd, Pedro M. Domingos

Proceedings of the UAI 2008, 2008

Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition.

Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla, Marc Sumner, Jue Wang

Proceedings of the Structural, 2008

Just Add Weights: Markov Logic for the Semantic Web.

Pedro M. Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla

Proceedings of the Uncertainty Reasoning for the Semantic Web I, 2008

Markov Logic.

Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla

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

2007

Efficient Weight Learning for Markov Logic Networks.

Daniel Lowd, Pedro M. Domingos

Proceedings of the Knowledge Discovery in Databases: PKDD 2007, 2007

Recursive Random Fields.

Daniel Lowd, Pedro M. Domingos

Proceedings of the IJCAI 2007, 2007

2005

Adversarial learning.

Daniel Lowd, Christopher Meek

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

Naive Bayes models for probability estimation.

Daniel Lowd, Pedro M. Domingos

Proceedings of the Machine Learning, 2005

Good Word Attacks on Statistical Spam Filters.

Daniel Lowd, Christopher Meek

Proceedings of the CEAS 2005, 2005