In this tool paper, we design, develop, and release BoolXAI, an interpretable machine learning classification approach for Explainable AI (XAI) based on expressive Boolean formulas. The Boolean formula defines a logical rule with tunable complexity according to which input data are classified. Beyond the classical conjunction and disjunction, BoolXAI offers expressive operators such as AtLeast, AtMost, and Choose and their parameterization. This provides higher expressiveness compared to rigid rules- and tree-based approaches. We show how to train BoolXAI classifiers effectively using native local optimization to search the space of feasible formulas. We provide illustrative results on several well-known public benchmarks that demonstrate the competitive nature of our approach compared to existing methods. Our work is embodied in the open-source BoolXAI library with a high-level user interface to serve researchers and practitioners. BoolXAI can be used either as a standalone interpretable classifier or for post-hoc explanations of other black-box models or observed behavior. We highlight several desirable benefits of our tool, especially in industrial settings where rapid experimentation, reusability, reproducibility, deployment, and maintenance are of great interest. Finally, we showcase a deployed service powered by BoolXAI as an enterprise application.

Mixed-integer programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems. Recently, learning-based approaches have shown a potential to speed up MIP solving via offline training that then guides important design decisions during the search. However, a significant drawback of these methods is their heavy reliance on offline training, which requires collecting training datasets and computationally costly training epochs yet offering only limited generalization to unseen (larger) instances. In this paper, we propose Balans, an adaptive meta-solver for MIPs with online learning capability that does not require any supervision or apriori training. At its core, Balans is based on adaptive large-neighborhood search, operating on top of an MIP solver by successive applications of destroy and repair neighborhood operators. During the search, the selection among different neighborhood definitions is guided on the fly for the instance at hand via multi-armed bandit algorithms. Our extensive experiments on hard optimization instances show that Balans offers significant performance gains over the default MIP solver, is better than committing to any single best neighborhood, and improves over the state-of-the-art large-neighborhood search for MIPs. Finally, we release Balans as a highly configurable, MIP solver agnostic, open-source software.

It is imperative to ensure that AI models perform well for all groups including those from underprivileged populations. By comparing the performance of models for the protected group with respect to the rest of the population, we can uncover and prevent unwanted bias. However, a significant drawback of such binary fairness evaluation is its dependency on protected group membership attributes. In various real-world scenarios, protected status for individuals is sparse, unavailable, or even illegal to collect. This article extends the previous work on binary fairness metrics to relax the requirement on deterministic membership to its surrogate counterpart under a probabilistic setting. We show how to conduct binary fairness evaluation when exact protected attributes are not available, but their surrogates as likelihoods are accessible. In theory, we prove that inferred metrics calculated from surrogates are valid under standard statistical assumptions. In practice, we demonstrate the effectiveness of our approach using publicly available data from the Home Mortgage Disclosure Act and simulated benchmarks that mimic real-world conditions under different levels of model disparity. We extend the results from previous work to include comparisons with alternative model-based methods and we develop further practical guidance based on our extensive simulation. Finally, we embody our method in open source software that is readily available for use in other applications.

We present a modular recommender system framework that tightly integrates yet maintains the independence of individual components, thus satisfying two of the most critical aspects of industrial applications, generality and specificity. On the one hand, we ensure that each component remains self-contained and is ready to serve in other applications beyond recommender systems. On the other hand, when these components are combined, a unified theme emerges for recommender systems. We present the details of each component in the context of recommender systems and other applications. We release each component as an open-source library, and most importantly, we release their integration under MAB2REC, an industry-strength open-source software for building bandit-based recommender systems. By bringing standalone components together, Mab2Rec realizes a powerful and scalable toolchain to build and deploy business-relevant personalization applications. Finally, we share our experience and best practices for user training, adoption, performance evaluation, deployment, and model governance within the enterprise and the broader community.

Handwriting recognition relies on supervised data for training. Annotations typically include both the written text and the author's identity to facilitate the recognition of a particular style. A large annotation set is required for robust recognition, which is not always available in historical texts and low-annotation languages. To mitigate this challenge, we propose the Read-Write-Learn framework. In this setting, we augment the training process of handwriting recognition with a language model and a handwriting generator. Specifically, in the first reading step, we employ a language model to identify text that is likely detected correctly by the recognition model. Then, in the writing step, we generate more training data in the same writing style. Finally, in the learning step, we use the newly generated data in the same writing style to finetune the recognition model. Our Read-Write-Learn framework allows the recognition model to incrementally converge on the new style. Our experiments on historical handwritten documents demonstrate the benefits of the approach, and we present several examples to showcase improved recognition.

Pattern mining is an essential part of knowledge discovery and data analytics. It is a powerful paradigm, especially when combined with constraint reasoning. In this paper, we present Seq2Pat, a constraint-based sequential pattern mining tool with a high-level declarative user interface. The library finds patterns that frequently occur in large sequence databases subject to constraints. We highlight key benefits that are desirable, especially in industrial settings where scalability, explainability, rapid experimentation, reusability, and reproducibility are of great interest. We then showcase an automated feature extraction process powered by Seq2Pat to discover high-level insights and boost downstream machine learning models for customer intent prediction.

We propose a novel context-free grammar to represent text embeddings in conjunction with their various transformations. We show how this grammar can serve as a unification layer on top of different featurization techniques, and their hybridization thereof. The approach is embodied in an open-source library, called TextWiser, with a high-level user interface to serve researchers and practitioners. The goal of TextWiser is to enable rapid experimentation with various featurization methods and to serve as a building block within AI applications consuming unstructured data. We highlight several key benefits that are desirable especially in industrial settings where rapid experimentation, reusability, reproducibility, and time to market are of great interest. Finally, we showcase a deployed service powered by TextWiser as a proof-of-concept enterprise application.

Uplift modeling is used extensively to identify treatment candidates that are more likely to benefit from an intervention. However, the fairness evaluation of such models remains a challenge due to the lack of ground truth on the outcome measure since a candidate cannot be in both treatment and control simultaneously. In this paper, we propose a framework that generates surrogate ground truth to serve as a proxy for counterfactual labels of uplm modeling campaigns. We then leverage the surrogate ground truth to conduct a more comprehensive binary fairness evaluation. We show how to apply the approach in a real-world marketing campaign for promotional offers and demonstrate its enhancement for fairness evaluation.

Contextual multi-armed bandit algorithms serve as an effective technique to address online sequential decision-making problems. Despite their popularity, when it comes to off-the-shelf tools the library support remains limited, in particular for the Python technology stack. To fill this gap, in this paper we present a system that provides context-free, parametric and non-parametric contextual multi-armed bandit models. The available bandit policies accommodate both batch and online learning. The MABWISER system is implemented as an open-source Python library. Our design enables built-in parallelization to speed up training and test components for scalability while ensuring the reproducibility of results. We present a running example to highlight the user-friendly nature of the public interface and discuss the simulation capability of the library for hyper-parameter tuning and rapid experimentation.

Personalized Recommendation Systems require an effective method to balance exploration and exploitation. To learn effective strategies, user and item attributes are critical data sources to capture contextual information. In this paper, we first present an approach based on Bayesian Deep Learning to learn a compact representation of user and item attributes to guide exploitation. A key novelty of the approach lies in its ability to also capture the uncertainty associated with the model output to guide exploration. We then show how to further boost exploration by incorporating model uncertainty with that of data uncertainty. Experimental results demonstrate the benefits of our approach in terms of accuracy in recommendations as well as its performance in an online setting.

This paper introduces a combinatorial problem arising from real-world business requirements as part of resource allocation in Cloud Management. In particular, we focus on the allocation of a set of heterogeneous resources serving multiple tenants with different service level agreements. There exist certain business rules that govern the application stemming from privacy, performance, and capacity requirements. We show how to formulate the problem as constrained optimization and then solve it efficiently using Artificial Intelligence based constraint propagation. Our approach stands out as a high-level, declarative solution that is efficient and easy to maintain and update.

We consider the problem of parallelizing restarted backtrack search. With few notable exceptions, most commercial and academic constraint programming solvers do not learn no-goods during search. Depending on the branching heuristics used, this means that there are little to no side-effects between restarts, making them an excellent target for parallelization. We develop a simple technique for parallelizing restarted search deterministically and demonstrate experimentally that we can achieve near-linear speed-ups in practice.

We present a dynamic branching scheme for set partitioning problems. The idea is to trace features of the underlying MIP model and to base search decisions on the features of the current subproblem to be solved. We show how such a system can be trained efficiently by introducing minimal learning bias that traditional model-based machine learning approaches rely on. Experiments on a highly heterogeneous collection of set partitioning instances show significant gains over dynamic search guidance in Cplex as well as instance-specifically tuned pure search heuristics.

This study focuses on handling high-dimensional classification problems by means of feature selection. The data sets used are provided by the organizers of the Interspeech 2012 Speaker Trait Challenge. A combination of two feature selection approaches gives results that approach or exceed the challenge baselines using a knearest-neighbor classifier. One of the feature selection methods is based on covering the data set with correct unsupervised or supervised classifications according to individual features. The other selection method applies a measure of statistical dependence between discretized features and class labels. Index Terms: pattern recognition, feature selection, high-dimensional data, speaker characteristics

We present a new method for instance-specific algorithm configuration (ISAC). It is based on the integration of the algorithm configuration system GGA and the recently proposed stochastic offline programming paradigm. ISAC is provided a solver with categorical, ordinal, and/or continuous parameters, a training benchmark set of input instances for that solver, and an algorithm that computes a feature vector that characterizes any given instance. ISAC then provides high quality parameter settings for any new input instance. Experiments on a variety of different constrained optimization and constraint satisfaction solvers show that automatic algorithm configuration vastly outperforms manual tuning. Moreover, we show that instance-specific tuning frequently leads to significant speed-ups over instance-oblivious configurations.