Empowered by the widespread adoption of Internet of Things (IoT) devices and smartphones, last-mile delivery services have evolved to accommodate both delivery and pickup tasks. An essential challenge in last-mile delivery is efficiently planning routes for couriers to handle pre-scheduled delivery requests as well as stochastic pickup requests. Existing work approaches this problem by either adjusting routes on the fly when new requests arise or preplanning routes based on predicted future pickup requests. However, these methods either compromise the optimality of planned routes or heavily rely on the accuracy of predictions. In this work, we take conformal prediction as an opportunity to address the issue of prediction uncertainty. We design ROPU, a novel courier route planning framework for logistics systems that incorporates conformal prediction into reinforcement learning. Our work advances the existing work from two aspects: (i) Pickup request prediction utilizes spatial-temporal conformal prediction to capture historical pickup request patterns, providing a unified spatial-temporal conformal interval with high confidence (ii) A spatial-temporal attention network assesses location importance from various perspectives and enables the actor to perceive time and integrate the spatial-temporal conformal interval. We implement and evaluate ROPU on one of the largest logistics platforms. Extensive experiment results demonstrate that our method outperforms other state-of-the-art methods with improvements of at least 30.49% in the pickup overdue rate, 25.00% in the delivery overdue rate, and 5.49% in the traveling distance metric.
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Processing SPARQL queries over large federations of SPARQL endpoints is crucial for keeping the Semantic Web decentralized. Despite the existence of hundreds of SPARQL endpoints, current federation engines only scale to dozens. One major issue comes from the current definition of the source selection problem, i.e., finding the minimal set of SPARQL endpoints to contact per triple pattern. Even if such a source selection is minimal, only a few combinations of sources may return results. Consequently, most of the query processing time is wasted evaluating combinations that return no results. In this paper, we introduce the concept of Result-Aware query plans. This concept ensures that every subquery of the query plan effectively contributes to the result of the query. To compute a Result-Aware query plan, we propose FedUP, a new federation engine able to produce Result-Aware query plans by tracking the provenance of query results. However, getting query results requires computing source selection, and computing source selection requires query results. To break this vicious cycle, FedUP computes results and provenances on tiny quotient summaries of federations at the cost of source selection accuracy. Experimental results on federated benchmarks demonstrate that FedUP outperforms state-of-the-art federation engines by orders of magnitude in the context of large-scale federations.
Retrieval-augmented generation have become central in natural language processing due to their efficacy in generating factual content. While traditional methods employ single-time retrieval, more recent approaches have shifted towards multi-time retrieval for multi-hop reasoning tasks. However, these strategies are bound by predefined reasoning steps, potentially leading to inaccuracies in response generation. This paper introduces MetaRAG, an approach that combines the retrieval-augmented generation process with metacognition. Drawing from cognitive psychology, metacognition allows an entity to self-reflect and critically evaluate its cognitive processes. By integrating this, MetaRAG enables the model to monitor, evaluate, and plan its response strategies, enhancing its introspective reasoning abilities. Through a three-step metacognitive regulation pipeline, the model can identify inadequacies in initial cognitive responses and fixes them. Empirical evaluations show that MetaRAG significantly outperforms existing methods.
Traffic flow forecasting is a fundamental research issue for transportation planning and management, which serves as a canonical and typical example of spatial-temporal predictions. In recent years, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) have achieved great success in capturing spatial-temporal correlations for traffic flow forecasting. Yet, two non-ignorable issues haven't been well solved: 1) The message passing in GNNs is immediate, while in reality the spatial message interactions among neighboring nodes can be delayed. The change of traffic flow at one node will take several minutes, i.e., time delay, to influence its connected neighbors. 2) Traffic conditions undergo continuous changes. The prediction frequency for traffic flow forecasting may vary based on specific scenario requirements. Most existing discretized models require retraining for each prediction horizon, restricting their applicability. To tackle the above issues, we propose a neural Spatial-Temporal Delay Differential Equation model, namely STDDE. It includes both delay effects and continuity into a unified delay differential equation framework, which explicitly models the time delay in spatial information propagation. Furthermore, theoretical proofs are provided to show its stability. Then we design a learnable traffic-graph time-delay estimator, which utilizes the continuity of the hidden states to achieve the gradient backward process. Finally, we propose a continuous output module, allowing us to accurately predict traffic flow at various frequencies, which provides more flexibility and adaptability to different scenarios. Extensive experiments show the superiority of STDDE. Both quantitative and qualitative experiments are conducted to validate the concept of a delay-aware module. Also, the flexibility validation shows the effectiveness of the continuous output module.
Online navigation platforms are well optimized to solve the standard objective of minimizing travel time and typically require precomputation-based architectures (such as Contraction Hierarchies and Customizable Route Planning) to do so in a fast manner. The reason for this dependence is the size of the graph that represents the road network, which is large. The need to go beyond minimizing the travel time and introduce various types of customizations has led to approaches that rely on alternative route computation or, more generally, small subgraph extraction. On a small subgraph, one can run computationally expensive algorithms at query time and compute optimal solutions for multiple routing problems. In this framework, it is critical for the subgraph to (a) be small and (b) include (near) optimal routes for a collection of customizations. This is precisely the setting that we study in this work. We design algorithms that extract a subgraph connecting designated terminals with the objective of minimizing the subgraph's size and the constraint of including near-optimal routes for a set of predefined cost functions. We provide theoretical guarantees for our algorithms and evaluate them empirically using real-world road networks.
There is huge value in making software development more productive with AI. An important component of this vision is the capability to translate natural language to a programming language ("NL2Code") and thus to significantly accelerate the speed at which code is written. This workshop gathers researchers, practitioners, and users from industry and academia that are working on NL2Code, specifically on the problem of using large language models to convert statements posed in a human language to a formal programming language.
epiDAMIK 2024: The 7th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery
PDF ↗While the worst of COVID-19 pandemic has most likely passed us, an occurrence of equally devastating global pandemic or regional epidemic cannot be ruled out in future. H1N1, Zika, SARS, MERS, and Ebola outbreaks over the past few decades have sharply illustrated our enormous vulnerability to emerging infectious diseases. While the data mining research community has demonstrated increased interest in epidemiological applications, much is still left to be desired. For example, there is an urgent need to develop sound theoretical principles and transformative computational approaches that will allow us to address the escalating threat of current and future pandemics. Data mining and knowledge discovery have an important role to play in this regard. Different aspects of infectious disease modeling, analysis, and control have traditionally been studied within the confines of individual disciplines, such as mathematical epidemiology and public health, and data mining and machine learning. Coupled with increasing data generation across multiple domains/sources (e.g., wastewater surveillance, electronic medical records, and social media), there is a clear need for analyzing them to inform public health policies and outcomes timely. Recent advances in disease surveillance and forecasting, and initiatives such as the CDC Flu Challenge, CDC COVID-19 Forecasting Hub etc., have brought these disciplines closer together. On the one hand, public health practitioners seek to use novel datasets, such as Safegraph, Unacast, and Google mobility data, and techniques like Graph Neural Networks. On the other hand, researchers from data mining and machine learning develop novel tools for solving many fundamental problems in the public health policy planning and decision-making process, leveraging novel datasets (e.g., COVID-19 behavioral health surveys, contact tracing trees, and satellite images of urban streets) and combining them with more traditional time series information (e.g., surveillance, hospitalization, and death records). We believe the next stage of advances will result from closer collaborations between these two groups, which is the main objective of epiDAMIK.
Deep Learning has facilitated various high-stakes applications such as crime detection, urban planning, drug discovery, and healthcare. Its continuous success hinges on learning from massive data in miscellaneous sources, ranging from data with independent distributions to graph-structured data capturing intricate inter-sample relationships. Scaling up the data access requires global collaboration from distributed data owners. Yet, centralizing all data sources to an untrustworthy centralized server will put users' data at risk of privacy leakage or regulation violation. Federated Learning (FL) is a de facto decentralized learning framework that enables knowledge aggregation from distributed users without exposing private data. Though promising advances are witnessed for FL, new challenges are emerging when integrating FL with the rising needs and opportunities in data mining, graph analytics, foundation models, generative AI, and new interdisciplinary applications in science. By hosting this workshop, we aim to attract a broad range of audiences, including researchers and practitioners from academia and industry interested in the emergent challenges in FL. As an effort to advance the fundamental development of FL, this workshop will encourage ideas exchange on the trustworthiness, scalability, and robustness of distributed data mining and graph analytics and their emergent challenges.
The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2024 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.
Large Language Models (LLMs) are revolutionizing the field of code development by leveraging their deep understanding of code patterns, syntax, and semantics to assist developers in various tasks, from code generation and testing to code understanding and documentation. In this survey, accompanying our proposed lecture-style tutorial for KDD 2024, we explore the multifaceted impact of LLMs on the code development, delving into techniques for generating a high-quality code, creating comprehensive test cases, automatically generating documentation, and engaging in an interactive code reasoning. Throughout the survey, we highlight some crucial components surrounding LLMs, including pre-training, fine-tuning, prompt engineering, iterative refinement, agent planning, and hallucination mitigation. We put forward that such ingredients are essential to harness the full potential of these powerful AI models in revolutionizing software engineering and paving the way for a more efficient, effective, and innovative future in code development.
Large language models (LLMs) have been rapidly adopted, as showcased by ChatGPT's overnight popularity, and are integrated in products used by millions of people every day, such as search engines and productivity suites. Yet the societal impact of LLMs, encompassing both benefits and harms, is not well understood. Inspired by cybersecurity practices, red-teaming is emerging as a technique to uncover model vulnerabilities. Despite increasing attention from industry, academia, and government centered around red-teaming LLMs, such efforts are still limited in the diversity of the red-teaming focus, approaches and participants. Importantly, given that LLMs are becoming ubiquitous, it is imperative that red-teaming efforts are scaled out to include large segments of the research, practitioners and the people whom are directly affected by the deployment of these systems. The goal of this tutorial is two fold. First, we introduce the topic of LLM red-teaming by reviewing the state of the art for red-teaming practices, from participatory events to automatic AI-focused approaches, exposing the gaps in both the techniques and coverage of the targeted harms. Second, we plan to engage the audience in a hands-on and interactive exercise in LLM red-teaming to showcase the ease (or difficulty) of exposing model vulnerabilities, contingent on both the targeted harm and model capabilities. We believe that the KDD community of researchers and practitioners are in a unique position to address the existing gaps in red-teaming approaches, given their longstanding research and practice of extracting knowledge from data.
Understanding the Weakness of Large Language Model Agents within a Complex Android Environment
PDF ↗Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to maintain an up-to-date understanding and deliver accurate responses. Secondly, real-world tasks often require inter-application cooperation, demanding farsighted planning from LLM agents. Thirdly, agents need to identify optimal solutions aligning with user constraints, such as security concerns and preferences. These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system. To address high-cost of manpower, we design a scalable and semi-automated method to construct the benchmark. In the task evaluation, AndroidArena incorporates accurate and adaptive metrics to address the issue of non-unique solutions. Our findings reveal that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints. Additionally, we identify a lack of four key capabilities, i.e. understanding, reasoning, exploration, and reflection, as primary reasons for the failure of LLM agents. Furthermore, we provide empirical analysis on the failure of reflection, and improve the success rate by 27% with our proposed exploration strategy. This work is the first to present valuable insights in understanding fine-grained weakness of LLM agents, and offers a path forward for future research in this area. Environment, benchmark, prompt, and evaluation code for AndroidArena are released at https://github.com/AndroidArenaAgent/AndroidArena.
Privacy policies have disrupted the multi-billion dollar online advertising market by making real-time and precise user data untraceable, which poses significant challenges to the optimization of Return-On-Investment (ROI) constrained products in the online advertising industry. Privacy protection strategies, including event aggregation and reporting delays, hinder access to detailed and instantaneous feedback data, thus incapacitating traditional identity-revealing attribution techniques. In this paper, we introduces a novel Spending Programmed Bidding (SPB) framework to navigate these challenges. SPB is a two-stage framework that separates long horizon delivery spend planning (the macro stage) and short horizon bidding execution (the micro stage). The macro stage models the target ROI to achieve maximum utility and derives the expected spend, whereas the micro stage optimizes the bid price given the expected spend. We further extend our framework to the cross-channel scenario where the agent bids in both privacy-constrained and identity-revealing attribution channels. We find that when privacy-constrained channels are present, SPB is superior to state-of-the-art bidding methods in both offline datasets and online experiments on a large ad platform.
Connecting Large Language Models (LLMs) with the ability to leverage APIs (Web Search, Charting, Calculators, Calendar, Flight Search, Hotel Search, Data Lookup, etc. ) is likely to allow us to solve a variety of new hard problems. Several research efforts have made this observation and suggested recipes for LLMs to emit API calls, and proposed mechanisms by which they can generate additional text conditioned on the output for the API call. However, in practice, the focus has been on relatively simple slot-filling tasks that make an API call rather unlocking novel capabilities by combining different tools, reasoning over the response from a tool, making multiple invocations, or complex planning. In this paper, we pose the following question: what does it mean to say that an LLM is proficient at using a set of APIs? We answer this question in the context of structured APIs by defining seven capabilities for API-use. We provide an approach for generating synthetic tasks that exercise each of these capabilities given only the description of an API. We argue that this provides practitioners with a principled way to construct a dataset to evaluate an LLM's ability to use a given set of APIs. Through human evaluations, we show that our approach produces high-quality tasks for each of the seven capabilities. We also describe how we used this approach to on-board new API and create principled evaluation sets for multiple LLM-based products.
Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.
Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services to bolster intelligent transportation systems. As one of the world's largest ride-hailing platforms, DiDi places great emphasis on the accuracy of congestion prediction to enhance the effectiveness and reliability of their real-time services, such as travel time estimation and route planning. Despite numerous efforts have been made on congestion prediction, most of them fall short in handling heterogeneous and dynamic spatio-temporal dependencies (e.g., periodic and non-periodic congestions), particularly in the presence of noisy and incomplete traffic data. In this paper, we introduce a Congestion Prediction Mixture-of-Experts, CP-MoE, to address the above challenges. We first propose a sparsely-gated Mixture of Adaptive Graph Learners (MAGLs) with congestion-aware inductive biases to improve the model capacity for efficiently capturing complex spatio-temporal dependencies in varying traffic scenarios. Then, we devise two specialized experts to help identify stable trends and periodic patterns within the traffic data, respectively. By cascading these experts with MAGLs, CP-MoE delivers congestion predictions in a more robust and interpretable manner. Furthermore, an ordinal regression strategy is adopted to facilitate effective collaboration among diverse experts. Extensive experiments on real-world datasets demonstrate the superiority of our proposed method compared with state-of-the-art spatio-temporal prediction models. More importantly, CP-MoE has been deployed in DiDi to improve the accuracy and reliability of the travel time estimation system.
Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information
PDF ↗Cancer remains a global challenge due to its growing clinical and economic burden. Its uniquely personal manifestation, which makes treatment difficult, has fuelled the quest for personalized treatment strategies. Thus, genomic profiling is increasingly becoming part of clinical diagnostic panels. Effective use of such panels requires accurate drug response prediction (DRP) models, which are challenging to build due to limited labelled patient data. Previous methods to address this problem have used various forms of transfer learning. However, they do not explicitly model the variable length sequential structure of the list of mutations in such diagnostic panels. Further, they do not utilize auxiliary information (like patient survival) for model training. We address these limitations through a novel transformer-based method, which surpasses the performance of state-of-the-art DRP models on benchmark data. Code for our method is available at https://github.com/CDAL-SOC/PREDICT-AI. We also present the design of a treatment recommendation system (TRS), which is currently deployed at the National University Hospital, Singapore and is being evaluated in a clinical trial. We discuss why the recommended drugs and their predicted scores alone, obtained from DRP models, are insufficient for treatment planning. Treatment planning for complex cancer cases, in the face of limited clinical validation, requires assessment of many other factors, including several indirect sources of evidence on drug efficacy. We discuss key lessons learnt on model validation and use of indirect supporting evidence to build clinicians' trust and aid their decision making.
The rapid decline in groundwater around the world poses a significant challenge to sustainable agriculture. To address this issue, agricultural managed aquifer recharge (Ag-MAR) is proposed to recharge the aquifer by artificially flooding agricultural lands using surface water. Ag-MAR requires a carefully selected flooding schedule to avoid affecting the oxygen absorption of crop roots. However, current Ag-MAR scheduling does not take into account complex environmental factors such as weather and soil oxygen, resulting in crop damage and insufficient recharging amounts. This paper proposes MARLP, the first end-to-end data-driven control system for Ag-MAR. We first formulate Ag-MAR as an optimization problem. To that end, we analyze four-year in-field datasets, which reveal the multi-periodicity feature of the soil oxygen level trends and the opportunity to use external weather forecasts and flooding proposals as exogenous clues for soil oxygen prediction. Then, we design a two-stage forecasting framework. In the first stage, it extracts both the cross-variate dependency and the periodic patterns from historical data to conduct preliminary forecasting. In the second stage, it uses weather-soil and flooding-soil causality to facilitate an accurate prediction of soil oxygen levels. Finally, we conduct model predictive control (MPC) for Ag-MAR flooding. To address the challenge of large action spaces, we devise a heuristic planning module to reduce the number of flooding proposals to enable the search for optimal solutions. Real-world experiments show that MARLP reduces the oxygen deficit ratio by 86.8% while improving the recharging amount in unit time by 35.8%, compared with the previous four years.
Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and Optimization
PDF ↗With the rapid development of the Fifth Generation Mobile Communication Technology (5G) networks, network planning and optimization have become increasingly crucial. Generating high-fidelity network traffic data can preemptively estimate the network demands of mobile users, which holds potential for network operators to improve network performance. However, the data required by existing generation methods is predominantly inaccessible to the public, resulting in a lack of reproducibility for the models and high deployment costs in practice. In this article, we propose an Open data-based Diffusion model for mobile traffic generation (OpenDiff), where a multi-positive contrastive learning algorithm is designed to construct conditional information for the diffusion model using entirely publicly available satellite remote sensing images, Point of Interest (POI), and population data. The conditional information contains relevant human activities in geographical areas, which can effectively guide the generation of network traffic data. We further design an attention-based fusion mechanism to capture the implicit correlations between network traffic and human activity features, enhancing the model's controllable generation capability. We conduct evaluations on three different cities with varying scales, where experimental results verify that our proposed model outperforms existing methods by 14.36% and 13.05% in terms of generation fidelity and controllability. To further validate the effectiveness of the model, we leverage the generated traffic data to assist the operators with network planning on a real-world network optimization platform of China Mobile Communications Corporation. The source code is available online:https://github.com/impchai/OpenDiff-diffusion-model-with-open-data.
Population aging, increasing social demands, and rising costs of treatments are stressing healthcare systems to the point of risking the sustainability of universal and accessible healthcare. A hope in this dismal panorama is that there are large inefficiencies, and so opportunities for getting more from the same resources. To name a few, avoidable hospitalizations, unnecessary medication and tests, and lack of coordination among healthcare agents are estimated to cost several hundred billion euros per year in the EU. Technology can be useful for locating and reducing these inefficiencies, and within technology, the full exploitation of the data that the system collects to record its activity. In this talk, I will review the case for activity data analytics in healthcare, with two main considerations: 1) The need to include information about resources and costs in the models, in addition to clinical knowledge and patient outcomes, and 2) the need to use mostly data that healthcare organizations already collect and is not locked and distributed in silos. Fortunately, data collected for administrative and billing purposes, even though imperfect, partial, and low resolution, can be used to improve efficiency and safety, as well as fairness and equity. I will focus on the work carried out at Amalfi Analytics, a spin-off of my research group at UPC in Barcelona. On the one hand, we have addressed predictive management in hospitals, from influx to the emergency room to availability of surgical areas, beds, and staff. Anticipating activity, needs, and resource availability lets managers improve critical KPIs, e.g. waiting times, but also reduce staff stress, which leads to fewer medical errors and accidents. On the other hand, we have developed a patient cohort analyzer, based mostly on a recent clustering algorithm, that gives experts a fresh view of their patient population and lets them refine protocols and identify high-risk patient groups. This tool has also been used to support territorial planning and resource allocation. These problems have been extensively addressed in the past, but actual penetration of solutions in hospitals is smaller than one could expect. For example, one can find hundreds of papers on predicting influx to emergency rooms or bed demands, but many of them conclude after producing an AUC figure, and even fewer describe a working system that can be exported from the hospital where they were developed to others at an affordable cost. I will describe the approach taken at Amalfi so that hospitals can have such a solution up and running in a few days of work for their IT departments, in what I think is an interesting combination of software engineering and automatic Machine Learning.