DBLP: Daniel Lowd

	2012
c17		Shangpu Jiang, Daniel Lowd, Dejing Dou: Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic. ICDM 2012: 912-917
c16		Daniel Lowd: Closed-Form Learning of Markov Networks from Dependency Networks. UAI 2012: 533-542
i2		Daniel Lowd, Pedro 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
c15		Daniel Lowd, Arash Shamaei: Mean Field Inference in Dependency Networks: An Empirical Study. AAAI 2011
	2010
c14		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. Statistical Relational Artificial Intelligence 2010
c13		Daniel Lowd, Jesse Davis: Learning Markov Network Structure with Decision Trees. ICDM 2010: 334-343
c12		Daniel Lowd, Pedro Domingos: Approximate Inference by Compilation to Arithmetic Circuits. NIPS 2010: 1477-1485
c11		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
	2009
b1		Pedro Domingos, Daniel Lowd: Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009
c10		Daniel Lowd, Nicholas Kushmerick: Using salience to segment desktop activity into projects. IUI 2009: 463-468
	2008
c9		Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla: Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117
c8		Pedro 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 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 Domingos: Learning Arithmetic Circuits. UAI 2008: 383-392
	2007
c5		Daniel Lowd, Pedro Domingos: Recursive Random Fields. IJCAI 2007: 950-955
c4		Daniel Lowd, Pedro 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 Domingos: Naive Bayes models for probability estimation. ICML 2005: 529-536
c1		Daniel Lowd, Christopher Meek: Adversarial learning. KDD 2005: 641-647

2012

c17

Shangpu Jiang, Daniel Lowd, Dejing Dou: Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic. ICDM 2012: 912-917

c16

Daniel Lowd: Closed-Form Learning of Markov Networks from Dependency Networks. UAI 2012: 533-542

Daniel Lowd, Pedro Domingos: Learning Arithmetic Circuits. CoRR abs/1206.3271 (2012)

Daniel Lowd: Closed-Form Learning of Markov Networks from Dependency Networks. CoRR abs/1210.4896 (2012)

2011

c15

Daniel Lowd, Arash Shamaei: Mean Field Inference in Dependency Networks: An Empirical Study. AAAI 2011

2010

c14

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. Statistical Relational Artificial Intelligence 2010

c13

Daniel Lowd, Jesse Davis: Learning Markov Network Structure with Decision Trees. ICDM 2010: 334-343

c12

Daniel Lowd, Pedro Domingos: Approximate Inference by Compilation to Arithmetic Circuits. NIPS 2010: 1477-1485

c11

2009

Pedro Domingos, Daniel Lowd: Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2009

c10

Daniel Lowd, Nicholas Kushmerick: Using salience to segment desktop activity into projects. IUI 2009: 463-468

2008

Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla: Markov Logic. Probabilistic Inductive Logic Programming 2008: 92-117

Pedro 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

Pedro 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

Daniel Lowd, Pedro Domingos: Learning Arithmetic Circuits. UAI 2008: 383-392

2007

Daniel Lowd, Pedro Domingos: Recursive Random Fields. IJCAI 2007: 950-955

Daniel Lowd, Pedro Domingos: Efficient Weight Learning for Markov Logic Networks. PKDD 2007: 200-211

2005

Daniel Lowd, Christopher Meek: Good Word Attacks on Statistical Spam Filters. CEAS 2005

Daniel Lowd, Pedro Domingos: Naive Bayes models for probability estimation. ICML 2005: 529-536

Daniel Lowd, Christopher Meek: Adversarial learning. KDD 2005: 641-647

1	Jesse Davis	[c13]
2	Pedro Domingos	[i2] [c12] [b1] [c9] [c8] [c7] [c6] [c5] [c4] [c2]
3	Dejing Dou	[c17]
4	Shangpu Jiang	[c17]
5	Kristian Kersting	[c14] [c11]
6	Tushar Khot	[c14] [c11]
7	Stanley Kok	[c9] [c8] [c7]
8	Nicholas Kushmerick	[c10]
9	Christopher Meek	[c3] [c1]
10	Sriraam Natarajan	[c14] [c11]
11	Hoifung Poon	[c9] [c8] [c7]
12	Matthew Richardson	[c9] [c8] [c7]
13	Arash Shamaei	[c15]
14	Jude W. Shavlik	[c14] [c11]
15	Parag Singla	[c9] [c8] [c7]
16	Marc Sumner	[c7]
17	Prasad Tadepalli	[c14] [c11]
18	Jue Wang	[c7]

Daniel Lowd

Coauthor Index