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Daniel Lowd
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- affiliation: University of Oregon, Eugene, USA
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2020 – today
- 2024
- [j6]Zayd Hammoudeh
, Daniel Lowd
Training data influence analysis and estimation: a survey. Mach. Learn. 113(5): 2351-2403 (2024) - [c49]Zayd Hammoudeh, Daniel Lowd:
Provable Robustness against a Union of L_0 Adversarial Attacks. AAAI 2024: 21134-21142 - [i22]John Heibel, Daniel Lowd:
MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants. CoRR abs/2407.11072 (2024) - 2023
- [j5]Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd:
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees. J. Mach. Learn. Res. 24: 154:1-154:48 (2023) - [c48]Wencong You, Zayd Hammoudeh, Daniel Lowd:
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers. EMNLP (Findings) 2023: 12499-12527 - [c47]Zayd Hammoudeh, Daniel Lowd:
Reducing Certified Regression to Certified Classification for General Poisoning Attacks. SaTML 2023: 484-523 - [i21]Zayd Hammoudeh, Daniel Lowd:
Feature Partition Aggregation: A Fast Certified Defense Against a Union of Sparse Adversarial Attacks. CoRR abs/2302.11628 (2023) - [i20]Wencong You, Zayd Hammoudeh, Daniel Lowd:
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers. CoRR abs/2310.18603 (2023) - 2022
- [c46]Zayd Hammoudeh, Daniel Lowd:
Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation. CCS 2022: 1367-1381 - [c45]Jonathan Brophy, Daniel Lowd:
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees. NeurIPS 2022 - [i19]Zhouhang Xie, Jonathan Brophy, Adam Noack, Wencong You, Kalyani Asthana, Carter Perkins, Sabrina Reis, Sameer Singh, Daniel Lowd:
Identifying Adversarial Attacks on Text Classifiers. CoRR abs/2201.08555 (2022) - [i18]Zayd Hammoudeh, Daniel Lowd:
Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation. CoRR abs/2201.10055 (2022) - [i17]Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd:
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees. CoRR abs/2205.00359 (2022) - [i16]Jonathan Brophy, Daniel Lowd:
Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees. CoRR abs/2205.11412 (2022) - [i15]Zayd Hammoudeh, Daniel Lowd:
Reducing Certified Regression to Certified Classification. CoRR abs/2208.13904 (2022) - [i14]Kalyani Asthana, Zhouhang Xie, Wencong You, Adam Noack, Jonathan Brophy, Sameer Singh, Daniel Lowd:
TCAB: A Large-Scale Text Classification Attack Benchmark. CoRR abs/2210.12233 (2022) - [i13]Zayd Hammoudeh, Daniel Lowd:
Training Data Influence Analysis and Estimation: A Survey. CoRR abs/2212.04612 (2022) - 2021
- [c44]Zhouhang Xie, Jonathan Brophy, Adam Noack, Wencong You, Kalyani Asthana, Carter Perkins, Sabrina Reis, Zayd Hammoudeh, Daniel Lowd, Sameer Singh:
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations. BlackboxNLP@EMNLP 2021: 69-78 - [c43]Jonathan Brophy, Daniel Lowd:
Machine Unlearning for Random Forests. ICML 2021: 1092-1104 - 2020
- [c42]Zayd Hammoudeh, Daniel Lowd:
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift. NeurIPS 2020 - [c41]Soheil Jamshidi, Zayd Hammoudeh, Ramakrishnan Durairajan, Daniel Lowd, Reza Rejaie, Walter Willinger:
On the Practicality of Learning Models for Network Telemetry. TMA 2020 - [i12]Jonathan Brophy, Daniel Lowd:
EGGS: A Flexible Approach to Relational Modeling of Social Network Spam. CoRR abs/2001.04909 (2020) - [i11]Zayd Hammoudeh, Daniel Lowd:
Learning from Positive and Unlabeled Data with Arbitrary Positive Shift. CoRR abs/2002.10261 (2020) - [i10]Jonathan Brophy, Daniel Lowd:
TREX: Tree-Ensemble Representer-Point Explanations. CoRR abs/2009.05530 (2020) - [i9]Jonathan Brophy, Daniel Lowd:
DART: Data Addition and Removal Trees. CoRR abs/2009.05567 (2020)
2010 – 2019
- 2019
- [j4]Pedro M. Domingos, Daniel Lowd:
Unifying logical and statistical AI with Markov logic. Commun. ACM 62(7): 74-83 (2019) - 2018
- [c40]Javid Ebrahimi, Anyi Rao
HotFlip: White-Box Adversarial Examples for Text Classification. ACL (2) 2018: 31-36 - [c39]Javid Ebrahimi, Daniel Lowd, Dejing Dou:
On Adversarial Examples for Character-Level Neural Machine Translation. COLING 2018: 653-663 - [i8]Javid Ebrahimi, Daniel Lowd, Dejing Dou:
On Adversarial Examples for Character-Level Neural Machine Translation. CoRR abs/1806.09030 (2018) - 2017
- [c38]Jonathan Brophy, Daniel Lowd:
Collective Classification of Social Network Spam. AAAI Workshops 2017 - [c37]Amir Pouran Ben Veyseh, Javid Ebrahimi, Dejing Dou, Daniel Lowd:
A Temporal Attentional Model for Rumor Stance Classification. CIKM 2017: 2335-2338 - [i7]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:
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation. CoRR abs/1711.03902 (2017) - [i6]Javid Ebrahimi, Anyi Rao
HotFlip: White-Box Adversarial Examples for NLP. CoRR abs/1712.06751 (2017) - 2016
- [j3]Shangpu Jiang, Daniel Lowd, Sabin Kafle, Dejing Dou:
Ontology Matching with Knowledge Rules. Trans. Large Scale Data Knowl. Centered Syst. 28: 75-95 (2016) - [c36]Shangpu Jiang, Daniel Lowd, Dejing Dou:
A Probabilistic Approach to Knowledge Translation. AAAI 2016: 1716-1722 - [c35]Amirmohammad Rooshenas, Daniel Lowd:
Discriminative Structure Learning of Arithmetic Circuits. AAAI 2016: 4258-4259 - [c34]Amirmohammad Rooshenas, Daniel Lowd:
Discriminative Structure Learning of Arithmetic Circuits. AISTATS 2016: 1506-1514 - [c33]Javid Ebrahimi, Dejing Dou, Daniel Lowd:
A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets. COLING 2016: 2656-2665 - [c32]Hao Wang, Dejing Dou, Daniel Lowd:
Ontology-Based Deep Restricted Boltzmann Machine. DEXA (1) 2016: 431-445 - [c31]Javid Ebrahimi, Dejing Dou, Daniel Lowd:
Weakly Supervised Tweet Stance Classification by Relational Bootstrapping. EMNLP 2016: 1012-1017 - [c30]Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla:
Unifying Logical and Statistical AI. LICS 2016: 1-11 - 2015
- [j2]Daniel Lowd, Amirmohammad Rooshenas:
The Libra toolkit for probabilistic models. J. Mach. Learn. Res. 16: 2459-2463 (2015) - [c29]Igor Burago, Daniel Lowd:
Automated Attacks on Compression-Based Classifiers. AISec@CCS 2015: 69-80 - [c28]Shangpu Jiang, Daniel Lowd, Dejing Dou:
Ontology Matching with Knowledge Rules. DEXA (1) 2015: 94-108 - [i5]Daniel Lowd, Amirmohammad Rooshenas:
The Libra Toolkit for Probabilistic Models. CoRR abs/1504.00110 (2015) - [i4]Shangpu Jiang, Daniel Lowd, Dejing Dou:
Ontology Matching with Knowledge Rules. CoRR abs/1507.03097 (2015) - [i3]Shangpu Jiang, Daniel Lowd, Dejing Dou:
A Probabilistic Approach to Knowledge Translation. CoRR abs/1507.03181 (2015) - 2014
- [j1]Daniel Lowd, Jesse Davis:
Improving Markov network structure learning using decision trees. J. Mach. Learn. Res. 15(1): 501-532 (2014) - [c27]Daniel Lowd, Brenton Lessley, Mino De Raj:
Towards Adversarial Reasoning in Statistical Relational Domains. StarAI@AAAI 2014 - [c26]Reza Motamedi, Reza Rejaie, Walter Willinger, Daniel Lowd, Roberto Gonzalez:
Inferring coarse views of connectivity in very large graphs. COSN 2014: 191-202 - [c25]MohamadAli Torkamani, Daniel Lowd:
On Robustness and Regularization of Structural Support Vector Machines. ICML 2014: 577-585 - [c24]Amirmohammad Rooshenas, Daniel Lowd:
Learning Sum-Product Networks with Direct and Indirect Variable Interactions. ICML 2014: 710-718 - [c23]Adam Bates, Ryan Leonard, Hannah Pruse, Daniel Lowd, Kevin R. B. Butler:
Leveraging USB to Establish Host Identity Using Commodity Devices. NDSS 2014 - 2013
- [c22]Amirmohammad Rooshenas, Daniel Lowd:
Learning Tractable Graphical Models Using Mixture of Arithmetic Circuits. AAAI (Late-Breaking Developments) 2013 - [c21]Daniel Lowd, Amirmohammad Rooshenas:
Learning Markov Networks With Arithmetic Circuits. AISTATS 2013: 406-414 - [c20]David Stevens, Daniel Lowd:
On the hardness of evading combinations of linear classifiers. AISec 2013: 77-86 - [c19]MohamadAli Torkamani, Daniel Lowd:
Convex Adversarial Collective Classification. ICML (1) 2013: 642-650 - 2012
- [c18]Shangpu Jiang, Daniel Lowd, Dejing Dou:
Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic. ICDM 2012: 912-917 - [c17]Shangpu Jiang, Daniel Lowd, Dejing Dou:
Using Markov Logic to Refine an Automatically Extracted Knowledge Base. StarAI@UAI 2012 - [c16]MohamadAli Torkamani, Daniel Lowd:
Convex Adversarial Collective Classification. StarAI@UAI 2012 - [c15]Daniel Lowd:
Closed-Form Learning of Markov Networks from Dependency Networks. UAI 2012: 533-542 - [i2]Daniel Lowd, Pedro M. Domingos:
Learning Arithmetic Circuits. CoRR abs/1206.3271 (2012) - [i1]Daniel Lowd:
Closed-Form Learning of Markov Networks from Dependency Networks. CoRR abs/1210.4896 (2012) - 2011
- [c14]Daniel Lowd, Arash Shamaei:
Mean Field Inference in Dependency Networks: An Empirical Study. AAAI 2011: 404-410 - 2010
- [c13]Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. StarAI@AAAI 2010 - [c12]Daniel Lowd, Jesse Davis
Learning Markov Network Structure with Decision Trees. ICDM 2010: 334-343 - [c11]Daniel Lowd, Pedro M. Domingos:
Approximate Inference by Compilation to Arithmetic Circuits. NIPS 2010: 1477-1485 - [c10]Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik:
Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. ECML/PKDD (2) 2010: 434-450 - [p2]Pedro M. Domingos, Daniel Lowd, Stanley Kok, Aniruddh Nath, Hoifung Poon, Matthew Richardson, Parag Singla:
Markov Logic: A Language and Algorithms for Link Mining. Link Mining 2010: 135-161
2000 – 2009
- 2009
- [b1]Pedro M. Domingos, Daniel Lowd:
Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009, ISBN 978-3-031-00421-6 - [c9]Daniel Lowd, Nicholas Kushmerick:
Using salience to segment desktop activity into projects. IUI 2009: 463-468 - 2008
- [c8]Pedro M. Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, Parag Singla:
Just Add Weights: Markov Logic for the Semantic Web. URSW (LNCS Vol.) 2008: 1-25 - [c7]Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla, Marc Sumner, Jue Wang:
Markov Logic: A Unifying Language for Structural and Statistical Pattern Recognition. SSPR/SPR 2008: 3 - [c6]Daniel Lowd, Pedro M. Domingos:
Learning Arithmetic Circuits. UAI 2008: 383-392 - [p1]Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla:
Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117 - 2007
- [c5]Daniel Lowd, Pedro M. Domingos:
Recursive Random Fields. IJCAI 2007: 950-955 - [c4]Daniel Lowd, Pedro M. Domingos:
Efficient Weight Learning for Markov Logic Networks. PKDD 2007: 200-211 - 2005
- [c3]Daniel Lowd, Christopher Meek:
Good Word Attacks on Statistical Spam Filters. CEAS 2005 - [c2]Daniel Lowd, Pedro M. Domingos:
Naive Bayes models for probability estimation. ICML 2005: 529-536 - [c1]Daniel Lowd, Christopher Meek:
Adversarial learning. KDD 2005: 641-647
Coauthor Index
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last updated on 2024-09-13 00:43 CEST by the dblp team
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