Publications

Publications (grouped by year): 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, <=2006.
2017
  • N. Gupta, S. Singh, D. Roth. Entity Linking via Joint Encoding of Types, Descriptions, and Context. Empirical Methods in Natural Language Processing (EMNLP). 2017Conference
    For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features.
    In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively "embed" entities that are new to the KB, and is able to link its mentions accurately.
    @inproceedings{neuralel:emnlp17,
     author = {Nitish Gupta and Sameer Singh and Dan Roth},
     title = { Entity Linking via Joint Encoding of Types, Descriptions, and Context },
     booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
     year = {2017}
    }
  • A. Ananya, S. Singh. How Biased Are We? Automated Detection of Gendered Language. ACL Workshop on Women and Underrepresented Minorities in NLP (WiNLP). 2017Workshop
    Coming Soon!
    @inproceedings{gender:winlp17,
     author = {Ananya Ananya and Sameer Singh},
     title = { How Biased Are We? Automated Detection of Gendered Language },
     booktitle = {ACL Workshop on Women and Underrepresented Minorities in NLP (WiNLP)},
     year = {2017}
    }
  • P. Kordjamshidi, S. Singh, D. Khashabi, C. Christodoulopoulos, M. Summons, S. Sinha, D. Roth. Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks. International Workshop on Statistical Relational AI (StarAI). 2017Workshop
    Many real world systems need to operate on heterogeneous information networks that consist of numerous interacting components of different types. Examples include systems that perform data analysis on biological information networks; social networks; and information extraction systems processing unstructured data to convert raw text to knowledge graphs. Many previous works describe specialized approaches to perform specific types of analysis, mining and learning on such networks. In this work, we propose a unified framework consisting of a data model -a graph with a first order schema along with a declarative language for constructing, querying and manipulating such networks in ways that facilitate relational and structured machine learning. In particular, we provide an initial prototype for a relational and graph traversal query language where queries are directly used as relational features for structured machine learning models. Feature extraction is performed by making declarative graph traversal queries. Learning and inference models can directly operate on this relational representation and augment it with new data and knowledge that, in turn, is integrated seamlessly into the relational structure to support new predictions. We demonstrate this system's capabilities by showcasing tasks in natural language processing and computational biology domains.
    @inproceedings{saul:starai17,
     author = {Parisa Kordjamshidi and Sameer Singh and Daniel Khashabi and Christos Christodoulopoulos and Mark Summons and Saurabh Sinha and Dan Roth},
     title = { Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks },
     booktitle = {International Workshop on Statistical Relational AI (StarAI)},
     year = {2017}
    }
  • I. Burago, M. Levorato, S. Singh. Semantic Compression for Edge-Assisted Systems. Technical Report, Information Theory and Applications Workshop. 2017TechReport
    Coming Soon!
    @techreport{semcompress:ita17,
     author = {Igor Burago and Marco Levorato and Sameer Singh},
     title = { Semantic Compression for Edge-Assisted Systems },
     institution = {Information Theory and Applications Workshop},
     year = {2017}
    }
2016
  • H. Rashkin, S. Singh, Y. Choi. Connotation Frames: A Data-Driven Investigation. Association for Computational Linguistics (ACL). 2016Conference
    Coming Soon!
    @inproceedings{connot:acl16,
     author = {Hannah Rashkin and Sameer Singh and Yejin Choi},
     title = { Connotation Frames: A Data-Driven Investigation },
     booktitle = {Association for Computational Linguistics (ACL)},
     year = {2016}
    }
  • P. Kordjamshidi, D. Khashabi, C. Christodoulopoulos, B. Mangipudi, S. Singh, D. Roth. Better call Saul: Flexible Programming for Learning and Inference in NLP. International Conference on Computational Linguistics (COLING). 2016Conference
    Coming Soon!
    @inproceedings{saul:coling16,
     author = {Parisa Kordjamshidi and Daniel Khashabi and Christos Christodoulopoulos and Bhargav Mangipudi and Sameer Singh and Dan Roth},
     title = { Better call Saul: Flexible Programming for Learning and Inference in NLP },
     booktitle = {International Conference on Computational Linguistics (COLING)},
     year = {2016}
    }
  • M. Tulio Ribeiro, S. Singh, C. Guestrin. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Knowledge Discovery and Data Mining (KDD). 2016Conference
    Audience Appreciation Award
    Also presented at the CHI 2016 Workshop on Human-Centred Machine Learning (HCML).
    Coming Soon!
    @inproceedings{lime:kdd16,
     author = {Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
     title = { "Why Should I Trust You?": Explaining the Predictions of Any Classifier },
     booktitle = {Knowledge Discovery and Data Mining (KDD)},
     year = {2016}
    }
  • S. Singh, S. Riedel. Creating Interactive and Visual Educational Resources for AI. AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI). 2016Workshop
    Teaching artificial intelligence is effective if the experience is a visual and interactive one, with educational materials that utilize combinations of various content types such as text, math, and code into an integrated experience. Unfortunately, easy-to-use tools for creating such pedagogical resources are not available to the educators, resulting in most courses being taught using a disconnected set of static materials, which is not only ineffective for learning AI, but further, requires repeated and redundant effort for the instructor. In this paper, we introduce Moro, a software tool for easily creating and presenting AI-friendly teaching materials. Moro notebooks integrate content of different types (text, math, code, images), allow real-time interactions via modifiable and executable code blocks, and are viewable in browsers both as long-form pages and as presentations. Creating notebooks is easy and intuitive; the creation tool is also in-browser, is WYSIWYG for quick iterations of editing, and supports a variety of shortcuts and customizations for efficiency. We present three deployed case studies of Moro that widely differ from each other, demonstrating its utility in a variety of scenarios such as in-class teaching and conference tutorials.
    @inproceedings{moro:eaai16,
     author = {Sameer Singh and Sebastian Riedel},
     title = { Creating Interactive and Visual Educational Resources for {AI} },
     booktitle = {AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI)},
     year = {2016}
    }
  • M. Tulio Ribeiro, S. Singh, C. Guestrin. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. CHI Workshop on Human-Centred Machine Learning (HCML). 2016Workshop
    Shorter version of the paper presented at KDD 2016.
    Coming Soon!
    @inproceedings{lime:hcml16,
     author = {Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
     title = { "Why Should I Trust You?": Explaining the Predictions of Any Classifier },
     booktitle = {CHI Workshop on Human-Centred Machine Learning (HCML)},
     year = {2016}
    }
  • M. Tulio Ribeiro, S. Singh, C. Guestrin. Model-Agnostic Interpretability of Machine Learning. ICML Workshop on Human Interpretability in Machine Learning (WHI). 2016Workshop
    Best Paper Award
    Coming Soon!
    @inproceedings{lime:whi16,
     author = {Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
     title = { Model-Agnostic Interpretability of Machine Learning },
     booktitle = {ICML Workshop on Human Interpretability in Machine Learning (WHI)},
     year = {2016}
    }
  • S. Singh, M. Tulio Ribeiro, C. Guestrin. Programs as Black-Box Explanations. NIPS Workshop on Interpretable Machine Learning in Complex Systems. 2016Workshop
    Recent work in model-agnostic explanations of black-box machine learning has demonstrated that interpretability of complex models does not have to come at the cost of accuracy or model flexibility. However, it is not clear what kind of explanations, such as linear models, decision trees, and rule lists, are the appropriate family to consider, and different tasks and models may benefit from different kinds of explanations. Instead of picking a single family of representations, in this work we propose to use "programs" as model-agnostic explanations. We show that small programs can be expressive yet intuitive as explanations, and generalize over a number of existing interpretable families. We propose a prototype program induction method based on simulated annealing that approximates the local behavior of black-box classifiers around a specific prediction using random perturbations. Finally, we present preliminary application on small datasets and show that the generated explanations are intuitive and accurate for a number of classifiers.
    @inproceedings{prog:nipsws16,
     author = {Sameer Singh and Marco Tulio Ribeiro and Carlos Guestrin},
     title = { Programs as Black-Box Explanations },
     booktitle = {NIPS Workshop on Interpretable Machine Learning in Complex Systems},
     year = {2016}
    }
  • M. Tulio Ribeiro, S. Singh, C. Guestrin. Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance. NIPS Workshop on Interpretable Machine Learning in Complex Systems. 2016Workshop
    At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model's behavior.
    In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.
    @inproceedings{anchor:nipsws16,
     author = {Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
     title = { Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance },
     booktitle = {NIPS Workshop on Interpretable Machine Learning in Complex Systems},
     year = {2016}
    }
  • M. Tulio Ribeiro, S. Singh, C. Guestrin. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Demo at the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2016Demo
    Demonstration of the KDD 2016 paper.
    Coming Soon!
    @inproceedings{lime:naacl16,
     author = {Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
     title = { "Why Should I Trust You?": Explaining the Predictions of Any Classifier },
     booktitle = {Demo at the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2016}
    }
  • M. Tulio Ribeiro, S. Singh, C. Guestrin. Introduction to Local Interpretable Model-Agnostic Explanations (LIME). O'Reilly Media. 2016Online
    Coming Soon!
    @misc{lime:oreilly16,
     author = {Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
     title = { Introduction to Local Interpretable Model-Agnostic Explanations (LIME) },
     editor = {O'Reilly Media},
     url = {https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime},
     year = {2016}
    }
2015
  • T. Rocktaschel, S. Singh, S. Riedel. Injecting Logical Background Knowledge into Embeddings for Relation Extraction. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2015Conference
    Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortunately, these methods share a shortcoming with all other distantly supervised approaches: they cannot learn to extract target relations without existing data in the knowledge base, and likewise, these models are inaccurate for relations with sparse data. Rule-based extractors, on the other hand, can be easily extended to novel relations and improved for existing but inaccurate relations, through first-order formulae that capture auxiliary domain knowledge. However, usually a large set of such formulae is necessary to achieve generalization.
    In this paper, we introduce a paradigm for learning low-dimensional embeddings of entity-pairs and relations that combine the advantages of matrix factorization with first-order logic domain knowledge. We introduce simple approaches for estimating such embeddings, as well as a novel training algorithm to jointly optimize over factual and first-order logic information. Our results show that this method is able to learn accurate extractors with little or no distant supervision alignments, while at the same time generalizing to textual patterns that do not appear in the formulae.
    @inproceedings{logicmf:naacl15,
     author = {Tim Rocktaschel and Sameer Singh and Sebastian Riedel},
     title = { Injecting Logical Background Knowledge into Embeddings for Relation Extraction },
     booktitle = {Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2015}
    }
  • T. Chen, S. Singh, B. Taskar, C. Guestrin. Efficient Second-Order Gradient Boosting for Conditional Random Fields. International Conference on Artificial Intelligence and Statistics (AISTATS). 2015Conference
    Coming Soon!
    @inproceedings{gbcrf:aistats15,
     author = {Tianqi Chen and Sameer Singh and Ben Taskar and Carlos Guestrin},
     title = { Efficient Second-Order Gradient Boosting for Conditional Random Fields },
     booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
     year = {2015}
    }
  • X. Ling, S. Singh, D. Weld. Design Challenges for Entity Linking. Transactions of the Association for Computational Linguistics (TACL). 2015Journal
    To be presented at ACL, Beijing, July 26-31, 2015.
    Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.
    @article{el:tacl15,
     author = {Xiao Ling and Sameer Singh and Dan Weld},
     title = { Design Challenges for Entity Linking },
     journal = {Transactions of the Association for Computational Linguistics (TACL)},
     volume = {3},
     year = {2015}
    }
  • S. Singh, T. Rocktaschel, S. Riedel. Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction. NAACL Workshop on Vector Space Modeling for NLP. 2015Workshop
    Coming Soon!
    @inproceedings{mftf:vsm15,
     author = {Sameer Singh and Tim Rocktaschel and Sebastian Riedel},
     title = { Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction },
     booktitle = {NAACL Workshop on Vector Space Modeling for NLP},
     year = {2015}
    }
  • G. Bouchard, S. Singh, T. Trouillon. On Approximate Reasoning Capabilities of Low-Rank Vector Spaces. AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches. 2015Workshop
    Coming Soon!
    @inproceedings{logicmf:krr15,
     author = {Guillaume Bouchard and Sameer Singh and Theo Trouillon},
     title = { On Approximate Reasoning Capabilities of Low-Rank Vector Spaces },
     booktitle = {AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches},
     year = {2015}
    }
  • I. Sanchez, T. Rocktaschel, S. Riedel, S. Singh. Towards Extracting Faithful and Descriptive Representations of Latent Variable Models. AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches. 2015Workshop
    Coming Soon!
    @inproceedings{explain:krr15,
     author = {Ivan Sanchez and Tim Rocktaschel and Sebastian Riedel and Sameer Singh},
     title = { Towards Extracting Faithful and Descriptive Representations of Latent Variable Models },
     booktitle = {AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches},
     year = {2015}
    }
  • N. Gupta, S. Singh. Collective Factorization for Relational Data: An Evaluation on the Yelp Datasets. Technical Report, Yelp Dataset Challenge, Round 4. 2015TechReport
    Grand Prize Winner of Yelp Dataset Challenge Round 4
    Coming Soon!
    @techreport{factordb:yelp15,
     author = {Nitish Gupta and Sameer Singh},
     title = { Collective Factorization for Relational Data: An Evaluation on the Yelp Datasets },
     institution = {Yelp Dataset Challenge, Round 4},
     year = {2015}
    }
  • S. Singh, T. Rocktaschel, L. Hewitt, J. Naradowsky, S. Riedel. WOLFE: An NLP-friendly Declarative Machine Learning Stack. Demo at the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2015Demo
    Developing machine learning algorithms for natural language processing (NLP) applications is inherently an iterative process, involving a continuous refinement of the choice of model, engineering of features, selection of inference algorithms, search for the right hyper-parameters, and error analysis. Existing probabilistic program languages (PPLs) only provide partial solutions; most of them do not support commonly used models such as matrix factorization or neural networks, and do not facilitate interactive and iterative programming that is crucial for rapid development of these models.
    In this demo we introduce WOLFE, a stack designed to facilitate the development of NLP applications: (1) the WOLFE language allows the user to concisely define complex models, enabling easy modification and extension, (2) the WOLFE interpreter transforms declarative machine learning code into automatically differentiable terms or, where applicable, into factor graphs that allow for complex models to be applied to real-world applications, and (3) the WOLFE IDE provides a number of different visual and interactive elements, allowing intuitive exploration and editing of the data representations, the underlying graphical models, and the execution of the inference algorithms.
    @inproceedings{wolfe:naacl15,
     author = {Sameer Singh and Tim Rocktaschel and Luke Hewitt and Jason Naradowsky and Sebastian Riedel},
     title = { {WOLFE}: An {NLP}-friendly Declarative Machine Learning Stack },
     booktitle = {Demo at the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2015}
    }
2014
  • I. Cano, S. Singh, C. Guestrin. Distributed Non-Parametric Representations for Vital Filtering: UW at TREC KBA 2014. Text REtrieval Conference (TREC): Knowledge-Base Acceleration (KBA) Track. 2014Conference
    Coming Soon!
    @inproceedings{uw:kba14,
     author = {Ignacio Cano and Sameer Singh and Carlos Guestrin},
     title = { Distributed Non-Parametric Representations for Vital Filtering: {UW at TREC KBA} 2014 },
     booktitle = {Text REtrieval Conference (TREC): Knowledge-Base Acceleration (KBA) Track},
     year = {2014}
    }
  • T. Rocktaschel, S. Singh, M. Bosnjak, S. Riedel. Low-dimensional Embeddings of Logic. ACL 2014 Workshop on Semantic Parsing (SP14). 2014Workshop
    Exceptional Submission Award
    Also presented at StarAI 2014 with minor changes.
    Coming Soon!
    @inproceedings{logic:sp14,
     author = {Tim Rocktaschel and Sameer Singh and Matko Bosnjak and Sebastian Riedel},
     title = { Low-dimensional Embeddings of Logic },
     booktitle = {ACL 2014 Workshop on Semantic Parsing (SP14)},
     year = {2014}
    }
  • S. Riedel, S. Singh, V. Srikumar, T. Rocktaschel, L. Visengeriyeva, J. Noessner. WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming. International Workshop on Statistical Relational AI (StarAI). 2014Workshop
    Also presented at NIPS Probabilistic Programming Workshop.
    Coming Soon!
    @inproceedings{wolfe:starai14,
     author = {Sebastian Riedel and Sameer Singh and Vivek Srikumar and Tim Rocktaschel and Larysa Visengeriyeva and Jan Noessner},
     title = { WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming },
     booktitle = {International Workshop on Statistical Relational AI (StarAI)},
     year = {2014}
    }
  • M. Niepert, S. Singh. Out of Many, One: Unifying Web-Extracted Knowledge Bases. Workshop on Automated Knowledge Base Construction (AKBC). 2014Workshop
    Coming Soon!
    @inproceedings{kb-integration:akbc14,
     author = {Mathias Niepert and Sameer Singh},
     title = { Out of Many, One: Unifying Web-Extracted Knowledge Bases },
     booktitle = {Workshop on Automated Knowledge Base Construction (AKBC)},
     year = {2014}
    }
  • S. Singh, S. Riedel, L. Hewitt, T. Rocktaschel. Designing an IDE for Probabilistic Programming: Challenges and a Prototype. NIPS Workshop on Probabilistic Programming. 2014WorkshopDemo
    Also presented at NIPS 2014 as a demo.
    Coming Soon!
    @inproceedings{ppl-ide:probprog14,
     author = {Sameer Singh and Sebastian Riedel and Luke Hewitt and Tim Rocktaschel},
     title = { Designing an IDE for Probabilistic Programming: Challenges and a Prototype },
     booktitle = {NIPS Workshop on Probabilistic Programming},
     year = {2014}
    }
  • V. Lin, S. Singh, L. He, B. Taskar, L. Zettlemoyer. Multi-label Learning with Posterior Regularization. NIPS Workshop on Modern Machine Learning and Natural Language Processing. 2014Workshop
    Also presented at the Pacific Northwest Regional NLP Workshop (NW-NLP) 2014.
    Coming Soon!
    @inproceedings{prlr:mmlnlp14,
     author = {Victoria (Xi) Lin and Sameer Singh and Luheng He and Ben Taskar and Luke Zettlemoyer},
     title = { Multi-label Learning with Posterior Regularization },
     booktitle = {NIPS Workshop on Modern Machine Learning and Natural Language Processing},
     year = {2014}
    }
  • X. Ling, S. Singh, D. Weld. Context Representation for Named Entity Linking. Pacific Northwest Regional NLP Workshop (NW-NLP). 2014Workshop
    Coming Soon!
    @inproceedings{context:nwnlp14,
     author = {Xiao Ling and Sameer Singh and Dan Weld},
     title = { Context Representation for Named Entity Linking },
     booktitle = {Pacific Northwest Regional NLP Workshop (NW-NLP)},
     year = {2014}
    }
  • S. Singh, T. Graepel, L. J. Bordeaux, A. D. Gordon. Relational database management. US Patent Number 0188928. 2014
    Coming Soon!
    @techreport{rdb:patent14,
     author = {Sameer Singh and Thore Graepel and Lucas J. Bordeaux and Andrew D. Gordon},
     title = { Relational database management },
     institution = {US Patent Number 0188928},
     year = {2014}
    }
  • S. Singh. Scaling MCMC Inference and Belief Propagation for Large, Dense Graphical Models. PhD Thesis, University of Massachusetts. 2014
    Committee: Andrew McCallum, Carlos Guestrin, Ben Marlin, David Jensen, Michael Zink.
    With the physical constraints of semiconductor-based electronics becoming increasingly limiting in the past decade, single-core CPUs have given way to multi-core and distributed computing platforms. At the same time, access to large data collections is progressively becoming commonplace due to the lowering cost of storage and bandwidth. Traditional machine learning paradigms that have been designed to operate sequentially on single processor architectures seem destined to become obsolete in this world of multi-core, multi-node systems and massive data sets. Inference for graphical models is one such example for which most existing algorithms are sequential in nature and are difficult to scale using parallel computations. Further, modeling large datasets leads to an escalation in the number of variables, factors, domains, and the density of the models, all of which have a substantial impact on the computational and storage complexity of inference. To achieve scalability, existing techniques impose strict independence assumptions on the model, resulting in tractable inference at the expense of expressiveness, and therefore of accuracy and utility, of the model.
    Motivated by the need to scale inference to large, dense graphical models, in this thesis we explore approximations to Markov chain Monte Carlo (MCMC) and belief propagation (BP) that induce dynamic sparsity in the model to utilize parallelism. In particular, since computations over some factors, variables, and values are more important than over others at different stages of inference, proposed approximations that prioritize and parallelize such computations facilitate efficient inference. First, we show that a synchronously distributed MCMC algorithm that uses dynamic partitioning of the model achieves scalable inference. We then identify bottlenecks in the synchronous architecture, and demonstrate that a collection of MCMC techniques that use asynchronous updates are able to address these drawbacks. For large domains and high-order factors, we find that dynamically inducing sparsity in variable domains, results in scalable belief propagation that enables joint inference. We also show that formulating distributed BP and joint inference as generalized BP on cluster graphs, and by using cluster message approximations, provides significantly lower communication cost and running time.With these tools for inference in hand, we are able to tackle entity tagging, relation extraction, entity resolution, cross-document coreference, joint inference, and other information extraction tasks over large text corpora.
    @phdthesis{thesis,
     author = {Sameer Singh},
     title = { Scaling MCMC Inference and Belief Propagation for Large, Dense Graphical Models },
     school = {University of Massachusetts},
     year = {2014}
    }
2013
  • S. Singh, T. Graepel. Automated Probabilistic Modeling for Relational Data. ACM Conference of Information and Knowledge Management (CIKM). 2013Conference
    Probabilistic graphical model representations of relational data provide a number of desired features, such as inference of missing values, detection of errors, visualization of data, and probabilistic answers to relational queries. However, adoption has been slow due to the high level of expertise expected both in probability and in the domain from the user. Instead of requiring a domain expert to specify the probabilistic dependencies of the data, we present an approach that uses the relational DB schema to automatically construct a Bayesian graphical model for a database. This resulting model contains customized distributions for the attributes, latent variables that cluster the records, and factors that reflect and represent the foreign key links, whilst allowing efficient inference. Experiments demonstrate the accuracy of the model and scalability of inference on synthetic and real-world data.
    @inproceedings{cikm13,
     author = {Sameer Singh and Thore Graepel},
     title = { Automated Probabilistic Modeling for Relational Data },
     booktitle = {ACM Conference of Information and Knowledge Management (CIKM)},
     year = {2013}
    }
  • F. M. Suchanek, S. Singh, S. Riedel, P. P. Talukdar. AKBC 2013: Third Workshop on Automated Knowledge Base Construction. ACM Conference of Information and Knowledge Management (CIKM). 2013Conference
    The AKBC 2013 workshop aims to be a venue of excellence and vision in the area of knowledge base construction. This year's workshop will feature keynotes by ten leading researchers in the field, including from Google, Microsoft, Stanford, and CMU. The submissions focus on visionary ideas instead of on experimental evaluation. Nineteen accepted papers will be presented as posters, with nine exceptional papers also highlighted as spotlight talks. Thereby, the workshop aims provides a vivid forum of discussion about the field of automated knowledge base construction.
    @inproceedings{akbc13,
     author = {Fabian M. Suchanek and Sameer Singh and Sebastian Riedel and Partha P. Talukdar},
     title = { AKBC 2013: Third Workshop on Automated Knowledge Base Construction },
     booktitle = {ACM Conference of Information and Knowledge Management (CIKM)},
     year = {2013}
    }
  • J. Zheng, L. Vilnis, S. Singh, J. D. Choi, A. McCallum. Dynamic Knowledge-Base Alignment for Coreference Resolution. Conference on Computational Natural Language Learning (CoNLL). 2013Conference
    Coreference resolution systems can benefit greatly from inclusion of global context, and a number of recent approaches have demonstrated improvements when precomputing an alignment to external knowledge sources. However, since alignment itself is a challenging task and is often noisy, existing systems either align conservatively, resulting in very few links, or combine the attributes of multiple candidates, leading to a conflation of entities. Our approach instead performs joint inference between within-document coreference and entity linking, maintaining ranked lists of candidate entities that are dynamically merged and reranked during inference. Further, we incorporate a large set of surface string variations for each entity by using anchor texts from the web that link to the entity. These forms of global context enables our system to improve classifier-based coreference by 1.09 B3 F1 points, and improve over the previous state-of-art by 0.41 points, thus introducing a new state-of-art result on the ACE 2004 data.
    @inproceedings{conll13,
     author = {Jiaping Zheng and Luke Vilnis and Sameer Singh and Jinho D. Choi and Andrew McCallum},
     title = { Dynamic Knowledge-Base Alignment for Coreference Resolution },
     booktitle = {Conference on Computational Natural Language Learning (CoNLL)},
     year = {2013}
    }
  • S. Singh, L. Yao, D. Belanger, A. Kobren, S. Anzaroot, M. Wick, A. Passos, H. Pandya, J. D. Choi, B. Martin, A. McCallum. Universal Schema for Slot Filling and Cold Start: UMass IESL at TACKBP 2013. Text Analysis Conference on Knowledge Base Population (TAC KBP). 2013Conference
    We employ universal schema for the TAC KBP slot filling and cold start tasks. The technique enlarges the set of relations in an ontology, e.g., TACKBP slots, to contain all surface patterns between pairs of entities in a large corpus. By factorizing the matrix of co-occurrences between entity pairs and universal schema relations, we are able to predict new target slots. This differs fundamentally from traditional relation extraction approaches because an entire knowledge base is constructed jointly over train and test data. To produce submissions for the slot filling and cold start tasks, we simply query this knowledge base. We describe universal schema, our data preprocessing pipeline, and additional techniques we employ for predicting entities' attributes.
    @inproceedings{kbp13,
     author = {Sameer Singh and Limin Yao and David Belanger and Ari Kobren and Sam Anzaroot and Michael Wick and Alexandre Passos and Harshal Pandya and Jinho D. Choi and Brian Martin and Andrew McCallum},
     title = { Universal Schema for Slot Filling and Cold Start: UMass IESL at TACKBP 2013 },
     booktitle = {Text Analysis Conference on Knowledge Base Population (TAC KBP)},
     year = {2013}
    }
  • M. Wick, S. Singh, A. Kobren, A. McCallum. Assessing Confidence of Knowledge Base Content with an Experimental Study in Entity Resolution. CIKM Workshop on Automated Knowledge Base Construction (AKBC). 2013Workshop
    Coming Soon!
    @inproceedings{conf:akbc13,
     author = {Michael Wick and Sameer Singh and Ari Kobren and Andrew McCallum},
     title = { Assessing Confidence of Knowledge Base Content with an Experimental Study in Entity Resolution },
     booktitle = {CIKM Workshop on Automated Knowledge Base Construction (AKBC)},
     year = {2013}
    }
  • S. Singh, S. Riedel, B. Martin, J. Zheng, A. McCallum. Joint Inference of Entities, Relations, and Coreference. CIKM Workshop on Automated Knowledge Base Construction (AKBC). 2013Workshop
    Coming Soon!
    @inproceedings{jnt:akbc13,
     author = {Sameer Singh and Sebastian Riedel and Brian Martin and Jiaping Zheng and Andrew McCallum},
     title = { Joint Inference of Entities, Relations, and Coreference },
     booktitle = {CIKM Workshop on Automated Knowledge Base Construction (AKBC)},
     year = {2013}
    }
  • S. Singh, S. Riedel, A. McCallum. Anytime Belief Propagation Using Sparse Domains. Neural Information Processing Systems (NIPS) Workshop on Resource Efficient Machine Learning. 2013Workshop
    Belief Propagation has been widely used for marginal inference, however it is slow on problems with large-domain variables and high-order factors. Previous work provides useful approximations to facilitate inference on such models, but lacks important anytime properties such as: 1) providing accurate and consistent marginals when stopped early, 2) improving the approximation when run longer, and 3) converging to the fixed point of BP. To this end, we propose a message passing algorithm that works on sparse (partially instantiated) domains, and converges to consistent marginals using dynamic message scheduling. The algorithm grows the sparse domains incrementally, selecting the next value to add using prioritization schemes based on the gradients of the marginal inference objective. Our experiments demonstrate local anytime consistency and fast convergence, providing significant speedups over BP to obtain low-error marginals: up to 25 times on grid models, and up to 6 times on a real-world natural language processing task.
    @inproceedings{sparse:reseff13,
     author = {Sameer Singh and Sebastian Riedel and Andrew McCallum},
     title = { Anytime Belief Propagation Using Sparse Domains },
     booktitle = {Neural Information Processing Systems (NIPS) Workshop on Resource Efficient Machine Learning},
     year = {2013}
    }
2012
  • M. Wick, S. Singh, A. McCallum. A Discriminative Hierarchical Model for Fast Coreference at Large Scale. Association for Computational Linguistics (ACL). 2012Conference
    Coming Soon!
    @inproceedings{hcoref:acl12,
     author = {Michael Wick and Sameer Singh and Andrew McCallum},
     title = { A Discriminative Hierarchical Model for Fast Coreference at Large Scale },
     booktitle = {Association for Computational Linguistics (ACL)},
     year = {2012}
    }
  • S. Singh, M. Wick, A. McCallum. Monte Carlo MCMC: Efficient Inference by Approximate Sampling. Empirical Methods in Natural Language Processing (EMNLP). 2012Conference
    Coming Soon!
    @inproceedings{mcmcmc:emnlp12,
     author = {Sameer Singh and Michael Wick and Andrew McCallum},
     title = { Monte Carlo MCMC: Efficient Inference by Approximate Sampling },
     booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
     year = {2012}
    }
  • S. Singh, M. Wick, A. McCallum. Monte Carlo MCMC: Efficient Inference by Sampling Factors. NAACL/HLT Workshop on Automated Knowledge Base Construction (AKBC-WEKEX). 2012Workshop
    Coming Soon!
    @inproceedings{mcmcmc:akbc12,
     author = {Sameer Singh and Michael Wick and Andrew McCallum},
     title = { Monte Carlo MCMC: Efficient Inference by Sampling Factors },
     booktitle = {NAACL/HLT Workshop on Automated Knowledge Base Construction (AKBC-WEKEX)},
     year = {2012}
    }
  • S. Singh, T. Graepel. Compiling Relational Database Schemata into Probabilistic Graphical Models. NIPS Workshop on Probabilistic Programming. 2012Workshop
    Coming Soon!
    @inproceedings{mldb:probprog12,
     author = {Sameer Singh and Thore Graepel},
     title = { Compiling Relational Database Schemata into Probabilistic Graphical Models },
     booktitle = {NIPS Workshop on Probabilistic Programming},
     year = {2012}
    }
  • S. Singh, G. Druck, A. McCallum. Constraint-Driven Training of Complex Models Using MCMC. Technical Report, University of Massachusetts Amherst, CMPSCI UM-CS-2012-032. 2012TechReport
    Coming Soon!
    @techreport{mcmcge:tr2012,
     author = {Sameer Singh and Gregory Druck and Andrew McCallum},
     title = { Constraint-Driven Training of Complex Models Using MCMC },
     institution = {University of Massachusetts Amherst, CMPSCI UM-CS-2012-032},
     year = {2012}
    }
2011
  • S. Singh, A. Subramanya, F. Pereira, A. McCallum. Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models. Association for Computational Linguistics (ACL). 2011Conference
    Best Talk Award at DARPA Machine Reading Project Kickoff.
    Coming Soon!
    @inproceedings{dcoref:acl11,
     author = {Sameer Singh and Amarnag Subramanya and Fernando Pereira and Andrew McCallum},
     title = { Large-Scale Cross-Document Coreference Using Distributed Inference and Hierarchical Models },
     booktitle = {Association for Computational Linguistics (ACL)},
     year = {2011}
    }
  • S. Singh, A. McCallum. Towards Asynchronous Distributed MCMC Inference for Large Graphical Models. Neural Information Processing Systems (NIPS) Workshop on Big Learning. 2011Workshop
    Coming Soon!
    @inproceedings{asyncmcmc:biglearn11,
     author = {Sameer Singh and Andrew McCallum},
     title = { Towards Asynchronous Distributed MCMC Inference for Large Graphical Models },
     booktitle = {Neural Information Processing Systems (NIPS) Workshop on Big Learning},
     year = {2011}
    }
  • S. Singh, B. Martin, A. McCallum. Inducing Value Sparsity for Parallel Inference in Tree-shaped Models. Neural Information Processing Systems (NIPS) Workshop on Computational Trade-offs in Statistical Learning (COST). 2011Workshop
    Coming Soon!
    @inproceedings{sparsebp:cost11,
     author = {Sameer Singh and Brian Martin and Andrew McCallum},
     title = { Inducing Value Sparsity for Parallel Inference in Tree-shaped Models },
     booktitle = {Neural Information Processing Systems (NIPS) Workshop on Computational Trade-offs in Statistical Learning (COST)},
     year = {2011}
    }
  • J. Kubica, S. Singh, D. Sorokina. Parallel Large-scale Feature Selection. Scaling Up Machine Learning, Cambridge University Press. 2011
    Coming Soon!
    @misc{parfs:suml11,
     author = {Jeremy Kubica and Sameer Singh and Daria Sorokina},
     title = { Parallel Large-scale Feature Selection },
     series = {Scaling Up Machine Learning, Cambridge University Press},
     year = {2011}
    }
2010
  • S. Singh, D. Hillard, C. Leggetter. Minimally-Supervised Extraction of Entities from Text Advertisements. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2010Conference
    Extraction of entities from ad creatives is an important problem that can benefit many computational advertising tasks. Supervised and semi-supervised solutions rely on labeled data which is expensive, time consuming, and difficult to procure for ad creatives. A small set of manually derived constraints on feature expectations over unlabeled data can be used to *partially* and *probabilistically* label large amounts of data. Utilizing recent work in constraint-based semi-supervised learning, this paper injects light weight supervision specified as these ``constraints'' into a semi-Markov conditional random field model of entity extraction in ad creatives. Relying solely on the constraints, the model is trained on a set of unlabeled ads using an online learning algorithm. We demonstrate significant accuracy improvements on a manually labeled test set as compared to a baseline dictionary approach. We also achieve accuracy that approaches a fully supervised classifier.
    @inproceedings{min:naacl10,
     author = {Sameer Singh and Dustin Hillard and Chris Leggetter},
     title = { Minimally-Supervised Extraction of Entities from Text Advertisements },
     booktitle = {Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2010}
    }
  • S. Singh, L. Yao, S. Riedel, A. McCallum. Constraint-Driven Rank-Based Learning for Information Extraction. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2010Conference
    Most learning algorithms for factor graphs require complete inference over the dataset or an instance before making an update to the parameters. SampleRank is a rank-based learning framework that alleviates this problem by updating the parameters during inference. Most semi-supervised learning algorithms also rely on the complete inference, i.e. calculating expectations or MAP configurations. We extend the SampleRank framework to the semi-supervised learning, avoiding these inference bottlenecks. Different approaches for incorporating unlabeled data and prior knowledge into this framework are explored. We evaluated our method on a standard information extraction dataset. Our approach outperforms the supervised method significantly and matches the result of the competing semi-supervised learning approach.Most learning algorithms for factor graphs require complete inference over the dataset or an instance before making an update to the parameters. SampleRank is a rank-based learning framework that alleviates this problem by updating the parameters during inference. Most semi-supervised learning algorithms also rely on the complete inference, i.e. calculating expectations or MAP configurations. We extend the SampleRank framework to the semi-supervised learning, avoiding these inference bottlenecks. Different approaches for incorporating unlabeled data and prior knowledge into this framework are explored. We evaluated our method on a standard information extraction dataset. Our approach outperforms the supervised method significantly and matches the result of the competing semi-supervised learning approach.
    @inproceedings{cons:naacl10,
     author = {Sameer Singh and Limin Yao and Sebastian Riedel and Andrew McCallum},
     title = { Constraint-Driven Rank-Based Learning for Information Extraction },
     booktitle = {Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
     year = {2010}
    }
  • S. Singh, A. Subramanya, F. Pereira, A. McCallum. Distributed MAP Inference for Undirected Graphical Models. Neural Information Processing Systems (NIPS) Workshop on Learning on Cores, Clusters, and Clouds (LCCC). 2010Workshop
    In this work, we distribute the MCMC-based MAP inference using the Map-Reduce framework. The variables are assigned randomly to machines, which leads to some factors that neighbor variables on separate machines. Parallel MCMC-chains are initiated using proposal distributions that only suggest local changes such that factors that lie across machines are not examined. After a fixed number of samples on each machine, we redistribute the variables amongst the machines to enable proposals across variables that were on different machines. To demonstrate the distribution strategy on a real-world information extraction application, we model the task of cross-document coreference.
    @inproceedings{distmap:lccc10,
     author = {Sameer Singh and Amarnag Subramanya and Fernando Pereira and Andrew McCallum},
     title = { Distributed MAP Inference for Undirected Graphical Models },
     booktitle = {Neural Information Processing Systems (NIPS) Workshop on Learning on Cores, Clusters, and Clouds (LCCC)},
     year = {2010}
    }
  • S. Singh, M. Wick, A. McCallum. Distantly Labeling Data for Large Scale Cross-Document Coreference. Technical Report, Computing Research Repository (CoRR) eprint arXiv:1005.4298. 2010TechReport
    Cross-document coreference, the problem of resolving entity mentions across multi-document collections, is crucial to automated knowledge base construction and data mining tasks. However, the scarcity of large labeled data sets has hindered supervised machine learning research for this task. In this paper we develop and demonstrate an approach based on ``distantly-labeling'' a data set from which we can train a discriminative cross-document coreference model. In particular we build a dataset of more than a million people mentions extracted from 3.5 years of New York Times articles, leverage Wikipedia for distant labeling with a generative model (and measure the reliability of such labeling); then we train and evaluate a conditional random field coreference model that has factors on cross-document entities as well as mention-pairs. This coreference model obtains high accuracy in resolving mentions and entities that are not present in the training data, indicating applicability to non-Wikipedia data. Given the large amount of data, our work is also an exercise demonstrating the scalability of our approach.
    @techreport{distantly:tr10,
     author = {Sameer Singh and Michael Wick and Andrew McCallum},
     title = { Distantly Labeling Data for Large Scale Cross-Document Coreference },
     institution = {Computing Research Repository (CoRR) eprint arXiv:1005.4298},
     year = {2010}
    }
2009
  • S. Singh, K. Schultz, A. McCallum. Bi-directional Joint Inference for Entity Resolution and Segmentation using Imperatively-Defined Factor Graphs. Machine Learning and Knowledge Discovery in Databases (Lecture Notes in Computer Science) and European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). 2009Conference
    Coming Soon!
    @inproceedings{bidirectional:ecml09,
     author = {Sameer Singh and Karl Schultz and Andrew McCallum},
     title = { Bi-directional Joint Inference for Entity Resolution and Segmentation using Imperatively-Defined Factor Graphs },
     booktitle = {Machine Learning and Knowledge Discovery in Databases (Lecture Notes in Computer Science) and European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)},
     year = {2009}
    }
  • S. Singh, J. Kubica, S. E. Larsen, D. Sorokina. Parallel Large Scale Feature Selection for Logistic Regression. SIAM International Conference on Data Mining (SDM). 2009Conference
    Coming Soon!
    @inproceedings{parallel:sdm09,
     author = {Sameer Singh and Jeremy Kubica and Scott E. Larsen and Daria Sorokina},
     title = { Parallel Large Scale Feature Selection for Logistic Regression },
     booktitle = {SIAM International Conference on Data Mining (SDM)},
     year = {2009}
    }
  • A. McCallum, K. Schultz, S. Singh. FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs. Neural Information Processing Systems (NIPS). 2009Conference
    Coming Soon!
    @inproceedings{factorie:nips09,
     author = {Andrew McCallum and Karl Schultz and Sameer Singh},
     title = { FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs },
     booktitle = {Neural Information Processing Systems (NIPS)},
     year = {2009}
    }
  • M. Wick, K. Rohanimanesh, S. Singh, A. McCallum. Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference. Neural Information Processing Systems (NIPS). 2009Conference
    Coming Soon!
    @inproceedings{rlmap:nips09,
     author = {Michael Wick and Khashyar Rohanimanesh and Sameer Singh and Andrew McCallum},
     title = { Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference },
     booktitle = {Neural Information Processing Systems (NIPS)},
     year = {2009}
    }
  • S. Singh. Option Discovery in Hierarchical Reinforcement Learning for Training Large Factor Graphs for Information Extraction. University of Massachusetts Amherst, PhD Candidacy/Synthesis Report. 2009
    Readers: Andy Barto and Andrew McCallum
    Coming Soon!
    @misc{option:synth09,
     author = {Sameer Singh},
     title = { Option Discovery in Hierarchical Reinforcement Learning for Training Large Factor Graphs for Information Extraction },
     series = {University of Massachusetts Amherst, PhD Candidacy/Synthesis Report},
     year = {2009}
    }
2008
  • A. McCallum, K. Rohanimanesh, M. Wick, K. Schultz, S. Singh. FACTORIE: Efficient Probabilistic Programming via Imperative Declarations of Structure, Inference and Learning. NIPS Workshop on Probabilistic Programming. 2008Workshop
    Coming Soon!
    @inproceedings{factorie:nipsws08,
     author = {Andrew McCallum and Khashyar Rohanimanesh and Michael Wick and Karl Schultz and Sameer Singh},
     title = { FACTORIE: Efficient Probabilistic Programming via Imperative Declarations of Structure, Inference and Learning },
     booktitle = {NIPS Workshop on Probabilistic Programming},
     year = {2008}
    }
  • K. Rohanimanesh, M. Wick, S. Singh, A. McCallum. Reinforcement Learning for MAP Inference in Large Factor Graphs. Technical Report, University of Massachusetts Amherst, CMPSCI UM-CS-2008-040. 2008TechReport
    Coming Soon!
    @techreport{rlmap:tr08,
     author = {Khashyar Rohanimanesh and Michael Wick and Sameer Singh and Andrew McCallum},
     title = { Reinforcement Learning for MAP Inference in Large Factor Graphs },
     institution = {University of Massachusetts Amherst, CMPSCI UM-CS-2008-040},
     year = {2008}
    }
  • S. Singh, E. S. Larsen, J. Kubica, A. W. Moore. Feature selection for large scale models. US Patent Number 8190537. 2008
    Coming Soon!
    @techreport{feature:patent08,
     author = {Sameer Singh and E. S. Larsen and Jeremy Kubica and Andrew W. Moore},
     title = { Feature selection for large scale models },
     institution = {US Patent Number 8190537},
     year = {2008}
    }
2007
  • D. Feitelson, T. Adeshiyan, D. Balasubramanian, Y. Etsion, G. Madl, E. Osses, S. Singh, K. Suwanmongkol, M. Xie, S. Schach. Fine-Grain Analysis of Common Coupling and its Application to a Linux Case Study. Journal of Systems and Software (JSS). 2007Journal
    Coming Soon!
    @article{jss07,
     author = {D.G. Feitelson and T.O.S. Adeshiyan and D. Balasubramanian and Y. Etsion and G. Madl and E.P. Osses and Sameer Singh and K. Suwanmongkol and M. Xie and S.R. Schach},
     title = { Fine-Grain Analysis of Common Coupling and its Application to a Linux Case Study },
     journal = {Journal of Systems and Software (JSS)},
     volume = {80},
     year = {2007}
    }
  • S. Schach, T. Adeshiyan, D. Balasubramanian, G. Madl, E. Osses, S. Singh, K. Suwanmongkol, M. Xie, D. Feitelson. Common Coupling and Pointer Variables, with Application to a Linux Case Study. Software Quality Journal (SQJ). 2007Journal
    Coming Soon!
    @article{sqj07,
     author = {S.R. Schach and T.O.S. Adeshiyan and D. Balasubramanian and G. Madl and E.P. Osses and Sameer Singh and K. Suwanmongkol and M. Xie and D.G. Feitelson},
     title = { Common Coupling and Pointer Variables, with Application to a Linux Case Study },
     journal = {Software Quality Journal (SQJ)},
     volume = {15},
     year = {2007}
    }
  • T. Kichkaylo, C. v. Buskirk, S. Singh, H. Neema, M. Orosz, R. Neches. Mixed-Initiative Planning for Space Exploration Missions. International Conference on Automated Planning and Scheduling Workshop (ICAPS). 2007Workshop
    Coming Soon!
    @inproceedings{icaps07,
     author = {T. Kichkaylo and C. van Buskirk and Sameer Singh and H. Neema and M. Orosz and R. Neches},
     title = { Mixed-Initiative Planning for Space Exploration Missions },
     booktitle = {International Conference on Automated Planning and Scheduling Workshop (ICAPS)},
     year = {2007}
    }
2006
  • S. Singh, J. A. Adams. Transfer of Learning for Complex Domains: A Demonstration Using Multiple Robots. International Conference on Robotics and Automation (ICRA). 2006Conference
    Coming Soon!
    @inproceedings{icra06,
     author = {Sameer Singh and Julie A. Adams},
     title = { Transfer of Learning for Complex Domains: A Demonstration Using Multiple Robots },
     booktitle = {International Conference on Robotics and Automation (ICRA)},
     year = {2006}
    }
2003
  • S. Singh. Finding the shortest path for a mobile robot in an unmapped maze from minimum runs. Int Conf on CAD, CAM, Robotics and Autonomous Factories (INCARF). 2003Conference
    Coming Soon!
    @inproceedings{incarf03,
     author = {Sameer Singh},
     title = { Finding the shortest path for a mobile robot in an unmapped maze from minimum runs },
     booktitle = {Int Conf on CAD, CAM, Robotics and Autonomous Factories (INCARF)},
     year = {2003}
    }