@conference {key355, title = {BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic}, booktitle = {AAAI Conference on Artificial Intelligence (AAAI)}, year = {2020}, abstract = {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 influence 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 real world datasets.}, author = {Sriram Srinivasan and Golnoosh Farnadi and Lise Getoor} } @article {354, title = {A Declarative Approach to Fairness in Relational Domains}, journal = {IEEE Data Engineering Bulletin}, volume = {42}, year = {2019}, pages = {36--48}, abstract = {AI and machine learning tools are being used with increasing frequency for decision making in domains that affect peoples{\textquoteright} lives such as employment, education, policing and financial qualifications. These uses raise concerns about biases of algorithmic discrimination and have motivated the development of fairness-aware machine learning. However, existing fairness approaches are based solely on attributes of individuals. In many cases, discrimination is much more complex, and taking into account the social, organizational, and other connections between individuals is important. We introduce new notions of fairness that are able to capture the relational structure in a domain. We use first-order logic to provide a flexible and expressive language for specifying complex relational patterns of discrimination. Furthermore, we extend an existing statistical relational learning framework, probabilistic soft logic (PSL), to incorporate our definition of relational fairness. We refer to this fairness-aware framework FairPSL. FairPSL makes use of the logical definitions of fairnesss but also supports a probabilistic interpretation. In particular, we show how to perform maximum a posteriori (MAP) inference by exploiting probabilistic dependencies within the domain while avoiding violations of fairness guarantees. Preliminary empirical evaluation shows that we are able to make both accurate and fair decisions.}, author = {Golnoosh Farnadi and Behrouz Babaki and Lise Getoor} } @conference {341, title = {Lifted Hinge-Loss Markov Random Fields}, booktitle = {AAAI Conference on Artificial Intelligence (AAAI)}, year = {2019}, month = {11/2018}, abstract = {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 (HLMRF) 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 HLMRFs. 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.}, author = {Sriram Srinivasan and Behrouz Babaki and Golnoosh Farnadi and Lise Getoor} } @conference {337, title = {A Fairness-aware Hybrid Recommender System}, booktitle = {RecSys Workshop on Responsible Recommendation (FATREC)}, year = {2018}, abstract = {

Recommender systems are used in variety of domains affecting people{\textquoteright}s lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.

}, author = {Golnoosh Farnadi and Kouki, Pigi and Spencer K. Thompson and Sriram Srinivasan and Lise Getoor} } @conference {321, title = {Aligning Product Categories using Anchor Products}, booktitle = {Workshop on Knowledge Base Construction, Reasoning and Mining (KBCOM)}, year = {2018}, abstract = {

E-commerce sites group similar products into categories, and these categories are further organized in a taxonomy. Since different sites have different products and cater to a variety of shoppers, the taxonomies differ both in the categorization of products and the textual representation used for these categories. In this paper, we propose a technique to align categories across sites, which is useful information to have in product graphs. We use breadcrumbs present on the product pages to infer a site{\textquoteright}s taxonomy. We generate a list of candidate category pairs for alignment using anchor products products present in two or more sites. We use multiple similarity and distance metrics to compare these candidates. To generate the final set of alignments, we propose a model that combines these metrics with a set of structural constraints. The model is based on probabilistic soft logic (PSL), a scalable probabilistic programming framework. We run experiments on data extracted from Amazon, Ebay, Staples and Target, and show that the distance metric based on products, and the use of PSL to combine various metrics and structural constraints lead to improved alignments.

}, author = {Varun Embar and Golnoosh Farnadi and Jay Pujara and Lise Getoor} } @conference {322, title = {Fairness in Relational Domains}, booktitle = {Artificial Intelligence, Ethics, and Society (AIES)}, year = {2018}, abstract = {

AI and machine learning tools are being used with increasing frequency for decision making in domains that affect peoples{\textquoteright} lives such as employment, education, policing and loan approval. These uses raise concerns about biases of algorithmic discrimination and have motivated the development of fairness-aware machine learning. However, existing fairness approaches are based solely on attributes of individuals. In many cases, discrimination is much more complex, and taking into account the social, organizational, and other connections between individuals is important. We introduce new notions of fairness that are able to capture the relational structure in a domain. We use first-order logic to provide a flexible and expressive language for specifying complex relational patterns of discrimination. Furthermore, we extend an existing statistical relational learning framework, probabilistic soft logic (PSL), to incorporate our definition of relational fairness. We refer to this fairness-aware framework FairPSL. FairPSL makes use of the logical definitions of fairnesss but also supports a probabilistic interpretation. In particular, we show how to perform maximum a posteriori(MAP) inference by exploiting probabilistic dependencies within the domain while avoiding violation of fairness guarantees. Preliminary empirical evaluation shows that we are able to make both accurate and fair decisions.

}, author = {Golnoosh Farnadi and Behrouz Babaki and Lise Getoor} } @conference {318, title = {Fairness-aware Relational Learning and Inference}, booktitle = {AAAI Workshop on Declarative Learning Based Programming (DeLBP)}, year = {2018}, author = {Golnoosh Farnadi and Behrouz Babaki and Lise Getoor} } @article {336, title = {MLTrain: Collective Reasoning With Probabilistic Soft Logic}, year = {2018}, publisher = {Uncertainty in Artificial Intelligence (UAI)}, url = {https://github.com/linqs/psl-examples/tree/uai18}, author = {Eriq Augustine and Golnoosh Farnadi} } @conference {332, title = {Scalable Structure Learning for Probabilistic Soft Logic}, booktitle = {IJCAI Workshop on Statistical Relational AI (StarAI)}, year = {2018}, month = {06/2018}, abstract = {

Statistical relational frameworks such as Markov logic networks and probabilistic soft logic (PSL) encode model structure with weighted first-order logical clauses. Learning these clauses from data is referred to as structure learning. Structure learning alleviates the manual cost of specifying models. However, this benefit comes with high computational costs; structure learning typically requires an expensive search over the space of clauses which involves repeated optimization of clause weights. In this paper, we propose the first two approaches to structure learning for PSL. We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways. The highly scalable optimization method combines data-driven generation of clauses with a piecewise pseudolikelihood (PPLL) objective that learns model structure by optimizing clause weights only once. We compare both methods across five real-world tasks, showing that PPLL achieves an order of magnitude runtime speedup and AUC gains up to 15\% over greedy search.

}, author = {Varun Embar and Dhanya Sridhar and Golnoosh Farnadi and Lise Getoor} } @conference {334, title = {The Impact of Environmental Stressors on Human Trafficking}, booktitle = {ICWSM Workshop on Beyond Online Data (BOD)}, year = {2018}, abstract = {

Severe environmental events have extreme effects on all segments of society, including criminal activity. Extreme weather events, such as tropical storms, fires, and floods create instability in communities, and can be exploited by criminal organizations. Here we investigate the potential impact of catastrophic storms on the criminal activity of human trafficking. We propose three theories of how these catastrophic storms might impact trafficking and provide evidence for each. Researching human trafficking is made difficult by its illicit nature and the obscurity of high-quality data. Here, we analyze online advertisements for services which can be collected at scale and provide insights into traffickers{\textquoteright} behavior. To successfully combine relevant heterogenous sources of information, as well as spatial and temporal structure, we propose a collective, probabilistic approach. We implement this approach with Probabilistic Soft Logic, a probabilistic programming framework which can flexibly model relational structure and for which inference of future locations is highly efficient. Furthermore, this framework can be used to model hidden structure, such as latent links between locations. Our proposed approach can model and predict how traffickers move. In addition, we propose a model which learns connections between locations. This model is then adapted to have knowledge of environmental events, and we demonstrate that incorporating knowledge of environmental events can improve prediction of future locations. While we have validated our models on the impact of severe weather on human trafficking, we believe our models can be generalized to a variety of other settings in which environmental events impact human behavior.

}, author = {Tomkins, Sabina and Golnoosh Farnadi and Brian Amantullah and Lise Getoor and Steven Minton} } @conference {338, title = {The Impact of Environmental Stressors on Human Trafficking}, booktitle = {International Conference on Data Mining (ICDM)}, year = {2018}, abstract = {

{\textemdash}Severe environmental events have extreme effects on all segments of society, including criminal activity. Extreme weather events, such as tropical storms, fires, and floods create instability in communities, and can be exploited by criminal organizations. Here we investigate the potential impact of catastrophic storms on the criminal activity of human trafficking. We propose three theories of how these catastrophic storms might impact trafficking and provide evidence for each. Researching human trafficking is made difficult by its illicit nature and the obscurity of high-quality data. Here, we analyze online advertisements for services which can be collected at scale and provide insights into traffickers{\textquoteright} behavior. To successfully combine relevant heterogenous sources of information, as well as spatial and temporal structure, we propose a collective, probabilistic approach. We implement this approach with Probabilistic Soft Logic, a probabilistic programming framework which can flexibly model relational structure and for which inference of future locations is highly efficient. Furthermore, this framework can be used to model hidden structure, such as latent links between locations. Our proposed approach can model and predict how traffickers move. In addition, we propose a model which learns connections between locations. This model is then adapted to have knowledge of environmental events, and we demonstrate that incorporating knowledge of environmental events can improve prediction of future locations. While we have validated our models on the impact of severe weather on human trafficking, we believe our models can be generalized to a variety of other settings in which environmental events impact human behavior

}, author = {Tomkins, Sabina and Golnoosh Farnadi and Brian Amantullah and Lise Getoor and Steven Minton} } @article {farnadi:mlj17, title = {Soft quantification in statistical relational learning}, journal = {Machine Learning Journal}, year = {2017}, author = {Golnoosh Farnadi and Bach, Stephen H. and Moens, Marie-Francine and Lise Getoor and De Cock, Martine} } @conference {farnadi:ilp15, title = {Statistical Relational Learning with Soft Quantifiers}, booktitle = {International Conference on Inductive Logic Programming (ILP)}, year = {2015}, note = {Winner of Best Student Paper award.}, author = {Golnoosh Farnadi and Bach, Stephen H. and Blondeel, Marjon and Moens, Marie-Francine and Lise Getoor and De Cock, Martine} } @conference {farnadi:starai14, title = {Extending PSL with Fuzzy Quantifiers}, booktitle = {International Workshop on Statistical Relational Artificial Intelligence (StaRAI)}, year = {2014}, author = {Golnoosh Farnadi and Bach, Stephen H. and Moens, Marie-Francine and Lise Getoor and De Cock, Martine} }