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- Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy S. Liang
- AISTATS
- 2015

Tensor factorization arises in many machine learning applications, such as knowledge base modeling and parameter estimation in latent variable models. However, numerical methods for tensorâ€¦ (More)

- Arun Tejasvi Chaganty, Percy S. Liang
- ICML
- 2013

Let [n] = {1, . . . , n} denote the first n positive integers. We use xâŠ—p to represent the p-th order tensor formed by taking the outer product of x âˆˆ Rd; i.e. xâŠ—p i1...ip = xi1 Â· Â· Â·xip . We willâ€¦ (More)

- Gabor Angeli, Victor Zhong, +6 authors Christopher D. Manning
- TAC
- 2015

A central challenge in relation extraction is the lack of supervised training data. Pattern-based relation extractors suffer from low recall, whereas distant supervision yields noisy data which hurtsâ€¦ (More)

- Arun Tejasvi Chaganty, Percy S. Liang
- ICML
- 2014

Recent work on the method of moments enable consistent parameter estimation, but only for certain types of latent-variable models. On the other hand, pure likelihood objectives, though moreâ€¦ (More)

- Arun Tejasvi Chaganty, Aditya V. Nori, Sriram K. Rajamani
- AISTATS
- 2013

Probabilistic programs are intuitive and succinct representations of complex probability distributions. A natural approach to performing inference over these programs is to execute them and computeâ€¦ (More)

Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertaintyâ€¦ (More)

- Gabor Angeli, Arun Tejasvi Chaganty, +6 authors Christopher D. Manning
- TAC
- 2013

We describe Stanfordâ€™s entry in the TACKBP 2013 Slotfilling challenge. Our system makes use of a distantly supervised approach, implementing the multiinstance multi-label system of Surdeanu et al.â€¦ (More)

In statistical relational learning, one is concerned with inferring the most likely explanation (or world) that satisfies a given set of weighted constraints. The weight of a constraint signifies ourâ€¦ (More)

- Yuhao Zhang, Arun Tejasvi Chaganty, +4 authors Christopher D. Manning
- TAC
- 2016

We describe Stanfordâ€™s entries in the TAC KBP 2016 Cold Start Slot Filling and Knowledge Base Population challenge. Our biggest contribution is an entirely new Chinese entity detection and relationâ€¦ (More)

Knowledge base population (KBP) systems take in a large document corpus and extract entities and their relations. Thus far, KBP evaluation has relied on judgements on the pooled predictions ofâ€¦ (More)