Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Building with Predictive Air Conditioning Control: A Knapsack Approach

This thesis proposes a policy to control the heating, ventilation and air conditioning (HVAC) systems in an industrial building. The policy designed in this thesis aims to minimize the electricity cost of a building while maintaining human comfort. Occupancy prediction and building thermal dynamics are utilized in the policy. Because every building has a power constraint, the policy balances different rooms’ electricity needs and electricity price to allocate AC unit power for each room. In particular, energy costs are saved by reducing the system’s power for times when the occupancy is low. Human comfort is preserved by restricting the temperature to a given range when the room occupancy is above a preset threshold. This thesis proposes a greedy policy, with provably good performance bound, to reduce costs for a building while maintaining overall comfort levels. The approximation ratio of the policy is developed and analyzed, demonstrating the effectiveness of this approach as compared to an ideal optimal policy.
Counterfactual Evaluation and Learning for Interactive Systems: Foundations, Implementations, and Recent Advances

Counterfactual estimators enable the use of existing log data to estimate how some new target policy would have performed, if it had been used instead of the policy that logged the data. We say that those estimators work "off-policy", since the policy that logged the data is different from the target policy. In this way, counterfactual estimators enable Off-policy Evaluation (OPE) akin to an unbiased offline A/B test, as well as learning new decision-making policies through Off-policy Learning (OPL). The goal of this tutorial is to summarize Foundations, Implementations, and Recent Advances of OPE and OPL (OPE/OPL), with applications in recommendation, search, and an ever growing range of interactive systems. Specifically, we will introduce the fundamentals of OPE/OPL and provide theoretical and empirical comparisons of conventional methods. Then, we will cover emerging practical challenges such as how to handle large action spaces, distributional shift, and hyper-parameter tuning. We will then present Open Bandit Pipeline, an open-source Python software for OPE/OPL to better enable new research and applications. We will conclude the tutorial with future directions.
Pricing the Long Tail by Explainable Product Aggregation and Monotonic Bandits

In several e-commerce scenarios, pricing long-tail products effectively is a central task for the companies, and there is broad agreement that Artificial Intelligence (AI) will play a prominent role in doing that in the next future. Nevertheless, dealing with long-tail products raises major open technical issues due to data scarcity which preclude the adoption of the mainstream approaches requiring usually a huge amount of data, such as, e.g., deep learning. In this paper, we provide a novel online learning algorithm for dynamic pricing that deals with non-stationary settings due to, e.g., the seasonality or adaptive competitors, and is very efficient in terms of the need for data thanks to assumptions such as, e.g., the monotonicity of the demand curve in the price that are customarily satisfied in long-tail markets. Furthermore, our dynamic pricing algorithm is paired with a clustering algorithm for the long-tail products which aggregates similar products such that the data of all the products of the same cluster are merged and used to choose their best price. We first evaluate our algorithms in an offline synthetic setting, comparing their performance with the state of the art and showing that our algorithms are more robust and data-efficient in long-tail settings. Subsequently, we evaluate our algorithms in an online setting with more than 8,000 products, including popular and long-tail, in an A/B test with humans for about two months. The increase of revenue thanks to our algorithms is about 18% for the popular products and about 90% for the long-tail products.
Temporal Graph Learning for Financial World: Algorithms, Scalability, Explainability & Fairness

The most intuitive way to model a transaction in the financial world is through a Graph. Every transaction can be considered as an edge between two vertices, one of which is the paying party and another is the receiving party. Properties of these nodes and edges directly map to business problems in the financial world. The problem of detecting a fraudulent transaction can be considered as a property of the edge. The problem of money laundering can be considered as a path-detection in the Graph. The problem of a merchant going delinquent can be considered as the property of a node. While there are many such examples, the above help in realising the direct mapping of Graph properties with the financial problems in the real-world. This tutorial is based on the potential of using Graph Neural Network based Learning for solving business problems in the financial world.
MetaPTP: An Adaptive Meta-optimized Model for Personalized Spatial Trajectory Prediction

Trajectory prediction is a fundamental problem for a wide spectrum of location-based applications. Existing methods can achieve inspiring results in predicting personal frequent routes conditioned on massive historical data. However, trajectory estimation may involve cold-start routes or users due to the data sparsity problem, which severely limits the performance of spatial trajectory prediction. Although meta-learning models can alleviate the cold-start problem, they simply utilize the same initialization for all tasks and thus cannot fit each user well due to users' varying travel preferences. To this end, we propose an adaptive meta-optimized model called MetaPTP for personalized spatial trajectory prediction. Specifically, it adopts a soft-clustering based method to guide the network initialization in a finer granularity, so that shared knowledge can be better transferred across users with similar travel preferences. Besides, towards model fine-tuning, an effective trajectory sampling method is introduced to generate meaningful support set, which simultaneously considers user preference and spatial trace similarities to provide task-related information for model adaptation. In addition, we design a weight generator to adaptively assign reasonable weights to trajectories in support set to avoid sub-optimal results which will occur when fine-tuning the initial network with the same weight for trajectories with different user preferences and spatial distributions. Finally, extensive experiments on two real-world datasets demonstrate the superiority of our model.
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries

Knowledge graph (KG) embeddings have been a mainstream approach for reasoning over incomplete KGs. However, limited by their inherently shallow and static architectures, they can hardly deal with the rising focus on complex logical queries, which comprise logical operators, imputed edges, multiple source entities, and unknown intermediate entities. In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies. We design a KG triple transformation method to enable Transformer to handle KGs, which is further strengthened by the Mixture-of-Experts (MoE) sparse activation. We then formulate the complex logical queries as masked prediction and introduce a two-stage masked pre-training strategy to improve transferability and generalizability.Extensive experiments on two benchmarks demonstrate that kgTransformer can consistently outperform both KG embedding-based baselines and advanced encoders on nine in-domain and out-of-domain reasoning tasks. Additionally, kgTransformer can reason with explainability via providing the full reasoning paths to interpret given answers.
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks

With the advancement of data collection techniques, end users are interested in how different types of data can collaborate to improve our life experiences. Multimodal Federated Learning (MFL) is an emerging area allowing many distributed clients, each of which can collect data from multiple types of sensors, to participate in the training of some multimodal data-related models without sharing their data. In this paper, we address a novel challenging issue in MFL, the modality incongruity, where clients may have heterogeneous setups of sensors and their local data consists of different combinations of modalities. With the modality incongruity, clients may solve different tasks on different parameter spaces, which escalates the difficulties in dealing with the statistical heterogeneity problem of federated learning; also, it would be hard to perform accurate model aggregation across different types of clients. To tackle these challenges, in this work, we propose the FedMSplit framework, which allows federated training over multimodal distributed data without assuming similar active sensors in all clients. The key idea is to employ a dynamic and multi-view graph structure to adaptively capture the correlations amongst multimodal client models. More specifically, we split client models into smaller shareable blocks and allow each type of blocks to provide a specific view on client relationships. With the graph representation, the underlying correlations between clients can be captured as the edge features in the multi-view graph, and then be utilized to promote local model relations through the neighborhood message passing in the graph. Our experimental results demonstrate the effectiveness of our method under different sensor setups with statistical heterogeneity.
Model Monitoring in Practice: Lessons Learned and Open Challenges

Artificial Intelligence (AI) is increasingly playing an integral role in determining our day-to-day experiences. Increasingly, the applications of AI are no longer limited to search and recommendation systems, such as web search and movie and product recommendations, but AI is also being used in decisions and processes that are critical for individuals, businesses, and society. With AI based solutions in high-stakes domains such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. Consequently, it becomes critical to ensure that these models are making accurate predictions, are robust to shifts in the data, are not relying on spurious features, and are not unduly discriminating against minority groups. To this end, several approaches spanning various areas such as explainability, fairness, and robustness have been proposed in recent literature, and many papers and tutorials on these topics have been presented in recent computer science conferences. However, there is relatively less attention on the need for monitoring machine learning (ML) models once they are deployed and the associated research challenges. In this tutorial, we first motivate the need for ML model monitoring[14], as part of a broader AI model governance[9] and responsible AI framework, from societal, legal, customer/end-user, and model developer perspectives, and provide a roadmap for thinking about model monitoring in practice. We then present findings and insights on model monitoring desiderata based on interviews with various ML practitioners spanning domains such as financial services, healthcare, hiring, online retail, computational advertising, and conversational assistants[15]. We then describe the technical considerations and challenges associated with realizing the above desiderata in practice. We provide an overview of techniques/tools for model monitoring (e.g., see [1, 1, 2, 5, 6, 8, 10-13, 18-21]. Then, we focus on the real-world application of model monitoring methods and tools [3, 4, 7, 11, 13, 16, 17], present practical challenges/guidelines for using such techniques effectively, and lessons learned from deploying model monitoring tools for several web-scale AI/ML applications. We present case studies across different companies, spanning application domains such as financial services, healthcare, hiring, conversational assistants, online retail, computational advertising, search and recommendation systems, and fraud detection. We hope that our tutorial will inform both researchers and practitioners, stimulate further research on model monitoring, and pave the way for building more reliable ML models and monitoring tools in the future.
Augmenting Log-based Anomaly Detection Models to Reduce False Anomalies with Human Feedback

With the increasing complexity of modern software systems, it is essential yet hard to detect anomalies and diagnose problems precisely. Existing log-based anomaly detection approaches rely on a few key assumptions on system logs and perform well in some experimental systems. However, real-world industrial systems are often with poor logging quality, in which system logs are noisy and often violate the assumptions of existing approaches. This makes these approaches inefficient. This paper first conducts a comprehensive study on the system logs of three large-scale industrial software systems. Through the study, we identify four typical anti-patterns that affect the detection results the most. Based on these patterns, we propose HiLog, an effective human-in-the-loop log-based anomaly detection approach that integrates human knowledge to augment anomaly detection models. With little human labeling effort, our approach can significantly improve the effectiveness of existing models. Experiment results on three large-scale industrial software systems show that our method improves over 50% precision rate on average.
FlowGEN: A Generative Model for Flow Graphs

Flow graphs capture the directed flow of a quantity of interest (e.g., water, power, vehicles) being transported through an underlying network. Modeling and generating realistic flow graphs is key in many applications in infrastructure design, transportation, and biomedical and social sciences. However, they pose a great challenge to existing generative models due to a complex dynamics that is often governed by domain-specific physical laws or patterns. We introduce FlowGEN, an implicit generative model for flow graphs, that learns how to jointly generate graph topologies and flows with diverse dynamics directly from data using a novel (flow) graph neural network. Experiments show that our approach is able to effectively reproduce relevant local and global properties of flow graphs, including flow conservation, cyclic trends, and congestion around hotspots.
Optimizing Long-Term Efficiency and Fairness in Ride-Hailing via Joint Order Dispatching and Driver Repositioning

The ride-hailing service offered by mobility-on-demand platforms, such as Uber and Didi Chuxing, has greatly facilitated people's traveling and commuting, and become increasingly popular in recent years. Efficiency (e.g., gross merchandise volume) has always been an important metric for such platforms. However, only focusing on the efficiency inevitably ignores the fairness of driver incomes, which could impair the sustainability of the overall ride-hailing system in the long run. To optimize the aforementioned two essential metrics, order dispatching and driver repositioning play an important role, as they impact not only the immediate, but also the future order-serving outcomes of drivers. Thus, in this paper, we aim to exploit joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness for ride-hailing platforms. To address this problem, we propose a novel multi-agent reinforcement learning framework, referred to as JDRL, to help drivers make distributed order selection and repositioning decisions. Specifically, to cope with the variable action space, JDRL segments the action space into a fixed number of action groups, and fixes the policy output dimension for order selection as the number of action groups. In terms of the fairness criterion, JDRL adopts the max-min fairness, and augments the vanilla policy gradient to an iterative training algorithm that alternates between a minimization step and a policy improvement step to maximize both the worst and the overall performance of agents. In addition, we provide the theoretical convergence guarantee of our JDRL training algorithm even under non-convex policy networks and stochastic gradient updating. Extensive experiments are conducted with three public real-world ride-hailing order datasets, including over 2 million orders in Haikou, China, over 5 million orders in Chengdu, China, and over 6 million orders in New York City, USA. Experimental results show that JDRL demonstrates a consistent advantage compared to state-of-the-art baselines in terms of both efficiency and fairness. To the best of our knowledge, this is the first work that exploits joint order dispatching and driver repositioning to optimize both the long-term efficiency and fairness in a ride-hailing system.
Edge-computing-driven Internet of Things: A Survey

The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentralized computing model, is valued by more and more researchers in academia and industry. The main idea of edge computing is placing data processing in near-edge devices instead of remote cloud servers. It is promising to build more scalable, low-latency IoT systems. Many studies have been proposed on edge computing and IoT, but a comprehensive survey of this crossover area is still lacking. In this survey, we first introduce the impact of edge computing on the development of IoT and point out why edge computing is more suitable for IoT than other computing paradigms. Then, we analyze the necessity of systematical investigation on the edge-computing-driven IoT (ECDriven-IoT) and summarize new challenges occurring in ECDriven-IoT. We categorize recent advances from bottom to top, covering six aspects of ECDriven-IoT. Finally, we conclude lessons learned and propose some challenging
Crowdsourcing with Contextual Uncertainty

We study a crowdsourcing setting where we need to infer the latent truth about a task given observed labels together with context in the form of a classifier score. We present Theodon, a hierarchical non-parametric Bayesian model, developed and deployed at Meta, that captures both the prevalence of label categories and the accuracy of labelers as functions of the classifier score. Theodon uses Gaussian processes to model the non-uniformity of mistakes over the range of classifier scores. For our experiments, we used data generated from integrity applications at Meta as well as public datasets. We showed that Theodon (1) obtains 1-4% improvement in AUC-PR predictions on items' true labels compared to state-of-the-art baselines for public datasets, (2) is effective as a calibration method, and (3) provides detailed insights on labelers' performances.
Nonlinearity Encoding for Extrapolation of Neural Networks

Extrapolation to predict unseen data outside the training distribution is a common challenge in real-world scientific applications of physics and chemistry. However, the extrapolation capabilities of neural networks have not been extensively studied in machine learning. Although it has been recently revealed that neural networks become linear regression in extrapolation problems, a universally applicable method to support the extrapolation of neural networks in general regression settings has not been investigated. In this paper, we propose automated nonlinearity encoder (ANE) that is a data-agnostic embedding method to improve the extrapolation capabilities of neural networks by conversely linearizing the original input-to-target relationships without architectural modifications of prediction models. ANE achieved state-of-the-art extrapolation accuracies in extensive scientific applications of various data formats. As a real-world application, we applied ANE for high-throughput screening to discover novel solar cell materials, and ANE significantly improved the screening accuracy.
A Spectral Representation of Networks: The Path of Subgraphs

Network representation learning has played a critical role in studying networks. One way to study a graph is to focus on its spectrum, i.e., the eigenvalue distribution of its associated matrices. Recent advancements in spectral graph theory show that spectral moments of a network can be used to capture the network structure and various graph properties. However, sometimes networks with different structures or sizes can have the same or similar spectral moments, not to mention the existence of the cospectral graphs. To address such problems, we propose a 3D network representation that relies on the spectral information of subgraphs: the Spectral Path, a path connecting the spectral moments of the network and those of its subgraphs of different sizes. We show that the spectral path is interpretable and can capture relationship between a network and its subgraphs, for which we present a theoretical foundation. We demonstrate the effectiveness of the spectral path in applications such as network visualization and network identification.
Implementation of sensor selection and fusion using fuzzy logic

Different sensors may contain degrees of uncertainty and may be only reliable in particular situations, therefore sensor fusion and validation can be critical in complex redundant systems. This paper proposed a generic fuzzy logic algorithm for validation and fusion of uncertain sensor data. The system degrades marginal sensor data elegantly, while still removing obviously questionable data. Sensor data is represented as Gaussian curves. The sensor fusion problem is presented as determining a fused mean and standard deviation for the Gaussian of the output abstract sensor. Four variants of the fuzzy sensor fusion and validation system are presented and examined.
Algorithmic Fairness on Graphs: Methods and Trends

Graph is a ubiquitous type of data that appears in many real-world applications, including social network analysis, recommendations and financial security. Important as it is, decades of research have developed plentiful computational models to mine graphs. Despite its prosperity, concerns with respect to the potential algorithmic discrimination have been grown recently. Algorithmic fairness on graphs, which aims to mitigate bias introduced or amplified during the graph mining process, is an attractive yet challenging research topic. The first challenge corresponds to the theoretical challenge, where the non-IID nature of graph data may not only invalidate the basic assumption behind many existing studies in fair machine learning, but also introduce new fairness definition(s) based on the inter-correlation between nodes rather than the existing fairness definition(s) in fair machine learning. The second challenge regarding its algorithmic aspect aims to understand how to balance the trade-off between model accuracy and fairness. This tutorial aims to (1) comprehensively review the state-of-the-art techniques to enforce algorithmic fairness on graphs and (2) enlighten the open challenges and future directions. We believe this tutorial could benefit researchers and practitioners from the areas of data mining, artificial intelligence and social science.