pwc-archive/papers-with-abstracts huggingface dataset:
Cost-Effective Retraining of Machine Learning Models
Authors: Ananth Mahadevan, Michael Mathioudakis
Date: 2023-10-06 00:00:00
Tasks: Drift Detection

It is important to retrain a machine learning (ML) model in order to maintain its performance as the data changes over time. However, this can be costly as it usually requires processing the entire dataset again. This creates a trade-off between retraining too frequently, which leads to unnecessary computing costs, and not retraining often enough, which results in stale and inaccurate ML models. To address this challenge, we propose ML systems that make automated and cost-effective decisions about when to retrain an ML model. We aim to optimize the trade-off by considering the costs associated with each decision. Our research focuses on determining whether to retrain or keep an existing ML model based on various factors, including the data, the model, and the predictive queries answered by the model. Our main contribution is a Cost-Aware Retraining Algorithm called Cara, which optimizes the trade-off over streams of data and queries. To evaluate the performance of Cara, we analyzed synthetic datasets and demonstrated that Cara can adapt to different data drifts and retraining costs while performing similarly to an optimal retrospective algorithm. We also conducted experiments with real-world datasets and showed that Cara achieves better accuracy than drift detection baselines while making fewer retraining decisions, ultimately resulting in lower total costs.
On the Two Sides of Redundancy in Graph Neural Networks
Authors: Franka Bause, Samir Moustafa, Johannes Langguth, Wilfried N. Gansterer, Nils M. Kriege
Date: 2023-10-06 00:00:00

Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes is included. However, node embeddings may be unable to represent the growing node neighborhoods accurately and the influence of distant nodes may vanish, a problem referred to as oversquashing. Information redundancy in message passing, i.e., the repetitive exchange and encoding of identical information amplifies oversquashing. We develop a novel aggregation scheme based on neighborhood trees, which allows for controlling redundancy by pruning redundant branches of unfolding trees underlying standard message passing. While the regular structure of unfolding trees allows the reuse of intermediate results in a straightforward way, the use of neighborhood trees poses computational challenges. We propose compact representations of neighborhood trees and merge them, exploiting computational redundancy by identifying isomorphic subtrees. From this, node and graph embeddings are computed via a neural architecture inspired by tree canonization techniques. Our method is less susceptible to oversquashing than traditional message passing neural networks and can improve the accuracy on widely used benchmark datasets.
Entropic Score metric: Decoupling Topology and Size in Training-free NAS
Authors: Niccolò Cavagnero, Luca Robbiano, Francesca Pistilli, Barbara Caputo, Giuseppe Averta
Date: 2023-10-06 00:00:00
Tasks: GPU, Neural Architecture Search

Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models. Neural Architecture Search is a promising approach to automate this process, but existing competitive methods require large training time and computational resources to generate accurate models. To overcome these limits, this paper contributes with: i) a novel training-free metric, named Entropic Score, to estimate model expressivity through the aggregated element-wise entropy of its activations; ii) a cyclic search algorithm to separately yet synergistically search model size and topology. Entropic Score shows remarkable ability in searching for the topology of the network, and a proper combination with LogSynflow, to search for model size, yields superior capability to completely design high-performance Hybrid Transformers for edge applications in less than 1 GPU hour, resulting in the fastest and most accurate NAS method for ImageNet classification.
Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions
Authors: Udo Schlegel, Daniel A. Keim
Date: 2023-10-06 00:00:00
Tasks: Time Series, Time Series Classification, Weather Forecasting

Given the increasing amount and general complexity of time series data in domains such as finance, weather forecasting, and healthcare, there is a growing need for state-of-the-art performance models that can provide interpretable insights into underlying patterns and relationships. Attribution techniques enable the extraction of explanations from time series models to gain insights but are hard to evaluate for their robustness and trustworthiness. We propose the Attribution Stability Indicator (ASI), a measure to incorporate robustness and trustworthiness as properties of attribution techniques for time series into account. We extend a perturbation analysis with correlations of the original time series to the perturbed instance and the attributions to include wanted properties in the measure. We demonstrate the wanted properties based on an analysis of the attributions in a dimension-reduced space and the ASI scores distribution over three whole time series classification datasets.
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
Authors: Heehyeon Kim, Jinhyeok Choi, Joyce Jiyoung Whang
Date: 2023-10-06 00:00:00
Tasks: Fraud Detection, Graph Attention, Relation

Fraud detection aims to discover fraudsters deceiving other users by, for example, leaving fake reviews or making abnormal transactions. Graph-based fraud detection methods consider this task as a classification problem with two classes: frauds or normal. We address this problem using Graph Neural Networks (GNNs) by proposing a dynamic relation-attentive aggregation mechanism. Based on the observation that many real-world graphs include different types of relations, we propose to learn a node representation per relation and aggregate the node representations using a learnable attention function that assigns a different attention coefficient to each relation. Furthermore, we combine the node representations from different layers to consider both the local and global structures of a target node, which is beneficial to improving the performance of fraud detection on graphs with heterophily. By employing dynamic graph attention in all the aggregation processes, our method adaptively computes the attention coefficients for each node. Experimental results show that our method, DRAG, outperforms state-of-the-art fraud detection methods on real-world benchmark datasets.
Amortized Network Intervention to Steer the Excitatory Point Processes
Authors: Zitao Song, Wendi Ren, Shuang Li
Date: 2023-10-06 00:00:00
Tasks: Decision Making, Model-based Reinforcement Learning, Model Predictive Control, Point Processes, Transfer Learning

Excitatory point processes (i.e., event flows) occurring over dynamic graphs (i.e., evolving topologies) provide a fine-grained model to capture how discrete events may spread over time and space. How to effectively steer the event flows by modifying the dynamic graph structures presents an interesting problem, motivated by curbing the spread of infectious diseases through strategically locking down cities to mitigating traffic congestion via traffic light optimization. To address the intricacies of planning and overcome the high dimensionality inherent to such decision-making problems, we design an Amortized Network Interventions (ANI) framework, allowing for the pooling of optimal policies from history and other contexts while ensuring a permutation equivalent property. This property enables efficient knowledge transfer and sharing across diverse contexts. Each task is solved by an H-step lookahead model-based reinforcement learning, where neural ODEs are introduced to model the dynamics of the excitatory point processes. Instead of simulating rollouts from the dynamics model, we derive an analytical mean-field approximation for the event flows given the dynamics, making the online planning more efficiently solvable. We empirically illustrate that this ANI approach substantially enhances policy learning for unseen dynamics and exhibits promising outcomes in steering event flows through network intervention using synthetic and real COVID datasets.
From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying
Authors: Biao Wu, Qiang Huang, Anthony K. H. Tung
Date: 2023-10-06 00:00:00

Safeguarding the Intellectual Property (IP) of data has become critically important as machine learning applications continue to proliferate, and their success heavily relies on the quality of training data. While various mechanisms exist to secure data during storage, transmission, and consumption, fewer studies have been developed to detect whether they are already leaked for model training without authorization. This issue is particularly challenging due to the absence of information and control over the training process conducted by potential attackers. In this paper, we concentrate on the domain of tabular data and introduce a novel methodology, Local Distribution Shifting Synthesis (\textsc{LDSS}), to detect leaked data that are used to train classification models. The core concept behind \textsc{LDSS} involves injecting a small volume of synthetic data--characterized by local shifts in class distribution--into the owner's dataset. This enables the effective identification of models trained on leaked data through model querying alone, as the synthetic data injection results in a pronounced disparity in the predictions of models trained on leaked and modified datasets. \textsc{LDSS} is \emph{model-oblivious} and hence compatible with a diverse range of classification models. We have conducted extensive experiments on seven types of classification models across five real-world datasets. The comprehensive results affirm the reliability, robustness, fidelity, security, and efficiency of \textsc{LDSS}. Extending \textsc{LDSS} to regression tasks further highlights its versatility and efficacy compared with baseline methods.
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Authors: Daniela Thyssens, Tim Dernedde, Jonas K. Falkner, Lars Schmidt-Thieme
Date: 2023-10-06 00:00:00
Tasks: Combinatorial Optimization

Neural Combinatorial Optimization has been researched actively in the last eight years. Even though many of the proposed Machine Learning based approaches are compared on the same datasets, the evaluation protocol exhibits essential flaws and the selection of baselines often neglects State-of-the-Art Operations Research approaches. To improve on both of these shortcomings, we propose the Routing Arena, a benchmark suite for Routing Problems that provides a seamless integration of consistent evaluation and the provision of baselines and benchmarks prevalent in the Machine Learning- and Operations Research field. The proposed evaluation protocol considers the two most important evaluation cases for different applications: First, the solution quality for an a priori fixed time budget and secondly the anytime performance of the respective methods. By setting the solution trajectory in perspective to a Best Known Solution and a Base Solver's solutions trajectory, we furthermore propose the Weighted Relative Average Performance (WRAP), a novel evaluation metric that quantifies the often claimed runtime efficiency of Neural Routing Solvers. A comprehensive first experimental evaluation demonstrates that the most recent Operations Research solvers generate state-of-the-art results in terms of solution quality and runtime efficiency when it comes to the vehicle routing problem. Nevertheless, some findings highlight the advantages of neural approaches and motivate a shift in how neural solvers should be conceptualized.
Automatic Aspect Extraction from Scientific Texts
Authors: Anna Marshalova, Elena Bruches, Tatiana Batura
Date: 2023-10-06 00:00:00
Tasks: Aspect Extraction

Being able to extract from scientific papers their main points, key insights, and other important information, referred to here as aspects, might facilitate the process of conducting a scientific literature review. Therefore, the aim of our research is to create a tool for automatic aspect extraction from Russian-language scientific texts of any domain. In this paper, we present a cross-domain dataset of scientific texts in Russian, annotated with such aspects as Task, Contribution, Method, and Conclusion, as well as a baseline algorithm for aspect extraction, based on the multilingual BERT model fine-tuned on our data. We show that there are some differences in aspect representation in different domains, but even though our model was trained on a limited number of scientific domains, it is still able to generalize to new domains, as was proved by cross-domain experiments. The code and the dataset are available at \url{https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts}.
How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation
Authors: Josh Alman, Zhao Song
Date: 2023-10-06 00:00:00

In the classical transformer attention scheme, we are given three $n \times d$ size matrices $Q, K, V$ (the query, key, and value tokens), and the goal is to compute a new $n \times d$ size matrix $D^{-1} \exp(QK^\top) V$ where $D = \mathrm{diag}( \exp(QK^\top) {\bf 1}_n )$. In this work, we study a generalization of attention which captures triple-wise correlations. This generalization is able to solve problems about detecting triple-wise connections that were shown to be impossible for transformers. The potential downside of this generalization is that it appears as though computations are even more difficult, since the straightforward algorithm requires cubic time in $n$. However, we show that in the bounded-entry setting (which arises in practice, and which is well-studied in both theory and practice), there is actually a near-linear time algorithm. More precisely, we show that bounded entries are both necessary and sufficient for quickly performing generalized computations: $\bullet$ On the positive side, if all entries of the input matrices are bounded above by $o(\sqrt[3]{\log n})$ then we show how to approximate the ``tensor-type'' attention matrix in $n^{1+o(1)}$ time. $\bullet$ On the negative side, we show that if the entries of the input matrices may be as large as $\Omega(\sqrt[3]{\log n})$, then there is no algorithm that runs faster than $n^{3-o(1)}$ (assuming the Strong Exponential Time Hypothesis from fine-grained complexity theory). We also show that our construction, algorithms, and lower bounds naturally generalize to higher-order tensors and correlations. Interestingly, the higher the order of the tensors, the lower the bound on the entries needs to be for an efficient algorithm. Our results thus yield a natural tradeoff between the boundedness of the entries, and order of the tensor one may use for more expressive, efficient attention computation.
DEFT: A new distance-based feature set for keystroke dynamics
Authors: Nuwan Kaluarachchi, Sevvandi Kandanaarachchi, Kristen Moore, Arathi Arakala
Date: 2023-10-06 00:00:00
Tasks: User Identification

Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person's typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.
AUTOPARLLM: GNN-Guided Automatic Code Parallelization using Large Language Models
Authors: Quazi Ishtiaque Mahmud, Ali TehraniJamsaz, Hung D Phan, Nesreen K. Ahmed, Ali Jannesari
Date: 2023-10-06 00:00:00
Tasks: Code Generation, Graph Neural Network

Parallelizing sequentially written programs is a challenging task. Even experienced developers need to spend considerable time finding parallelism opportunities and then actually writing parallel versions of sequentially written programs. To address this issue, we present AUTOPARLLM, a framework for automatically discovering parallelism and generating the parallel version of the sequentially written program. Our framework consists of two major components: i) a heterogeneous Graph Neural Network (GNN) based parallelism discovery and parallel pattern detection module, and ii) an LLM-based code generator to generate the parallel counterpart of the sequential programs. We use the GNN to learn the flow-aware characteristics of the programs to identify parallel regions in sequential programs and then construct an enhanced prompt using the GNN's results for the LLM-based generator to finally produce the parallel counterparts of the sequential programs. We evaluate AUTOPARLLM on 11 applications of 2 well-known benchmark suites: NAS Parallel Benchmark and Rodinia Benchmark. Our results show that AUTOPARLLM is indeed effective in improving the state-of-the-art LLM-based models for the task of parallel code generation in terms of multiple code generation metrics. AUTOPARLLM also improves the average runtime of the parallel code generated by the state-of-the-art LLMs by as high as 3.4% and 2.9% for the NAS Parallel Benchmark and Rodinia Benchmark respectively. Additionally, to overcome the issue that well-known metrics for translation evaluation have not been optimized to evaluate the quality of the generated parallel code, we propose OMPScore for evaluating the quality of the generated code. We show that OMPScore exhibits a better correlation with human judgment than existing metrics, measured by up to 75% improvement of Spearman correlation.
Observation-Guided Diffusion Probabilistic Models
Authors: Junoh Kang, Jinyoung Choi, Sungik Choi, Bohyung Han
Date: 2023-10-06 00:00:00
Proceeding: CVPR 2024 1
Tasks: Denoising, Image Generation

We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM), which effectively addresses the tradeoff between quality control and fast sampling. Our approach reestablishes the training objective by integrating the guidance of the observation process with the Markov chain in a principled way. This is achieved by introducing an additional loss term derived from the observation based on a conditional discriminator on noise level, which employs a Bernoulli distribution indicating whether its input lies on the (noisy) real manifold or not. This strategy allows us to optimize the more accurate negative log-likelihood induced in the inference stage especially when the number of function evaluations is limited. The proposed training scheme is also advantageous even when incorporated only into the fine-tuning process, and it is compatible with various fast inference strategies since our method yields better denoising networks using the exactly the same inference procedure without incurring extra computational cost. We demonstrate the effectiveness of our training algorithm using diverse inference techniques on strong diffusion model baselines. Our implementation is available at https://github.com/Junoh-Kang/OGDM_edm.
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
Authors: Wei Lv, Chao Zhang, Huaxiong Li, Xiuyi Jia, Chunlin Chen
Date: 2023-10-06 00:00:00
Tasks: Clustering, Incomplete multi-view clustering, Tensor Decomposition

Incomplete multi-view clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multi-view data and seek complete graphs by exploring the incomplete subgraphs of each view for spectral clustering. However, the graphs constructed on the original high-dimensional data may be suboptimal due to feature redundancy and noise. Besides, previous methods generally ignored the graph noise caused by the inter-class and intra-class structure variation during the transformation of incomplete graphs and complete graphs. To address these problems, we propose a novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD) for IMVC. Specifically, to alleviate the influence of redundant features and noise in high-dimensional data, JPLTD introduces an orthogonal projection matrix to project the high-dimensional features into a lower-dimensional space for compact feature learning.Meanwhile, based on the lower-dimensional space, the similarity graphs corresponding to instances of different views are learned, and JPLTD stacks these graphs into a third-order low-rank tensor to explore the high-order correlations across different views. We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering.JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic tensor models the true data similarities. An effective optimization algorithm is adopted to solve the JPLTD model. Comprehensive experiments on several benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.
Genetic prediction of quantitative traits: a machine learner's guide focused on height
Authors: Lucie Bourguignon, Caroline Weis, Catherine R. Jutzeler, Michael Adamer
Date: 2023-10-06 00:00:00
Tasks: feature selection, Protein Folding

Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics.