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David Jensen

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8 papers

1 author row

AAMAS Conference 2025 Conference Paper

Open-World Classification with Bayesian Gaussian Mixture Models

Justin Clarke
Przemyslaw Grabowicz
David Jensen

Methods for solving classification tasks often assume a data generating process with stable structure that remains fixed during both training and inference. However, autonomous agents deployed in real-world environments often perform classification in situations where the data generating process is dynamic and the ontology of classes is only partially known. Such tasks are known as openworld classification (OWC). We present open-world mixture modeling (OMM), a framework for OWC using Bayesian Gaussian mixture models. With only slight modifications to the standard Bayesian variational inference algorithm, we are able to detect and model novel classes as they appear in a data stream, while maintaining and updating the classes learned during training. Empirical evaluations reveal that the method reliably detects novel classes with performance similar to a supervised classifier trained on labeled samples of the novel classes.

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NeurIPS Conference 2019 Conference Paper

The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data

Amanda Gentzel
Dan Garant
David Jensen

Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of impressive technical advances. However, evaluation techniques for causal modeling algorithms have remained somewhat primitive, limiting what we can learn from experimental studies of algorithm performance, constraining the types of algorithms and model representations that researchers consider, and creating a gap between theory and practice. We argue for more frequent use of evaluation techniques that examine interventional measures rather than structural or observational measures, and that evaluate those measures on empirical data rather than synthetic data. We survey the current practice in evaluation and show that the techniques we recommend are rarely used in practice. We show that such techniques are feasible and that data sets are available to conduct such evaluations. We also show that these techniques produce substantially different results than using structural measures and synthetic data.

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AAAI Conference 2015 Conference Paper

Learning to Uncover Deep Musical Structure

Phillip Kirlin
David Jensen

The overarching goal of music theory is to explain the inner workings of a musical composition by examining the structure of the composition. Schenkerian music theory supposes that Western tonal compositions can be viewed as hierarchies of musical objects. The process of Schenkerian analysis reveals this hierarchy by identifying connections between notes or chords of a composition that illustrate both the small- and large-scale construction of the music. We present a new probabilistic model of this variety of music analysis, details of how the parameters of the model can be learned from a corpus, an algorithm for deriving the most probable analysis for a given piece of music, and both quantitative and human-based evaluations of the algorithm’s performance. This represents the first large-scale data-driven computational approach to hierarchical music analysis.

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AAAI Conference 2011 Conference Paper

Relational Blocking for Causal Discovery

Matthew Rattigan
Marc Maier
David Jensen

Blocking is a technique commonly used in manual statistical analysis to account for confounding variables. However, blocking is not currently used in automated learning algorithms. These algorithms rely solely on statistical conditioning as an operator to identify conditional independence. In this work, we present relational blocking as a new operator that can be used for learning the structure of causal models. We describe how blocking is enabled by relational data sets, where blocks are determined by the links in the network. By blocking on entities rather than conditioning on variables, relational blocking can account for both measured and unobserved variables. We explain the mechanism of these methods using graphical models and the semantics of dseparation. Finally, we demonstrate the effectiveness of relational blocking for use in causal discovery by showing how blocking can be used in the causal analysis of two real-world social media systems.

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AAAI Conference 2010 Conference Paper

Learning Causal Models of Relational Domains

Marc Maier
Brian Taylor
Huseyin Oktay
David Jensen

Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge representations for propositional domains. In this paper, we present several key algorithmic and theoretical innovations that extend causal discovery to relational domains. We provide strong evidence that effective learning of causal models is enhanced by relational representations. We present an algorithm, relational PC, that learns causal dependencies in a state-of-the-art relational representation, and we identify the key representational and algorithmic innovations that make the algorithm possible. Finally, we prove the algorithm’s theoretical correctness and demonstrate its effectiveness on synthetic and real data sets.

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JMLR Journal 2007 Journal Article

Relational Dependency Networks

Jennifer Neville
David Jensen

Recent work on graphical models for relational data has demonstrated significant improvements in classification and inference when models represent the dependencies among instances. Despite its use in conventional statistical models, the assumption of instance independence is contradicted by most relational data sets. For example, in citation data there are dependencies among the topics of a paper's references, and in genomic data there are dependencies among the functions of interacting proteins. In this paper, we present relational dependency networks (RDNs), graphical models that are capable of expressing and reasoning with such dependencies in a relational setting. We discuss RDNs in the context of relational Bayes networks and relational Markov networks and outline the relative strengths of RDNs---namely, the ability to represent cyclic dependencies, simple methods for parameter estimation, and efficient structure learning techniques. The strengths of RDNs are due to the use of pseudolikelihood learning techniques, which estimate an efficient approximation of the full joint distribution. We present learned RDNs for a number of real-world data sets and evaluate the models in a prediction context, showing that RDNs identify and exploit cyclic relational dependencies to achieve significant performance gains over conventional conditional models. In addition, we use synthetic data to explore model performance under various relational data characteristics, showing that RDN learning and inference techniques are accurate over a wide range of conditions. [abs] [ pdf ][ bib ] &copy JMLR 2007. ( edit, beta )

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AAAI Conference 1999 Conference Paper

Learning Quantitative Knowledge for Multiagent Coordination

David Jensen
Michael Atighetchi
Régis Vincent
Victor Lesser
University of Massachusetts at Amherst

A central challenge of multiagent coordination is reasoning about howthe actions of one agent affect the actions of another. Knowledge of these interrelationships can help coordinate agents -- preventing conflicts and exploiting beneficial relationships among actions. Weexplore three interlocking methods that learn quantitative knowledge of such non-local effects in T/EMS, a well-developed frameworkfor multiagent coordination. Thesurprising simplicity and effectiveness of these methods demonstrates howagents can learn domain-specificknowledge quickly, extendingthe utility of coordination frameworks that explicitly represent coordination knowledge.

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AAAI Conference 1999 Conference Paper

Toward a Theoretical Understanding of Why and When Decision Tree Pruning Algorithms Fail

Tim Oates
David Jensen
University of Massachusetts

Recent empirical studies revealed two surprising pathologies of several common decision tree pruning algorithms. First, tree size is often a linear function of training set size, evenwhenadditional tree structure yields no increase in accuracy. Second, building trees with data in whichthe class label and the attributes are independentoften results in large trees. In both cases, the pruning algorithms fail to control tree growth as one would expect themto. Weexplore this behaviortheoretically byconstructinga statistical modelof reduced error pruning. The model explains whyand whenthe pathologies occur, and makespredictions about howto lessen their effects. Thepredictions are operationalized in a variant of reducederror pruningthat is shownto control tree growthfar better than the original algorithm.

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