pwc-archive/papers-with-abstracts huggingface dataset:
Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
Authors: Cencheng Shen, Jonathan Larson, Ha Trinh, Carey E. Priebe
Date: 2024-05-21 00:00:00

This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within observed communities. We provide the theoretical rationale for the refinement procedure, demonstrating how and why our proposed method can effectively identify useful hidden communities under stochastic block models. Furthermore, we show how the refinement method leads to improved vertex embedding and better decision boundaries for subsequent vertex classification. The efficacy of our approach is validated through numerical experiments, which exhibit clear advantages in identifying meaningful latent communities and improved vertex classification across a collection of simulated and real-world graph data.
Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks
Authors: Francesco Zanetta, Daniele Nerini, Matteo Buzzi, Henry Moss
Date: 2024-05-21 00:00:00
Tasks: Decision Making, Gaussian Processes

Accurately representing surface weather at the sub-kilometer scale is crucial for optimal decision-making in a wide range of applications. This motivates the use of statistical techniques to provide accurate and calibrated probabilistic predictions at a lower cost compared to numerical simulations. Wind represents a particularly challenging variable to model due to its high spatial and temporal variability. This paper presents a novel approach that integrates Gaussian processes and neural networks to model surface wind gusts at sub-kilometer resolution, leveraging multiple data sources, including numerical weather prediction models, topographical descriptors, and in-situ measurements. Results demonstrate the added value of modeling the multivariate covariance structure of the variable of interest, as opposed to only applying a univariate probabilistic regression approach. Modeling the covariance enables the optimal integration of observed measurements from ground stations, which is shown to reduce the continuous ranked probability score compared to the baseline. Moreover, it allows the generation of realistic fields that are also marginally calibrated, aided by scalable techniques such as random Fourier features and pathwise conditioning. We discuss the effect of different modeling choices, as well as different degrees of approximation, and present our results for a case study.
Marginal and training-conditional guarantees in one-shot federated conformal prediction
Authors: Pierre Humbert, Batiste Le Bars, Aurélien Bellet, Sylvain Arlot
Date: 2024-05-21 00:00:00
Tasks: Conformal Prediction, Federated Learning, Prediction, valid

We study conformal prediction in the one-shot federated learning setting. The main goal is to compute marginally and training-conditionally valid prediction sets, at the server-level, in only one round of communication between the agents and the server. Using the quantile-of-quantiles family of estimators and split conformal prediction, we introduce a collection of computationally-efficient and distribution-free algorithms that satisfy the aforementioned requirements. Our approaches come from theoretical results related to order statistics and the analysis of the Beta-Beta distribution. We also prove upper bounds on the coverage of all proposed algorithms when the nonconformity scores are almost surely distinct. For algorithms with training-conditional guarantees, these bounds are of the same order of magnitude as those of the centralized case. Remarkably, this implies that the one-shot federated learning setting entails no significant loss compared to the centralized case. Our experiments confirm that our algorithms return prediction sets with coverage and length similar to those obtained in a centralized setting.
EKM: An exact, polynomial-time algorithm for the $K$-medoids problem
Authors: Xi He, Max A. Little
Date: 2024-05-16 00:00:00

The $K$-medoids problem is a challenging combinatorial clustering task, widely used in data analysis applications. While numerous algorithms have been proposed to solve this problem, none of these are able to obtain an exact (globally optimal) solution for the problem in polynomial time. In this paper, we present EKM: a novel algorithm for solving this problem exactly with worst-case $O\left(N^{K+1}\right)$ time complexity. EKM is developed according to recent advances in transformational programming and combinatorial generation, using formal program derivation steps. The derived algorithm is provably correct by construction. We demonstrate the effectiveness of our algorithm by comparing it against various approximate methods on numerous real-world datasets. We show that the wall-clock run time of our algorithm matches the worst-case time complexity analysis on synthetic datasets, clearly outperforming the exponential time complexity of benchmark branch-and-bound based MIP solvers. To our knowledge, this is the first, rigorously-proven polynomial time, practical algorithm for this ubiquitous problem.
Joint Prediction Regions for time-series models
Authors: Eshant English
Date: 2024-05-14 00:00:00
Tasks: Prediction, Prediction Intervals, Time Series

Machine Learning algorithms are notorious for providing point predictions but not prediction intervals. There are many applications where one requires confidence in predictions and prediction intervals. Stringing together, these intervals give rise to joint prediction regions with the desired significance level. It is an easy task to compute Joint Prediction regions (JPR) when the data is IID. However, the task becomes overly difficult when JPR is needed for time series because of the dependence between the observations. This project aims to implement Wolf and Wunderli's method for constructing JPRs and compare it with other methods (e.g. NP heuristic, Joint Marginals). The method under study is based on bootstrapping and is applied to different datasets (Min Temp, Sunspots), using different predictors (e.g. ARIMA and LSTM). One challenge of applying the method under study is to derive prediction standard errors for models, it cannot be obtained analytically. A novel method to estimate prediction standard error for different predictors is also devised. Finally, the method is applied to a synthetic dataset to find empirical averages and empirical widths and the results from the Wolf and Wunderli paper are consolidated. The experimental results show a narrowing of width with strong predictors like neural nets, widening of width with increasing forecast horizon H and decreasing significance level alpha, controlling the width with parameter k in K-FWE, and loss of information using Joint Marginals.
Address-Specific Sustainable Accommodation Choice Through Real-World Data Integration
Authors: Peter J. Bentley, Rajat Mathur, Soo Ling Lim, Sid Narang
Date: 2024-05-21 00:00:00
Tasks: Data Integration

Consumers wish to choose sustainable accommodation for their travels, and in the case of corporations, may be required to do so. Yet accommodation marketplaces provide no meaningful capability for sustainable choice: typically CO2 estimates are provided that are identical for all accommodation of the same type across an entire country. We propose a decision support system that enables real choice of sustainable accommodation. We develop a data-driven address-specific metric called EcoGrade, which integrates government approved datasets and uses interpolation where data is sparse. We validate the metric on 10,000 UK addresses in 10 cities, showing the match of our interpolations to reality is statistically significant. We show how the metric has been embedded into a decision support system for a global accommodation marketplace and tested by real users over several months with positive user feedback. In the EU, forty percent of final energy consumption is from buildings. We need to encourage all building owners to make their accommodation more efficient. The rental sector is one area where change can occur rapidly, as rented accommodation is renovated frequently. We anticipate our decision support system using EcoGrade will encourage this positive change.
Robust portfolio optimization model for electronic coupon allocation
Authors: Yuki Uehara, Naoki Nishimura, Yilin Li, Jie Yang, Deddy Jobson, Koya Ohashi, Takeshi Matsumoto, Noriyoshi Sukegawa, Yuichi Takano
Date: 2024-05-21 00:00:00
Tasks: Multiple-choice, Portfolio Optimization

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to the coupon allocation problem. We also validate the efficacy of our method through numerical experiments using actual data from randomly distributed coupons. Main contributions of our research are twofold. First, we handle six types of coupons, thereby making it extremely difficult to accurately estimate the difference in the effects of various coupons. Second, we demonstrate from detailed numerical results that the robust optimization model achieved larger uplifts of sales than did the commonly-used multiple-choice knapsack model and the conventional mean-variance optimization model. Our results open up great potential for robust portfolio optimization as an effective tool for practical coupon allocation.
Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation
Authors: Zhutian Lin, Junwei Pan, Haibin Yu, Xi Xiao, Ximei Wang, Zhixiang Feng, Shifeng Wen, Shudong Huang, Dapeng Liu, Lei Xiao
Date: 2024-05-21 00:00:00
Tasks: Disentanglement

Multi-domain learning (MDL) has become a prominent topic in enhancing the quality of personalized services. It's critical to learn commonalities between domains and preserve the distinct characteristics of each domain. However, this leads to a challenging dilemma in MDL. On the one hand, a model needs to leverage domain-aware modules such as experts or embeddings to preserve each domain's distinctiveness. On the other hand, real-world datasets often exhibit long-tailed distributions across domains, where some domains may lack sufficient samples to effectively train their specific modules. Unfortunately, nearly all existing work falls short of resolving this dilemma. To this end, we propose a novel Cross-experts Covariance Loss for Disentangled Learning model (Crocodile), which employs multiple embedding tables to make the model domain-aware at the embeddings which consist most parameters in the model, and a covariance loss upon these embeddings to disentangle them, enabling the model to capture diverse user interests among domains. Empirical analysis demonstrates that our method successfully addresses both challenges and outperforms all state-of-the-art methods on public datasets. During online A/B testing in Tencent's advertising platform, Crocodile achieves 0.72% CTR lift and 0.73% GMV lift on a primary advertising scenario.
RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search
Authors: Jianyang Gao, Cheng Long
Date: 2024-05-21 00:00:00
Tasks: Quantization

Searching for approximate nearest neighbors (ANN) in the high-dimensional Euclidean space is a pivotal problem. Recently, with the help of fast SIMD-based implementations, Product Quantization (PQ) and its variants can often efficiently and accurately estimate the distances between the vectors and have achieved great success in the in-memory ANN search. Despite their empirical success, we note that these methods do not have a theoretical error bound and are observed to fail disastrously on some real-world datasets. Motivated by this, we propose a new randomized quantization method named RaBitQ, which quantizes $D$-dimensional vectors into $D$-bit strings. RaBitQ guarantees a sharp theoretical error bound and provides good empirical accuracy at the same time. In addition, we introduce efficient implementations of RaBitQ, supporting to estimate the distances with bitwise operations or SIMD-based operations. Extensive experiments on real-world datasets confirm that (1) our method outperforms PQ and its variants in terms of accuracy-efficiency trade-off by a clear margin and (2) its empirical performance is well-aligned with our theoretical analysis.
Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search
Authors: Kaixin Ji, Sachin Pathiyan Cherumanal, Johanne R. Trippas, Danula Hettiachchi, Flora D. Salim, Falk Scholer, Damiano Spina
Date: 2024-05-21 00:00:00
Tasks: Conversational Search

Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines. However, user-system interactions in audio-only channels -- as is the case for Spoken Conversational Search (SCS) -- are harder to characterize, given the lack of instruments to effectively and precisely capture interactions. Furthermore, in this era of information overload, cognitive bias can significantly impact how we seek and consume information -- especially in the context of controversial topics or multiple viewpoints. This paper draws upon insights from multiple disciplines (including information seeking, psychology, cognitive science, and wearable sensors) to provoke novel conversations in the community. To this end, we discuss future opportunities and propose a framework including multimodal instruments and methods for experimental designs and settings. We demonstrate preliminary results as an example. We also outline the challenges and offer suggestions for adopting this multimodal approach, including ethical considerations, to assist future researchers and practitioners in exploring cognitive biases in SCS.
Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale
Authors: Sizhuo Li, Martin Brandt, Rasmus Fensholt, Ankit Kariryaa, Christian Igel, Fabian Gieseke, Thomas Nord-Larsen, Stefan Oehmcke, Ask Holm Carlsen, Samuli Junttila, Xiaoye Tong, Alexandre d’Aspremont, Philippe Ciais
Date: 2023-03-09 00:00:00
Proceeding: PNAS Nexus 2023 3
Tasks: Management

Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.
Intelligent and Miniaturized Neural Interfaces: An Emerging Era in Neurotechnology
Authors: Mahsa Shoaran, Uisub Shin, MohammadAli Shaeri
Date: 2024-05-13 00:00:00

Integrating smart algorithms on neural devices presents significant opportunities for various brain disorders. In this paper, we review the latest advancements in the development of three categories of intelligent neural prostheses featuring embedded signal processing on the implantable or wearable device. These include: 1) Neural interfaces for closed-loop symptom tracking and responsive stimulation; 2) Neural interfaces for emerging network-related conditions, such as psychiatric disorders; and 3) Intelligent BMI SoCs for movement recovery following paralysis.
Inner-approximate Reachability Computation via Zonotopic Boundary Analysis
Authors: Dejin Ren, Zhen Liang, Chenyu Wu, Jianqiang Ding, Taoran Wu, Bai Xue
Date: 2024-05-18 00:00:00

Inner-approximate reachability analysis involves calculating subsets of reachable sets, known as inner-approximations. This analysis is crucial in the fields of dynamic systems analysis and control theory as it provides a reliable estimation of the set of states that a system can reach from given initial states at a specific time instant. In this paper, we study the inner-approximate reachability analysis problem based on the set-boundary reachability method for systems modelled by ordinary differential equations, in which the computed inner-approximations are represented with zonotopes. The set-boundary reachability method computes an inner-approximation by excluding states reached from the initial set's boundary. The effectiveness of this method is highly dependent on the efficient extraction of the exact boundary of the initial set. To address this, we propose methods leveraging boundary and tiling matrices that can efficiently extract and refine the exact boundary of the initial set represented by zonotopes. Additionally, we enhance the exclusion strategy by contracting the outer-approximations in a flexible way, which allows for the computation of less conservative inner-approximations. To evaluate the proposed method, we compare it with state-of-the-art methods against a series of benchmarks. The numerical results demonstrate that our method is not only efficient but also accurate in computing inner-approximations.
Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy
Authors: Hao Tang, Brian Xiao, Wenhao He, Pero Subasic, Avetik R. Harutyunyan, Yao Wang, Fang Liu, Haowei Xu, Ju Li
Date: 2024-05-09 00:00:00
Tasks: Multi-Task Learning

Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work, we developed a unified ML method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties. As case studies, we apply the model to aromatic compounds and semiconducting polymers on both ground state and excited state properties, demonstrating its accuracy and generalization capability to complex systems that are hard to calculate using CCSD(T)-level methods.
Multi-speaker Text-to-speech Training with Speaker Anonymized Data
Authors: Wen-Chin Huang, Yi-Chiao Wu, Tomoki Toda
Date: 2024-05-20 00:00:00
Tasks: Speaker anonymization, text-to-speech, Text to Speech

The trend of scaling up speech generation models poses a threat of biometric information leakage of the identities of the voices in the training data, raising privacy and security concerns. In this paper, we investigate training multi-speaker text-to-speech (TTS) models using data that underwent speaker anonymization (SA), a process that tends to hide the speaker identity of the input speech while maintaining other attributes. Two signal processing-based and three deep neural network-based SA methods were used to anonymize VCTK, a multi-speaker TTS dataset, which is further used to train an end-to-end TTS model, VITS, to perform unseen speaker TTS during the testing phase. We conducted extensive objective and subjective experiments to evaluate the anonymized training data, as well as the performance of the downstream TTS model trained using those data. Importantly, we found that UTMOS, a data-driven subjective rating predictor model, and GVD, a metric that measures the gain of voice distinctiveness, are good indicators of the downstream TTS performance. We summarize insights in the hope of helping future researchers determine the goodness of the SA system for multi-speaker TTS training.
Untargeted Adversarial Attack on Knowledge Graph Embeddings
Authors: Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru, Qika Lin, Yuxia Geng, Jun Liu
Date: 2024-05-08 00:00:00
Tasks: Adversarial Attack, Graph Embedding, Knowledge Graph Embedding, Knowledge Graph Embeddings, Link Prediction

Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations on low-quality KGs that are prevalent in the real world. Some recent studies propose adversarial attacks to investigate the vulnerabilities of KGE methods, but their attackers are target-oriented with the KGE method and the target triples to predict are given in advance, which lacks practicability. In this work, we explore untargeted attacks with the aim of reducing the global performances of KGE methods over a set of unknown test triples and conducting systematic analyses on KGE robustness. Considering logic rules can effectively summarize the global structure of a KG, we develop rule-based attack strategies to enhance the attack efficiency. In particular,we consider adversarial deletion which learns rules, applying the rules to score triple importance and delete important triples, and adversarial addition which corrupts the learned rules and applies them for negative triples as perturbations. Extensive experiments on two datasets over three representative classes of KGE methods demonstrate the effectiveness of our proposed untargeted attacks in diminishing the link prediction results. And we also find that different KGE methods exhibit different robustness to untargeted attacks. For example, the robustness of methods engaged with graph neural networks and logic rules depends on the density of the graph. But rule-based methods like NCRL are easily affected by adversarial addition attacks to capture negative rules
Fully Distributed Fog Load Balancing with Multi-Agent Reinforcement Learning
Authors: Maad Ebrahim, Abdelhakim Hafid
Date: 2024-05-15 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Transfer Learning

Real-time Internet of Things (IoT) applications require real-time support to handle the ever-growing demand for computing resources to process IoT workloads. Fog Computing provides high availability of such resources in a distributed manner. However, these resources must be efficiently managed to distribute unpredictable traffic demands among heterogeneous Fog resources. This paper proposes a fully distributed load-balancing solution with Multi-Agent Reinforcement Learning (MARL) that intelligently distributes IoT workloads to optimize the waiting time while providing fair resource utilization in the Fog network. These agents use transfer learning for life-long self-adaptation to dynamic changes in the environment. By leveraging distributed decision-making, MARL agents effectively minimize the waiting time compared to a single centralized agent solution and other baselines, enhancing end-to-end execution delay. Besides performance gain, a fully distributed solution allows for a global-scale implementation where agents can work independently in small collaboration regions, leveraging nearby local resources. Furthermore, we analyze the impact of a realistic frequency to observe the state of the environment, unlike the unrealistic common assumption in the literature of having observations readily available in real-time for every required action. The findings highlight the trade-off between realism and performance using an interval-based Gossip-based multi-casting protocol against assuming real-time observation availability for every generated workload.