Lise Getoor

NeSy Conference 2025 Conference Paper

Neural-Symbolic Architectural Axioms of Integration: A Manifesto

• Connor Pryor
• Lise Getoor

The integration of neural and symbolic methods has long been viewed as a promising path toward more general, interpretable, and robust artificial intelligence. The past two decades have seen a rapid proliferation of neural-symbolic (NeSy) systems, spanning a wide range of architectures, reasoning strategies, and application domains. However, this growth has outpaced theoretical clarity: many existing approaches conflate the roles of learning, inference, and representation, leading to a fragmented field lacking principled foundations. In this work, we address this gap by proposing a set of architectural axioms of integration—formal, implementation-agnostic principles that define how neural and symbolic components can be coherently combined. These axioms abstract away from system-specific details and instead characterize the structural interface between neural perception and symbolic reasoning. Rather than introducing a new method, this work offers a foundation to organize, compare, and reason about the rapidly expanding space of NeSy approaches.

ICML Conference 2024 Conference Paper

Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning

• Charles Dickens
• Changyu Gao
• Connor Pryor
• Stephen J. Wright 0001
• Lise Getoor

We leverage convex and bilevel optimization techniques to develop a general gradient-based parameter learning framework for neural-symbolic (NeSy) systems. We demonstrate our framework with NeuPSL, a state-of-the-art NeSy architecture. To achieve this, we propose a smooth primal and dual formulation of NeuPSL inference and show learning gradients are functions of the optimal dual variables. Additionally, we develop a dual block coordinate descent algorithm for the new formulation that naturally exploits warm-starts. This leads to over $100 \times$ learning runtime improvements over the current best NeuPSL inference method. Finally, we provide extensive empirical evaluations across $8$ datasets covering a range of tasks and demonstrate our learning framework achieves up to a $16$% point prediction performance improvement over alternative learning methods.

ICML Conference 2023 Conference Paper

ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation

• Kaiwen Zhou 0002
• Kaizhi Zheng
• Connor Pryor
• Yilin Shen
• Hongxia Jin
• Lise Getoor
• Xin Eric Wang

The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments. In this work, we present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC), that transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience nor any other training on the visual environments. First, ESC leverages a pre-trained vision and language model for open-world prompt-based grounding and a pre-trained commonsense language model for room and object reasoning. Then ESC converts commonsense knowledge into navigation actions by modeling it as soft logic predicates for efficient exploration. Extensive experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines, and achieves new state-of-the-art results for zero-shot object navigation (e. g. , 288% relative Success Rate improvement than CoW on MP3D).

IJCAI Conference 2023 Conference Paper

NeuPSL: Neural Probabilistic Soft Logic

• Connor Pryor
• Charles Dickens
• Eriq Augustine
• Alon Albalak
• William Yang Wang
• Lise Getoor

In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural and symbolic representations, we propose a family of energy-based models, NeSy Energy-Based Models, and show that they are general enough to include NeuPSL and many other NeSy approaches. Using this framework, we show how to seamlessly integrate neural and symbolic parameter learning and inference in NeuPSL. Through an extensive empirical evaluation, we demonstrate the benefits of using NeSy methods, achieving upwards of 30% improvement over independent neural network models. On a well-established NeSy task, MNIST-Addition, NeuPSL demonstrates its joint reasoning capabilities by outperforming existing NeSy approaches by up to 10% in low-data settings. Furthermore, NeuPSL achieves a 5% boost in performance over state-of-the-art NeSy methods in a canonical citation network task with up to a 40 times speed up.

UAI Conference 2022 Conference Paper

Learning explainable templated graphical models

• Varun Embar
• Sriram Srinivasan 0004
• Lise Getoor

Templated graphical models (TGMs) encode model structure using rules that capture recurring relationships between multiple random variables. While the rules in TGMs are interpretable, it is not clear how they can be used to generate explanations for the individual predictions of the model. Further, learning these rules from data comes with high computational costs: it typically requires an expensive combinatorial search over the space of rules and repeated optimization over rule weights. In this work, we propose a new structure learning algorithm, Explainable Structured Model Search (ESMS), that learns a templated graphical model and an explanation framework for its predictions. ESMS uses a novel search procedure to efficiently search the space of models and discover models that trade-off predictive accuracy and explainability. We introduce the notion of relational stability and prove that our proposed explanation framework is stable. Further, our proposed piecewise pseudolikelihood (PPLL) objective does not require re-optimizing the rule weights across models during each iteration of the search. In our empirical evaluation on three realworld datasets, we show that our proposed approach not only discovers models that are explainable, but also significantly outperforms existing state-out-the-art structure learning approaches.

NeSy Conference 2022 Conference Paper

Visual Sudoku Puzzle Classification: A Suite of Collective Neuro-Symbolic Tasks

• Eriq Augustine
• Connor Pryor
• Charles Dickens
• Jay Pujara
• William Yang Wang
• Lise Getoor

Neuro-symbolic computing (NeSy) is an emerging field that has the goal of integrating the low-level representational power of deep neural networks with high-level symbolic reasoning. Due to the youth of the field and the complexity of neuro-symbolic integration, there are few benchmarks that showcase the powers of NeSy, and even fewer built specifically with NeSy in mind. To address the lack of NeSy benchmarks, we introduce Visual Sudoku Puzzle Classification (ViSudo-PC). ViSudo-PC is a new NeSy benchmark dataset combining visual perception with relational constraints. The goal of the benchmark is to both highlight opportunities and elicit challenges. In addition to providing a new NeSy benchmark suite, we also provide an exploratory analysis that showcases ViSudo-PC’s difficulty and possibilities.

ICML Conference 2021 Conference Paper

Context-Aware Online Collective Inference for Templated Graphical Models

• Charles Dickens
• Connor Pryor
• Eriq Augustine
• Alexander Miller
• Lise Getoor

In this work, we examine online collective inference, the problem of maintaining and performing inference over a sequence of evolving graphical models. We utilize templated graphical models (TGM), a general class of graphical models expressed via templates and instantiated with data. A key challenge is minimizing the cost of instantiating the updated model. To address this, we define a class of exact and approximate context-aware methods for updating an existing TGM. These methods avoid a full re-instantiation by using the context of the updates to only add relevant components to the graphical model. Further, we provide stability bounds for the general online inference problem and regret bounds for a proposed approximation. Finally, we implement our approach in probabilistic soft logic, and test it on several online collective inference tasks. Through these experiments we verify the bounds on regret and stability, and show that our approximate online approach consistently runs two to five times faster than the offline alternative while, surprisingly, maintaining the quality of the predictions.

NeurIPS Conference 2021 Conference Paper

Local Explanation of Dialogue Response Generation

• Yi-Lin Tuan
• Connor Pryor
• Wenhu Chen
• Lise Getoor
• William Yang Wang

In comparison to the interpretation of classification models, the explanation of sequence generation models is also an important problem, however it has seen little attention. In this work, we study model-agnostic explanations of a representative text generation task -- dialogue response generation. Dialog response generation is challenging with its open-ended sentences and multiple acceptable responses. To gain insights into the reasoning process of a generation model, we propose a new method, local explanation of response generation (LERG) that regards the explanations as the mutual interaction of segments in input and output sentences. LERG views the sequence prediction as uncertainty estimation of a human response and then creates explanations by perturbing the input and calculating the certainty change over the human response. We show that LERG adheres to desired properties of explanations for text generation including unbiased approximation, consistency and cause identification. Empirically, our results show that our method consistently improves other widely used methods on proposed automatic- and human- evaluation metrics for this new task by $4. 4$-$12. 8$\%. Our analysis demonstrates that LERG can extract both explicit and implicit relations between input and output segments.

AAAI Conference 2020 Conference Paper

BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic

• Sriram Srinivasan
• Golnoosh Farnadi
• Lise Getoor

Probabilistic soft logic (PSL) is a statistical relational learning framework that represents complex relational models with weighted first-order logical rules. The weights of the rules in PSL indicate their importance in the model and in- fluence the effectiveness of the model on a given task. Existing weight learning approaches often attempt to learn a set of weights that maximizes some function of data likelihood. However, this does not always translate to optimal performance on a desired domain metric, such as accuracy or F1 score. In this paper, we introduce a new weight learning approach called Bayesian optimization for weight learning (BOWL) based on Gaussian process regression that directly optimizes weights on a chosen domain performance metric. The key to the success of our approach is a novel projection that captures the semantic distance between the possible weight configurations. Our experimental results show that our proposed approach outperforms likelihood-based approaches and yields up to a 10% improvement across a variety of performance metrics. Further, we performed experiments to measure the scalability and robustness of our approach on various realworld datasets.

AAAI Conference 2020 Conference Paper

Tandem Inference: An Out-of-Core Streaming Algorithm for Very Large-Scale Relational Inference

• Sriram Srinivasan
• Eriq Augustine
• Lise Getoor

Statistical relational learning (SRL) frameworks allow users to create large, complex graphical models using a compact, rule-based representation. However, these models can quickly become prohibitively large and not fit into machine memory. In this work we address this issue by introducing a novel technique called tandem inference (TI). The primary idea of TI is to combine grounding and inference such that both processes happen in tandem. TI uses an out-of-core streaming approach to overcome memory limitations. Even when memory is not an issue, we show that our proposed approach is able to do inference faster while using less memory than existing approaches. To show the effectiveness of TI, we use a popular SRL framework called Probabilistic Soft Logic (PSL). We implement TI for PSL by proposing a gradient-based inference engine and a streaming approach to grounding. We show that we are able to run an SRL model with over 1B cliques in under nine hours and using only 10 GB of RAM; previous approaches required more than 800 GB for this model and are infeasible on common hardware. To the best of our knowledge, this is the largest SRL model ever run.

IJCAI Conference 2019 Conference Paper

Estimating Causal Effects of Tone in Online Debates

• Dhanya Sridhar
• Lise Getoor

Statistical methods applied to social media posts shed light on the dynamics of online dialogue. For example, users' wording choices predict their persuasiveness and users adopt the language patterns of other dialogue participants. In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses. The challenge for this estimation is that a reply's tone and subsequent responses are confounded by the users' ideologies on the debate topic and their emotions. To overcome this challenge, we learn representations of ideology using generative models of text. We study debates from 4Forums. com and compare annotated tones of replying such as emotional versus factual, or reasonable versus attacking. We show that our latent confounder representation reduces bias in ATE estimation. Our results suggest that factual and asserting tones affect dialogue and provide a methodology for estimating causal effects from text.

AAAI Conference 2019 Conference Paper

Lifted Hinge-Loss Markov Random Fields

• Sriram Srinivasan
• Behrouz Babaki
• Golnoosh Farnadi
• Lise Getoor

Statistical relational learning models are powerful tools that combine ideas from first-order logic with probabilistic graphical models to represent complex dependencies. Despite their success in encoding large problems with a compact set of weighted rules, performing inference over these models is often challenging. In this paper, we show how to effectively combine two powerful ideas for scaling inference for large graphical models. The first idea, lifted inference, is a wellstudied approach to speeding up inference in graphical models by exploiting symmetries in the underlying problem. The second idea is to frame Maximum a posteriori (MAP) inference as a convex optimization problem and use alternating direction method of multipliers (ADMM) to solve the problem in parallel. A well-studied relaxation to the combinatorial optimization problem defined for logical Markov random fields gives rise to a hinge-loss Markov random field (HL- MRF) for which MAP inference is a convex optimization problem. We show how the formalism introduced for coloring weighted bipartite graphs using a color refinement algorithm can be integrated with the ADMM optimization technique to take advantage of the sparse dependency structures of HL- MRFs. Our proposed approach, lifted hinge-loss Markov random fields (LHL-MRFs), preserves the structure of the original problem after lifting and solves lifted inference as distributed convex optimization with ADMM. In our empirical evaluation on real-world problems, we observe up to a three times speed up in inference over HL-MRFs.

IJCAI Conference 2018 Conference Paper

Scalable Probabilistic Causal Structure Discovery

• Dhanya Sridhar
• Jay Pujara
• Lise Getoor

Complex causal networks underlie many real-world problems, from the regulatory interactions between genes to the environmental patterns used to understand climate change. Computational methods seek to infer these causal networks using observational data and domain knowledge. In this paper, we identify three key requirements for inferring the structure of causal networks for scientific discovery: (1) robustness to noise in observed measurements; (2) scalability to handle hundreds of variables; and (3) flexibility to encode domain knowledge and other structural constraints. We first formalize the problem of joint probabilistic causal structure discovery. We develop an approach using probabilistic soft logic (PSL) that exploits multiple statistical tests, supports efficient optimization over hundreds of variables, and can easily incorporate structural constraints, including imperfect domain knowledge. We compare our method against multiple well-studied approaches on biological and synthetic datasets, showing improvements of up to 20% in F1-score over the best performing baseline in realistic settings.

IJCAI Conference 2017 Conference Paper

Disambiguating Energy Disaggregation: A Collective Probabilistic Approach

• Sabina Tomkins
• Jay Pujara
• Lise Getoor

Reducing household energy usage is a priority for improving the resiliency and stability of the power grid and decreasing the negative impact of energy consumption on the environment and public health. Relevant and timely feedback about the power consumption of specific appliances can help household residents to reduce their energy demand. Given only a total energy reading, such as that collected from a residential meter, energy disaggregation strives to discover the consumption of individual appliances. Existing disaggregation algorithms are computationally inefficient and rely heavily on high-resolution ground truth data. We introduce a probabilistic framework which infers the energy consumption of individual appliances using a hinge-loss Markov random field (HL-MRF), which admits highly scalable inference. To further enhance efficiency, we introduce a temporal representation which leverages state duration. We also explore how contextual information impacts solution quality with low-resolution data. Our framework is flexible in its ability to incorporate additional constraints; by constraining appliance usage with context and duration we can better disambiguate appliances with similar energy consumption profiles. We demonstrate the effectiveness of our framework on two public real-world datasets, reducing the error relative to a previous state-of-the-art method by as much as 50%.

JMLR Journal 2017 Journal Article

Hinge-Loss Markov Random Fields and Probabilistic Soft Logic

• Stephen H. Bach
• Matthias Broecheler
• Bert Huang
• Lise Getoor

A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to images, video, and natural language. In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data. The first, hinge-loss Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model that generalizes different approaches to convex inference. We unite three approaches from the randomized algorithms, probabilistic graphical models, and fuzzy logic communities, showing that all three lead to the same inference objective. We then define HL- MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We introduce an algorithm for inferring most-probable variable assignments (MAP inference) that is much more scalable than general-purpose convex optimization methods, because it uses message passing to take advantage of sparse dependency structures. We then show how to learn the parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous discrete models, but much more scalable. Together, these algorithms enable HL-MRFs and PSL to model rich, structured data at scales not previously possible. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

JMLR Journal 2016 Journal Article

Stability and Generalization in Structured Prediction

• Ben London
• Bert Huang
• Lise Getoor

Structured prediction models have been found to learn effectively from a few large examples--- sometimes even just one. Despite empirical evidence, canonical learning theory cannot guarantee generalization in this setting because the error bounds decrease as a function of the number of examples. We therefore propose new PAC-Bayesian generalization bounds for structured prediction that decrease as a function of both the number of examples and the size of each example. Our analysis hinges on the stability of joint inference and the smoothness of the data distribution. We apply our bounds to several common learning scenarios, including max-margin and soft-max training of Markov random fields. Under certain conditions, the resulting error bounds can be far more optimistic than previous results and can even guarantee generalization from a single large example. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

UAI Conference 2015 Conference Paper

Budgeted Online Collective Inference

• Jay Pujara
• Ben London 0001
• Lise Getoor

Updating inference in response to new evidence is a fundamental challenge in artificial intelligence. Many real problems require large probabilistic graphical models, containing millions of interdependent variables. For such large models, jointly updating the most likely (i. e. , MAP) configuration of the variables each time new evidence is encountered can be infeasible, even if inference is tractable. In this paper, we introduce budgeted online collective inference, in which the MAP configuration of a graphical model is updated efficiently by revising the assignments to a subset of the variables while holding others fixed. The goal is to selectively update certain variables without sacrificing quality with respect to full inference. To formalize the consequences of partially updating inference, we introduce the concept of inference regret. We derive inference regret bounds for a class of graphical models with strongly-convex free energies. These theoretical insights, combined with a thorough analysis of the optimization solver, motivate new approximate methods for efficiently updating the variable assignments under a budget constraint. In experiments, we demonstrate that our algorithms can reduce inference time by 65% with accuracy comparable to full inference.

ICML Conference 2015 Conference Paper

HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades

• Xinran He
• Theodoros Rekatsinas
• James R. Foulds
• Lise Getoor
• Yan Liu 0002

Understanding the diffusion of information in social network and social media requires modeling the text diffusion process. In this work, we develop the HawkesTopic model (HTM) for analyzing text-based cascades, such as "retweeting a post" or "publishing a follow-up blog post". HTM combines Hawkes processes and topic modeling to simultaneously reason about the information diffusion pathways and the topics characterizing the observed textual information. We show how to jointly infer them with a mean-field variational inference algorithm and validate our approach on both synthetic and real-world data sets, including a news media dataset for modeling information diffusion, and an ArXiv publication dataset for modeling scientific influence. The results show that HTM is significantly more accurate than several baselines for both tasks.

ICML Conference 2015 Conference Paper

Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models

• James R. Foulds
• Shachi H. Kumar
• Lise Getoor

Topic models have become increasingly prominent text-analytic machine learning tools for research in the social sciences and the humanities. In particular, custom topic models can be developed to answer specific research questions. The design of these models requires a non-trivial amount of effort and expertise, motivating general-purpose topic modeling frameworks. In this paper we introduce latent topic networks, a flexible class of richly structured topic models designed to facilitate applied research. Custom models can straightforwardly be developed in our framework with an intuitive first-order logical probabilistic programming language. Latent topic networks admit scalable training via a parallelizable EM algorithm which leverages ADMM in the M-step. We demonstrate the broad applicability of the models with case studies on modeling influence in citation networks, and U. S. Presidential State of the Union addresses.

IS Journal 2015 Journal Article

Model-Based Forecasting of Significant Societal Events

• Naren Ramakrishnan
• Chang-Tien Lu
• Madhav Marathe
• Achla Marathe
• Anil Vullikanti
• Stephen Eubank
• Scotland Leman
• Michael Roan

The article outlines some salient aspects of Embers-generated forecasts through its design considerations, system architecture, and user interface.

ICML Conference 2015 Conference Paper

Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs

• Stephen H. Bach
• Bert Huang
• Jordan L. Boyd-Graber
• Lise Getoor

Latent variables allow probabilistic graphical models to capture nuance and structure in important domains such as network science, natural language processing, and computer vision. Naive approaches to learning such complex models can be prohibitively expensive—because they require repeated inferences to update beliefs about latent variables—so lifting this restriction for useful classes of models is an important problem. Hinge-loss Markov random fields (HL-MRFs) are graphical models that allow highly scalable inference and learning in structured domains, in part by representing structured problems with continuous variables. However, this representation leads to challenges when learning with latent variables. We introduce paired-dual learning, a framework that greatly speeds up training by using tractable entropy surrogates and avoiding repeated inferences. Paired-dual learning optimizes an objective with a pair of dual inference problems. This allows fast, joint optimization of parameters and dual variables. We evaluate on social-group detection, trust prediction in social networks, and image reconstruction, finding that paired-dual learning trains models as accurate as those trained by traditional methods in much less time, often before traditional methods make even a single parameter update.

ICML Conference 2015 Conference Paper

The Benefits of Learning with Strongly Convex Approximate Inference

• Ben London 0001
• Bert Huang
• Lise Getoor

We explore the benefits of strongly convex free energies in variational inference, providing both theoretical motivation and a new meta-algorithm. Using the duality between strong convexity and stability, we prove a high-probability bound on the error of learned marginals that is inversely proportional to the modulus of convexity of the free energy, thereby motivating free energies whose moduli are constant with respect to the size of the graph. We identify sufficient conditions for Ω(1)-strong convexity in two popular variational techniques: tree-reweighted and counting number entropies. Our insights for the latter suggest a novel counting number optimization framework, which guarantees strong convexity for any given modulus. Our experiments demonstrate that learning with a strongly convex free energy, using our optimization framework to guarantee a given modulus, results in substantially more accurate marginal probabilities, thereby validating our theoretical claims and the effectiveness of our framework.

AAAI Conference 2014 Conference Paper

Learning Latent Engagement Patterns of Students in Online Courses

• Arti Ramesh
• Dan Goldwasser
• Bert Huang
• Hal Daume III
• Lise Getoor

Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement will help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interaction with the MOOC open up avenues for studying student engagement at scale. In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues. Our first contribution is the abstraction of student engagement types using latent representations. We use that abstraction in a probabilistic model to connect student behavior with course completion. We demonstrate that the latent formulation for engagement helps in predicting student survival across three MOOCs. Next, in order to initiate better instructor interventions, we need to be able to predict student survival early in the course. We demonstrate that we can predict student survival early in the course reliably using the latent model. Finally, we perform a closer quantitative analysis of user interaction with the MOOC and identify student activities that are good indicators for survival at different points in the course.

ICML Conference 2013 Conference Paper

Collective Stability in Structured Prediction: Generalization from One Example

• Ben London 0001
• Bert Huang
• Ben Taskar
• Lise Getoor

Structured predictors enable joint inference over multiple interdependent output variables. These models are often trained on a small number of examples with large internal structure. Existing distribution-free generalization bounds do not guarantee generalization in this setting, though this contradicts a large body of empirical evidence from computer vision, natural language processing, social networks and other fields. In this paper, we identify a set of natural conditions – weak dependence, hypothesis complexity and a new measure, collective stability – that are sufficient for generalization from even a single example, without imposing an explicit generative model of the data. We then demonstrate that the complexity and stability conditions are satisfied by a broad class of models, including marginal inference in templated graphical models. We thus obtain uniform convergence rates that can decrease significantly faster than previous bounds, particularly when each structured example is sufficiently large and the number of training examples is constant, even one.

UAI Conference 2013 Conference Paper

Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction

• Stephen H. Bach
• Bert Huang
• Ben London 0001
• Lise Getoor

Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. This paper demonstrates that HL- MRFs are general tools for fast and accurate structured prediction. We introduce the first inference algorithm that is both scalable and applicable to the full class of HL-MRFs, and show how to train HL-MRFs with several learning algorithms. Our experiments show that HL-MRFs match or surpass the predictive performance of state-of-the-art methods, including discrete models, in four application domains.

NeurIPS Conference 2012 Conference Paper

Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization

• Stephen Bach
• Matthias Broecheler
• Lise Getoor
• Dianne O'leary

Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensus-optimization framework and demonstrate their superior performance over state of the art. We show empirically that in a large-scale voter-preference modeling problem our algorithms scale linearly in the number of dependencies and constraints.

IJCAI Conference 2011 Conference Paper

Active Surveying: A Probabilistic Approach for Identifying Key Opinion Leaders

• Hossam Sharara
• Lise Getoor
• Myra Norton

Opinion leaders play an important role in influencing people's beliefs, actions and behaviors. Although a number of methods have been proposed for identifying influentials using secondary sources of information, the use of primary sources, such as surveys, is still favored in many domains. In this work we present a new surveying method which combines secondary data with partial knowledge from primary sources to guide the information gathering process. We apply our proposed active surveying method to the problem of identifying key opinion leaders in the medical field, and show how we are able to accurately identify the opinion leaders while minimizing the amount of primary data required, which results in significant cost reduction in data acquisition without sacrificing its integrity.

AAAI Conference 2010 Conference Paper

Active Inference for Collective Classification

• Mustafa Bilgic
• Lise Getoor

Labeling nodes in a network is an important problem that has seen a growing interest. A number of methods that exploit both local and relational information have been developed for this task. Acquiring the labels for a few nodes at inference time can greatly improve the accuracy, however the question of figuring out which node labels to acquire is challenging. Previous approaches have been based on simple structural properties. Here, we present a novel technique, which we refer to as reflect and correct, that can learn and predict when the underlying classification system is likely to make mistakes and it suggests acquisitions to correct those mistakes.

ICML Conference 2010 Conference Paper

Active Learning for Networked Data

• Mustafa Bilgic 0001
• Lilyana Mihalkova
• Lise Getoor

NeurIPS Conference 2010 Conference Paper

Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning

• Matthias Broecheler
• Lise Getoor

Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes. We prove that marginal computation for constrained continuous MRFs is #P-hard in general and present a polynomial-time approximation scheme under mild assumptions on the structure of the random field. Moreover, we introduce a sampling algorithm to compute marginal distributions and develop novel techniques to increase its efficiency. Continuous MRFs are a general purpose probabilistic modeling tool and we demonstrate how they can be applied to statistical relational learning. On the problem of collective classification, we evaluate our algorithm and show that the standard deviation of marginals serves as a useful measure of confidence.

UAI Conference 2010 Conference Paper

Probabilistic Similarity Logic

• Matthias Bröcheler
• Lilyana Mihalkova
• Lise Getoor

Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect–the need to reason about similarities–which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domainspecific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.

UAI Conference 2009 Conference Paper

Bisimulation-based Approximate Lifted Inference

• Prithviraj Sen
• Amol Deshpande
• Lise Getoor

There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the first-order model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic model to speedup inference. In particular, we describe approximate lifted inference techniques that allow the user to trade off inference accuracy for computational efficiency by using a handful of tunable parameters, while keeping the error bounded. Our algorithms are closely related to the graph-theoretic concept of bisimulation. We report experiments on both synthetic and real data to show that in the presence of symmetries, run-times for inference can be improved significantly, with approximate lifted inference providing orders of magnitude speedup over ground inference.

ICML Conference 2006 Conference Paper

Cost-sensitive learning with conditional Markov networks

• Prithviraj Sen
• Lise Getoor

AIIM Journal 2004 Journal Article

Understanding tuberculosis epidemiology using structured statistical models

• Lise Getoor
• Jeanne T Rhee
• Daphne Koller
• Peter Small

Molecular epidemiological studies can provide novel insights into the transmission of infectious diseases such as tuberculosis. Typically, risk factors for transmission are identified using traditional hypothesis-driven statistical methods such as logistic regression. However, limitations become apparent in these approaches as the scope of these studies expand to include additional epidemiological and bacterial genomic data. Here we examine the use of Bayesian models to analyze tuberculosis epidemiology. We begin by exploring the use of Bayesian networks (BNs) to identify the distribution of tuberculosis patient attributes (including demographic and clinical attributes). Using existing algorithms for constructing BNs from observational data, we learned a BN from data about tuberculosis patients collected in San Francisco from 1991 to 1999. We verified that the resulting probabilistic models did in fact capture known statistical relationships. Next, we examine the use of newly introduced methods for representing and automatically constructing probabilistic models in structured domains. We use statistical relational models (SRMs) to model distributions over relational domains. SRMs are ideally suited to richly structured epidemiological data. We use a data-driven method to construct a statistical relational model directly from data stored in a relational database. The resulting model reveals the relationships between variables in the data and describes their distribution. We applied this procedure to the data on tuberculosis patients in San Francisco from 1991 to 1999, their Mycobacterium tuberculosis strains, and data on contact investigations. The resulting statistical relational model corroborated previously reported findings and revealed several novel associations. These models illustrate the potential for this approach to reveal relationships within richly structured data that may not be apparent using conventional statistical approaches. We show that Bayesian methods, in particular statistical relational models, are an important tool for understanding infectious disease epidemiology.

ICML Conference 2003 Conference Paper

Link-based Classification

• Qing Lu
• Lise Getoor

ICML Conference 2001 Conference Paper

Learning Probabilistic Models of Relational Structure

• Lise Getoor
• Nir Friedman
• Daphne Koller
• Ben Taskar

UAI Conference 1998 Conference Paper

Utility Elicitation as a Classification Problem

• Urszula Chajewska
• Lise Getoor
• Joseph Norman
• Yuval Shahar

We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is long and tedious, particularly if the outcome space is large and not decomposable. There are two common approaches to utility function elicitation. The first is to base the determination of the users utility function solely ON elicitation OF qualitative preferences.The second makes assumptions about the form AND decomposability OF the utility function.Here we take a different approach: we attempt TO identify the new USERs utility function based on classification relative to a database of previously collected utility functions. We do this by identifying clusters of utility functions that minimize an appropriate distance measure. Having identified the clusters, we develop a classification scheme that requires many fewer and simpler assessments than full utility elicitation and is more robust than utility elicitation based solely on preferences. We have tested our algorithm on a small database of utility functions in a prenatal diagnosis domain and the results are quite promising.

AAAI Conference 1997 Conference Paper

Effective Redundant Constraints for Online Scheduling

• Lise Getoor
• Markus Fromherz

The use of heuristics as a means to improve constraint solver performance has been researched widely. However, most work has been on problem-independent heuristics (e. g. , variable and value ordering), and has focused on offline problems (e. g. , one-shot constraint satisfaction). In this paper, we present an online scheduling problem for which we are developing a real-tune scheduling algorithm. While we can and do use generic heuristics in the scheduler, here we focus on the use of domain-specific redundant constraints to effectively approximate optimal offline solutions. We present a taxonomy of redundant domain constraints, and examine their impact on the effectiveness of the scheduler. We also describe several techniques for generating redundant constraints, which can be applied to a large class of job shop scheduling problems.