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- Pedro M. Domingos, Daniel Lowd, +4 authors Parag Singla
- LICS
- 2006

Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on… (More)

- Aniruddh Nath, Pedro M. Domingos
- AAAI
- 2010

Many problems require repeated inference on probabilistic graphical models, with different values for evidence variables or other changes. Examples of such problems include utility maximization, MAP inference, online and interactive inference, parameter and structure learning, and dynamic inference. Since small changes to the evidence typically only affect… (More)

- Parag Singla, Aniruddh Nath, Pedro M. Domingos
- AAAI
- 2014

Many AI applications need to explicitly represent relational structure as well as handle uncertainty. First order probabilistic models combine the power of logic and probability to deal with such domains. A naive approach to inference in these models is to propositionalize the whole theory and carry out the inference on the ground network. Lifted inference… (More)

- Aniruddh Nath, Pedro M. Domingos
- AAAI
- 2010

Lifting can greatly reduce the cost of inference on firstorder probabilistic graphical models, but constructing the lifted network can itself be quite costly. In online applications (e.g., video segmentation) repeatedly constructing the lifted network for each new inference can be extremely wasteful, because the evidence typically changes little from one… (More)

- Parag Singla, Aniruddh Nath, Pedro M. Domingos
- Statistical Relational Artificial Intelligence
- 2010

Lifting can greatly reduce the cost of inference on firstorder probabilistic models, but constructing the lifted network can itself be quite costly. In addition, the minimal lifted network is often very close in size to the fully propositionalized model; lifted inference yields little or no speedup in these situations. In this paper, we address both these… (More)

- Aniruddh Nath, Pedro M. Domingos
- AAAI
- 2016

In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize across multiple programs. In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and… (More)

- Aniruddh Nath, Pedro M. Domingos
- AAAI
- 2015

Sum-product networks (SPNs) are a recently-proposed deep architecture that guarantees tractable inference, even on certain high-treewidth models. SPNs are a propositional architecture, treating the instances as independent and identically distributed. In this paper, we introduce Relational SumProduct Networks (RSPNs), a new tractable first-order… (More)

- Aniruddh Nath, Pedro M. Domingos
- AAAI Workshop: Statistical Relational Artificial…
- 2014

Intractable inference has been a major barrier to the wide adoption of statistical relational models. Existing exact methods suffer from a lack of scalability, and approximate methods tend to be unreliable. Sumproduct networks (SPNs; Poon and Domingos 2011) are a recently-proposed probabilistic architecture that guarantees tractable exact inference, even on… (More)

- Pedro Domingos, Daniel Lowd, +4 authors Parag Singla
- 2009

Link mining problems are characterized by high complexity (since linked objects are not statistically independent) and uncertainty (since data is noisy and incomplete). Thus they necessitate a modeling language that is both probabilistic and relational. Markov logic provides this by attaching weights to formulas in first-order logic and viewing them as… (More)

- Aniruddh Nath, Matthew Richardson
- AAAI
- 2012

Many first-order probabilistic models can be represented much more compactly using aggregation operations such as counting. While traditional statistical relational representations share factors across sets of interchangeable random variables, representations that explicitly model aggregations also exploit interchangeability of random variables within… (More)