pwc-archive/papers-with-abstracts huggingface dataset:
Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection
Authors: Ting Long, Yutong Xie, Xianyu Chen, Weinan Zhang, Qinxiang Cao, Yong Yu
Date: 2022-02-25 00:00:00
Tasks: Graph Neural Network

Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Recent work tries to represent programs with neural networks based on source code structures. However, such methods often focus on the syntax and consider only one single perspective of programs, limiting the representation power of models. This paper proposes a multi-view graph (MVG) program representation method. MVG pays more attention to code semantics and simultaneously includes both data flow and control flow as multiple views. These views are then combined and processed by a graph neural network (GNN) to obtain a comprehensive program representation that covers various aspects. We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP. Specifically, we use a public dataset POJ-104 and also construct a new challenging dataset ALG-109 to test our method. In experiments, MVG outperforms previous methods significantly, demonstrating our model's strong capability of representing source code.
Consolidated Adaptive T-soft Update for Deep Reinforcement Learning
Authors: Taisuke Kobayashi
Date: 2022-02-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Demand for deep reinforcement learning (DRL) is gradually increased to enable robots to perform complex tasks, while DRL is known to be unstable. As a technique to stabilize its learning, a target network that slowly and asymptotically matches a main network is widely employed to generate stable pseudo-supervised signals. Recently, T-soft update has been proposed as a noise-robust update rule for the target network and has contributed to improving the DRL performance. However, the noise robustness of T-soft update is specified by a hyperparameter, which should be tuned for each task, and is deteriorated by a simplified implementation. This study develops adaptive T-soft (AT-soft) update by utilizing the update rule in AdaTerm, which has been developed recently. In addition, the concern that the target network does not asymptotically match the main network is mitigated by a new consolidation for bringing the main network back to the target network. This so-called consolidated AT-soft (CAT-soft) update is verified through numerical simulations.
A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation
Authors: Rezaur Rahman, Samiul Hasan
Date: 2022-02-25 00:00:00
Tasks: Management, Traffic Prediction, Transfer Learning

Proactive evacuation traffic management largely depends on real-time monitoring and prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic prediction is challenging due to the uncertainties caused by sudden changes in projected hurricane paths and consequently household evacuation behavior. Moreover, modeling spatiotemporal traffic flow patterns requires extensive data over a longer time period, whereas evacuations typically last for 2 to 5 days. In this paper, we present a novel data-driven approach for predicting evacuation traffic at a network scale. We develop a dynamic graph convolution LSTM (DGCN-LSTM) model to learn the network dynamics of hurricane evacuation. We first train the model for non-evacuation period traffic data showing that the model outperforms existing deep learning models for predicting non-evacuation period traffic with an RMSE value of 226.84. However, when we apply the model for evacuation period, the RMSE value increased to 1440.99. We overcome this issue by adopting a transfer learning approach with additional features related to evacuation traffic demand such as distance from the evacuation zone, time to landfall, and other zonal level features to control the transfer of information (network dynamics) from non-evacuation periods to evacuation periods. The final transfer learned DGCN-LSTM model performs well to predict evacuation traffic flow (RMSE=399.69). The implemented model can be applied to predict evacuation traffic over a longer forecasting horizon (6 hour). It will assist transportation agencies to activate appropriate traffic management strategies to reduce delays for evacuating traffic.
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Authors: Pantelis Elinas, Edwin V. Bonilla
Date: 2022-02-25 00:00:00
Tasks: Graph Property Prediction, Property Prediction

Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task. It is known that deep GNNs suffer from over-smoothing where, as the number of layers increases, node representations become nearly indistinguishable and model performance on the downstream task degrades significantly. To address this problem, we propose deeply-supervised GNNs (DSGNNs), i.e., GNNs enhanced with deep supervision where representations learned at all layers are used for training. We show empirically that DSGNNs are resilient to over-smoothing and can outperform competitive benchmarks on node and graph property prediction problems.
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest
Authors: Shucen Ma, Jianqi Shi, Yanhong Huang, Shengchao Qin, Zhe Hou
Date: 2022-02-25 00:00:00
Tasks: Feature Importance, Logical Reasoning

The broad adoption of Machine Learning (ML) in security-critical fields demands the explainability of the approach. However, the research on understanding ML models, such as Random Forest (RF), is still in its infant stage. In this work, we leverage formal methods and logical reasoning to develop a novel model-specific method for explaining the prediction of RF. Our approach is centered around Minimal Unsatisfiable Cores (MUC) and provides a comprehensive solution for feature importance, covering local and global aspects, and adversarial sample analysis. Experimental results on several datasets illustrate the high quality of our feature importance measurement. We also demonstrate that our adversarial analysis outperforms the state-of-the-art method. Moreover, our method can produce a user-centered report, which helps provide recommendations in real-life applications.
Raman Spectrum Matching with Contrastive Representation Learning
Authors: Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm
Date: 2022-02-25 00:00:00
Tasks: BIG-bench Machine Learning, Conformal Prediction, Representation Learning

Raman spectroscopy is an effective, low-cost, non-intrusive technique often used for chemical identification. Typical approaches are based on matching observations to a reference database, which requires careful preprocessing, or supervised machine learning, which requires a fairly large number of training observations from each class. We propose a new machine learning technique for Raman spectrum matching, based on contrastive representation learning, that requires no preprocessing and works with as little as a single reference spectrum from each class. On three datasets we demonstrate that our approach significantly improves or is on par with the state of the art in prediction accuracy, and we show how to compute conformal prediction sets with specified frequentist coverage. Based on our findings, we believe contrastive representation learning is a promising alternative to existing methods for Raman spectrum matching.
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization
Authors: Weijia Zhang, Xuanhui Zhang, Han-Wen Deng, Min-Ling Zhang
Date: 2022-02-25 00:00:00
Tasks: Multiple Instance Learning, Out-of-Distribution Generalization, Representation Learning

Multi-instance learning (MIL) deals with objects represented as bags of instances and can predict instance labels from bag-level supervision. However, significant performance gaps exist between instance-level MIL algorithms and supervised learners since the instance labels are unavailable in MIL. Most existing MIL algorithms tackle the problem by treating multi-instance bags as harmful ambiguities and predicting instance labels by reducing the supervision inexactness. This work studies MIL from a new perspective by considering bags as auxiliary information, and utilize it to identify instance-level causal representations from bag-level weak supervision. We propose the CausalMIL algorithm, which not only excels at instance label prediction but also provides robustness to distribution change by synergistically integrating MIL with identifiable variational autoencoder. Our approach is based on a practical and general assumption: the prior distribution over the instance latent representations belongs to the non-factorized exponential family conditioning on the multi-instance bags. Experiments on synthetic and real-world datasets demonstrate that our approach significantly outperforms various baselines on instance label prediction and out-of-distribution generalization tasks.
Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression
Authors: Diksha Garg, Pankaj Malhotra, Anil Bhatia, Sanjay Bhat, Lovekesh Vig, Gautam Shroff
Date: 2022-02-25 00:00:00
Tasks: regression

We consider learning a trading agent acting on behalf of the treasury of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC). The goal of the agent is to maximize the expected HC at the end of the trading episode by deciding to hold or sell the FC at each time step in the trading episode. We pose this as an optimization problem, and consider a broad spectrum of approaches with the learning component ranging from supervised to imitation to reinforcement learning. We observe that most of the approaches considered struggle to improve upon simple heuristic baselines. We identify two key aspects of the problem that render standard solutions ineffective - i) while good forecasts of future FX rates can be highly effective in guiding good decisions, forecasting FX rates is difficult, and erroneous estimates tend to degrade the performance of trading agents instead of improving it, ii) the inherent non-stationary nature of FX rates renders a fixed decision-threshold highly ineffective. To address these problems, we propose a novel supervised learning approach that learns to forecast the top-K future FX rates instead of forecasting all the future FX rates, and bases the hold-versus-sell decision on the forecasts (e.g. hold if future FX rate is higher than current FX rate, sell otherwise). Furthermore, to handle the non-stationarity in the FX rates data which poses challenges to the i.i.d. assumption in supervised learning methods, we propose to adaptively learn decision-thresholds based on recent historical episodes. Through extensive empirical evaluation, we show that our approach is the only approach which is able to consistently improve upon a simple heuristic baseline. Further experiments show the inefficacy of state-of-the-art statistical and deep-learning-based forecasting methods as they degrade the performance of the trading agent.
Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting
Authors: Jiabin Tang, Tang Qian, Shijing Liu, Shengdong Du, Jie Hu, Tianrui Li
Date: 2022-02-25 00:00:00
Tasks: Benchmarking, Graph Neural Network, Graph structure learning

Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing. Recently, Graph Neural Network truly outperforms the traditional methods. Nevertheless, the most conventional GNN-based model works well while given a pre-defined graph structure. And the existing methods of defining the graph structures focus purely on spatial dependencies and ignore the temporal correlation. Besides, the semantics of the static pre-defined graph adjacency applied during the whole training progress is always incomplete, thus overlooking the latent topologies that may fine-tune the model. To tackle these challenges, we propose a new traffic forecasting framework -- Spatio-Temporal Latent Graph Structure Learning networks (ST-LGSL). More specifically, the model employs a graph generator based on Multilayer perceptron and K-Nearest Neighbor, which learns the latent graph topological information from the entire data considering both spatial and temporal dynamics. Furthermore, with the initialization of MLP-kNN based on ground-truth adjacency matrix and similarity metric in kNN, ST-LGSL aggregates the topologies focusing on geography and node similarity. Additionally, the generated graphs act as the input of the Spatio-temporal prediction module combined with the Diffusion Graph Convolutions and Gated Temporal Convolutions Networks. Experimental results on two benchmarking datasets in real world demonstrate that ST-LGSL outperforms various types of state-of-art baselines.
PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine
Authors: Alexander Van Craen, Marcel Breyer, Dirk Pflüger
Date: 2022-02-25 00:00:00
Tasks: CPU

Machine learning algorithms must be able to efficiently cope with massive data sets. Therefore, they have to scale well on any modern system and be able to exploit the computing power of accelerators independent of their vendor. In the field of supervised learning, Support Vector Machines (SVMs) are widely used. However, even modern and optimized implementations such as LIBSVM or ThunderSVM do not scale well for large non-trivial dense data sets on cutting-edge hardware: Most SVM implementations are based on Sequential Minimal Optimization, an optimized though inherent sequential algorithm. Hence, they are not well-suited for highly parallel GPUs. Furthermore, we are not aware of a performance portable implementation that supports CPUs and GPUs from different vendors. We have developed the PLSSVM library to solve both issues. First, we resort to the formulation of the SVM as a least squares problem. Training an SVM then boils down to solving a system of linear equations for which highly parallel algorithms are known. Second, we provide a hardware independent yet efficient implementation: PLSSVM uses different interchangeable backends--OpenMP, CUDA, OpenCL, SYCL--supporting modern hardware from various vendors like NVIDIA, AMD, or Intel on multiple GPUs. PLSSVM can be used as a drop-in replacement for LIBSVM. We observe a speedup on CPUs of up to 10 compared to LIBSVM and on GPUs of up to 14 compared to ThunderSVM. Our implementation scales on many-core CPUs with a parallel speedup of 74.7 on up to 256 CPU threads and on multiple GPUs with a parallel speedup of 3.71 on four GPUs. The code, utility scripts, and documentation are all available on GitHub: https://github.com/SC-SGS/PLSSVM.
Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection
Authors: Thabang Mathonsi, Terence L van Zyl
Date: 2022-02-25 00:00:00
Tasks: Anomaly Detection, Deep Learning, Prediction Intervals

Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks, and at quantifying the uncertainty associated with those forecasts (prediction intervals). One example is Multivariate Exponential Smoothing Long Short-Term Memory (MES-LSTM), a hybrid between a multivariate statistical forecasting model and a Recurrent Neural Network variant, Long Short-Term Memory. It has also been shown that a model that ($i$) produces accurate forecasts and ($ii$) is able to quantify the associated predictive uncertainty satisfactorily, can be successfully adapted to a model suitable for anomaly detection tasks. With the increasing ubiquity of multivariate data and new application domains, there have been numerous anomaly detection methods proposed in recent years. The proposed methods have largely focused on deep learning techniques, which are prone to suffer from challenges such as ($i$) large sets of parameters that may be computationally intensive to tune, $(ii)$ returning too many false positives rendering the techniques impractical for use, $(iii)$ requiring labeled datasets for training which are often not prevalent in real life, and ($iv$) understanding of the root causes of anomaly occurrences inhibited by the predominantly black-box nature of deep learning methods. In this article, an extension of MES-LSTM is presented, an interpretable anomaly detection model that overcomes these challenges. With a focus on renewable energy generation as an application domain, the proposed approach is benchmarked against the state-of-the-art. The findings are that MES-LSTM anomaly detector is at least competitive to the benchmarks at anomaly detection tasks, and less prone to learning from spurious effects than the benchmarks, thus making it more reliable at root cause discovery and explanation.
FedCAT: Towards Accurate Federated Learning via Device Concatenation
Authors: Ming Hu, Tian Liu, Zhiwei Ling, Zhihao Yue, Mingsong Chen
Date: 2022-02-23 00:00:00
Tasks: Federated Learning

As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy. However, for non-IID scenarios, the classification accuracy of FL models decreases drastically due to the weight divergence caused by data heterogeneity. Although various FL variants have been studied to improve model accuracy, most of them still suffer from the problem of non-negligible communication and computation overhead. In this paper, we introduce a novel FL approach named Fed-Cat that can achieve high model accuracy based on our proposed device selection strategy and device concatenation-based local training method. Unlike conventional FL methods that aggregate local models trained on individual devices, FedCat periodically aggregates local models after their traversals through a series of logically concatenated devices, which can effectively alleviate the model weight divergence problem. Comprehensive experimental results on four well-known benchmarks show that our approach can significantly improve the model accuracy of state-of-the-art FL methods without causing extra communication overhead.
Behaviorally Grounded Model-Based and Model Free Cost Reduction in a Simulated Multi-Echelon Supply Chain
Authors: James Paine
Date: 2022-02-25 00:00:00
Tasks: Management, model

Amplification and phase shift in ordering signals, commonly referred to as bullwhip, are responsible for both excessive strain on real world inventory management systems, stock outs, and unnecessary capital reservation though safety stock building. Bullwhip is a classic, yet persisting, problem with reverberating consequences in inventory management. Research on bullwhip has consistently emphasized behavioral influences for this phenomenon and leveraged behavioral ordering models to suggest interventions. However more recent model-free approaches have also seen success. In this work, the author develops algorithmic approaches towards mitigating bullwhip using both behaviorally grounded model-based approaches alongside a model-free dual deep Q-network reinforcement learning approach. In addition to exploring the utility of this specific model-free architecture to multi-echelon supply chains with imperfect information sharing and information delays, the author directly compares the performance of these model-based and model-free approaches. In doing so, this work highlights both the insights gained from exploring model-based approaches in the context of prior behavioral operations management literature and emphasizes the complementary nature of model-based and model-free approaches in approaching behaviorally grounded supply chain management problems.
Benchmark Assessment for DeepSpeed Optimization Library
Authors: Gongbo Liang, Izzat Alsmadi
Date: 2022-02-12 00:00:00

Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics. The size of such datasets and the complexity of DL models cause such models to be complex, consuming large amount of resources and time to train. Many recent libraries and applications are introduced to deal with DL complexity and efficiency issues. In this paper, we evaluated one example, Microsoft DeepSpeed library through classification tasks. DeepSpeed public sources reported classification performance metrics on the LeNet architecture. We extended this through evaluating the library on several modern neural network architectures, including convolutional neural networks (CNNs) and Vision Transformer (ViT). Results indicated that DeepSpeed, while can make improvements in some of those cases, it has no or negative impact on others.
Dynamic Regret of Online Mirror Descent for Relatively Smooth Convex Cost Functions
Authors: Nima Eshraghi, Ben Liang
Date: 2022-02-25 00:00:00

The performance of online convex optimization algorithms in a dynamic environment is often expressed in terms of the dynamic regret, which measures the decision maker's performance against a sequence of time-varying comparators. In the analysis of the dynamic regret, prior works often assume Lipschitz continuity or uniform smoothness of the cost functions. However, there are many important cost functions in practice that do not satisfy these conditions. In such cases, prior analyses are not applicable and fail to guarantee the optimization performance. In this letter, we show that it is possible to bound the dynamic regret, even when neither Lipschitz continuity nor uniform smoothness is present. We adopt the notion of relative smoothness with respect to some user-defined regularization function, which is a much milder requirement on the cost functions. We first show that under relative smoothness, the dynamic regret has an upper bound based on the path length and functional variation. We then show that with an additional condition of relatively strong convexity, the dynamic regret can be bounded by the path length and gradient variation. These regret bounds provide performance guarantees to a wide variety of online optimization problems that arise in different application domains. Finally, we present numerical experiments that demonstrate the advantage of adopting a regularization function under which the cost functions are relatively smooth.
An initial alignment between neural network and target is needed for gradient descent to learn
Authors: Emmanuel Abbe, Elisabetta Cornacchia, Jan Hązła, Christopher Marquis
Date: 2022-02-25 00:00:00

This paper introduces the notion of ``Initial Alignment'' (INAL) between a neural network at initialization and a target function. It is proved that if a network and a Boolean target function do not have a noticeable INAL, then noisy gradient descent on a fully connected network with normalized i.i.d. initialization will not learn in polynomial time. Thus a certain amount of knowledge about the target (measured by the INAL) is needed in the architecture design. This also provides an answer to an open problem posed in [AS20]. The results are based on deriving lower-bounds for descent algorithms on symmetric neural networks without explicit knowledge of the target function beyond its INAL.