Reviewerly/exHarmony huggingface dataset:
Reducing the Planning Horizon Through Reinforcement Learning

Planning is a computationally expensive process, which can limit the reactivity of autonomous agents. Planning problems are usually solved in isolation, independently of similar, previously solved problems. The depth of search that a planner requires to find a solution, known as the planning horizon, is a critical factor when integrating planners into reactive agents. We consider the case of an agent repeatedly carrying out a task from different initial states. We propose a combination of classical planning and model-free reinforcement learning to reduce the planning horizon over time. Control is smoothly transferred from the planner to the model-free policy as the agent compiles the planner’s policy into a value function. Local exploration of the model-free policy allows the agent to adapt to the environment and eventually overcome model inaccuracies. We evaluate the efficacy of our framework on symbolic PDDL domains and a stochastic grid world environment and show that we are able to significantly reduce the planning horizon while improving upon model inaccuracies.
Context Abstraction to Improve Decentralized Machine Learning in Structured Sensing Environments

In Internet of Things applications, data generated from devices with different characteristics and located at different positions are embedded into different contexts. This poses major challenges for decentralized machine learning as the data distribution across these devices and locations requires consideration for the invariants that characterize them, e.g., in activity recognition applications, the acceleration recorded by hand device must be corrected by the invariant related to the movement of the hand relative to the body. In this article, we propose a new approach that abstracts the exact context surrounding data generators and improves the reconciliation process for decentralized machine learning. Local learners are trained to decompose the learned representations into (i) universal components shared among devices and locations and (ii) local components that capture the specific context of device and location dependencies. The explicit representation of the relative geometry of devices through the special Euclidean Group SE(3) imposes additional constraints that improve the decomposition process. Comprehensive experimental evaluations are carried out on sensor-based activity recognition datasets featuring multi-location and multi-device data collected in a structured sensing environment. Obtained results show the superiority of the proposed method compared with the advanced solutions.
Constrained Multiagent Reinforcement Learning for Large Agent Population

Learning control policies for a large number of agents in a decentralized setting is challenging due to partial observability, uncertainty in the environment, and scalability challenges. While several scalable multiagent RL (MARL) methods have been proposed, relatively few approaches exist for large scale constrained MARL settings. To address this, we first formulate the constrained MARL problem in a collective multiagent setting where interactions among agents are governed by the aggregate count and types of agents, and do not depend on agents’ specific identities. Second, we show that standard Lagrangian relaxation methods, which are popular for single agent RL, do not perform well in constrained MARL settings due to the problem of credit assignment—how to identify and modify behavior of agents that contribute most to constraint violations (and also optimize primary objective alongside)? We develop a fictitious MARL method that addresses this key challenge. Finally, we evaluate our approach on two large-scale real-world applications: maritime traffic management and vehicular network routing. Empirical results show that our approach is highly scalable, can optimize the cumulative global reward and effectively minimize constraint violations, while also being significantly more sample efficient than previous best methods.
Session-Based Recommendation Along with the Session Style of Explanation

Explainability of recommendation algorithms is becoming an important characteristic in GDPR Europe. There are algorithms that try to provide explanations over graphs along with recommendations, but without focusing in user session information. In this paper, we study the problem of news recommendations using a heterogeneous graph and try to infer similarities between entities (i.e., sessions, articles, etc.) for predicting the next user click inside a user session. Moreover, we exploit meta paths to reveal semantic context about the session-article interactions and provide more accurate article recommendations along with robust explanations. We have experimentally compared our method against state-of-the-art algorithms on three real-life datasets. Our method outperforms its competitors in both accuracy and explainability. Finally, we have run a user study to measure the users’ satisfaction over different explanation styles and to find which explanations really help users to make more accurate decisions.
Detecting Anomalies with Autoencoders on Data Streams

Autoencoders have achieved impressive results in anomaly detection tasks by identifying anomalous data as instances that do not match their learned representation of normality. To this end, autoencoders are typically trained on large amounts of previously collected data before being deployed. However, in an online learning scenario, where a predictor has to operate on an evolving data stream and therefore continuously adapt to new instances, this approach is inadequate. Despite their success in offline anomaly detection, there has been little research leveraging autoencoders as anomaly detectors in such a setting. Therefore, in this work, we propose an approach for online anomaly detection with autoencoders and demonstrate its competitiveness against established online anomaly detection algorithms on multiple real-world datasets. We further address the issue of autoencoders gradually adapting to anomalies and thereby reducing their sensitivity to such data by introducing a simple modification to the models’ training approach. Our experimental results indicate that our solution achieves a larger gap between the losses on anomalous and normal instances than a conventional training procedure.
Robust High-Dimensional Time-Varying Coefficient Estimation

In this paper, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedure such as LASSO, we additionally handle the heavy-tailedness of high-frequency observations as well as time variations of coefficient processes. Specifically, we employ Huber loss and truncation scheme to handle heavy-tailed observations, while l1-regularization is adopted to overcome the curse of dimensionality under a sparse coefficient structure. To account for the time-varying coefficient, we estimate local high-dimensional coefficients which are biased estimators due to the l1-regularization. Thus, when estimating integrated coefficients, we propose a debiasing scheme to enjoy the law of large number property and employ a thresholding scheme to further accommodate the sparsity of the coefficients. We call this Robust thrEsholding Debiased LASSO (RED-LASSO) estimator. We show that the RED-LASSO estimator can achieve a near-optimal convergence rate with only finite a-th moment for any a>2. In the empirical study, we apply the RED-LASSO procedure to the high-dimensional integrated coefficient estimation using high-frequency trading data.
Anomaly Detection via Few-Shot Learning on Normality

One of the basic ideas for anomaly detection is to describe an enclosing boundary of normal data in order to identify cases outside as anomalies. In practice, however, normal data can consist of multiple classes, in which case the anomalies may appear not only outside such an enclosure but also in-between 'normal' classes. This paper addresses deep anomaly detection aimed at embedding 'normal' classes to individually close but mutually distant proximities. We introduce a problem setting where a limited number of labeled examples from each 'normal' class is available for training. Preparing such examples is much more feasible in practice than collecting examples of anomalies or labeling large-scale, normal data. We utilize the labeled examples in a margin-based loss reflecting the inter-class and the intra-class distances among the embedded labeled data. The two terms and their relations are derived from an information-theoretic principle. In an empirical study using image benchmark datasets, we show the advantage of the proposed method over existing deep anomaly detection models. We also show case studies using low-dimensional mappings to analyze the behavior of the proposed method.
Basket Booster for Prototype-based Contrastive Learning in Next Basket Recommendation

Next basket recommendation seeks to model the correlation of items and mine users’ interests hidden in basket sequences, and tries to infer a set of items that tend to be adopted in the next session with the mined information. However, the feedback provided by users often involves only a small fraction of millions to billions of items. Sparse data makes it hard for model to infer high-quality representations for basket sequences, which further leads to poor recommendation. Inspired by the recent success of representation learning in some fields, e.g., computer vision and clustering, we propose a basket booster for prototype-based contrastive learning (BPCL) in next basket recommendation. A correlative basket booster is designed to mine self-supervised signals just from raw data and make augmentation for baskets. To our best knowledge, this is the first work to promote learning of prototype representation through basket augmentation, which helps overcome the difficulties caused by data sparsity and leads to a better next basket recommendation performance. Extensive experiments on three public real-world datasets demonstrate that the proposed BPCL method achieves better performance than the existing state-of-the-art methods.
A U-Shaped Hierarchical Recommender by Multi-resolution Collaborative Signal Modeling

Items (users) in a recommender system inherently exhibit hierarchical structures with respect to interactions. Although explicit hierarchical structures are often missing in real-world recommendation scenarios, recent research shows that exploring implicit hierarchical structures for items (users) would largely benefit recommender systems. In this paper, we model user (item) implicit hierarchical structures to capture user-item relationships at various resolution scales resulting in better preferences customization. Specifically, we propose a U-shaped Graph Convolutional Network-based recommender system, namely UGCN, that adopts a hierarchical encoding-decoding process with a message-passing mechanism to construct user (item) implicit hierarchical structures and capture multi-resolution relationships simultaneously. To verify the effectiveness of the UGCN recommender, we conduct experiments on three public datasets. Results have confirmed that the UGCN recommender achieves overall prediction improvements over state-of-the-art models, simultaneously demonstrating a higher recommendation coverage ratio and better-personalized results.
Knowledge-Driven Interpretation of Convolutional Neural Networks

Since the widespread adoption of deep learning solutions in critical environments, the interpretation of artificial neural networks has become a significant issue. To this end, numerous approaches currently try to align human-level concepts with the activation patterns of artificial neurons. Nonetheless, they often understate two related aspects: the distributed nature of neural representations and the semantic relations between concepts. We explicitly tackled this interrelatedness by defining a novel semantic alignment framework to align distributed activation patterns and structured knowledge. In particular, we detailed a solution to assign to both neurons and their linear combinations one or more concepts from the WordNet semantic network. Acknowledging semantic links also enabled the clustering of neurons into semantically rich and meaningful neural circuits. Our empirical analysis of popular convolutional networks for image classification found evidence of the emergence of such neural circuits. Finally, we discovered neurons in neural circuits to be pivotal for the network to perform effectively on semantically related tasks. We also contribute by releasing the code that implements our alignment framework.
Graph Contrastive Learning with Adaptive Augmentation for Recommendation

Graph Convolutional Network (GCN) has been one of the most popular technologies in recommender systems, as it can effectively model high-order relationships. However, these methods usually suffer from two problems: sparse supervision signal and noisy interactions. To address these problems, graph contrastive learning is applied for GCN-based recommendation. The general framework of graph contrastive learning is first to perform data augmentation on the input graph to get two graph views and then maximize the agreement of representations in these views. Despite the effectiveness, existing methods ignore the differences in the impact of nodes and edges when performing data augmentation, which will degrade the quality of the learned representations. Meanwhile, they usually adopt manual data augmentation schemes, limiting the generalization of models. We argue that the data augmentation scheme should be learnable and adaptive to the inherent patterns in the graph structure. Thus, the model can learn representations that remain invariant to perturbations of unimportant structures while demanding fewer resources. In this work, we propose a novel Graph Contrastive learning framework with Adaptive data augmentation for Recommendation (GCARec). Specifically, for adaptive augmentation, we first calculate the retaining probability of each edge based on the attention mechanism and then sample edges according to the probability with a Gumbel Softmax. In addition, the adaptive data augmentation scheme is based on the neural network and requires no domain knowledge, making it learnable and generalizable. Extensive experiments on three real-world datasets show that GCARec outperforms state-of-the-art baselines.
Bi-directional Contrastive Distillation for Multi-behavior Recommendation

Multi-behavior recommendation leverages auxiliary behaviors (e.g., view, add-to-cart) to improve the prediction for target behaviors (e.g., buy). Most existing works are built upon the assumption that all the auxiliary behaviors are positively correlated with target behaviors. However, we empirically find that such an assumption may not hold in real-world datasets. In fact, some auxiliary feedback is too noisy to be helpful, and it is necessary to restrict its influence for better performance. To this end, in this paper we propose a Bi-directional Contrastive Distillation (BCD) model for multi-behavior recommendation, aiming to distill valuable knowledge (about user preference) from the interplay of multiple user behaviors. Specifically, we design a forward distillation to distill the knowledge from auxiliary behaviors to help model target behaviors, and then a backward distillation to distill the knowledge from target behaviors to enhance the modelling of auxiliary behaviors. Through this circular learning, we can better extract the common knowledge from multiple user behaviors, where noisy auxiliary behaviors will not be involved. The experimental results on two real-world datasets show that our approach outperforms other counterparts in accuracy.
InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Recently, self-supervised learning (e.g. contrastive learning) has been extended to cross-domain scenarios for reducing domain discrepancy in either instance-to-instance or instance-to-prototype manner. Although achieving remarkable progress, when the domain discrepancy is large, these methods would not perform well as a large shift leads to incorrect initial pseudo labels. To mitigate the performance degradation caused by large domain shifts, we propose to construct multiple intermediate prototypes for each class and perform cross-domain instance-to-prototype based contrastive learning with these constructed intermediate prototypes. Compared with direct cross-domain self-supervised learning, the intermediate prototypes could contain more accurate label information and achieve better performance. Besides, to learn discriminative features and perform domain-level distribution alignment, we perform intra-domain contrastive learning and domain adversarial training. Thus, the model could learn both discriminative and invariant features. Extensive experiments are conducted on three public benchmarks (ImageCLEF, Office-31, and Office-Home), and the results show that the proposed method outperforms baseline methods.
FastDEC: Clustering by Fast Dominance Estimation

k-Nearest Neighbors (k-NN) graph is essential for the various graph mining tasks. In this work, we study the density-based clustering on the k-NN graph and propose FastDEC, a clustering framework by fast dominance estimation. The nearest density higher (NDH) relation and dominance-component (DC), more specifically their integration with the k-NN graph, are formally defined and theoretically analyzed. FastDEC includes two extensions to satisfy different clustering scenarios: FastDEC $$_D$$ for partitioning data into clusters with arbitrary shapes, and FastDEC $$_K$$ for K-Way partition. Firstly, a set of DCs is detected as the results of FastDEC $$_D$$ by segmenting the given k-NN graph. Then, the K-Way partition is generated by selecting the top-K DCs in terms of the inter-dominance (ID) as the seeds, and assigning the remaining DCs to their nearest dominators. FastDEC can be viewed as a much faster, more robust, and k-NN based variant of the classical density-based clustering algorithm: Density Peak Clustering (DPC). DPC estimates the significance of data points from the density and geometric distance factors, while FastDEC innovatively uses the global rank of the dominator as an additional factor in the significance estimation. FastDEC naturally holds several critical characteristics: (1) excellent clustering performance; (2) easy to interpret and implement; (3) efficiency and robustness. Experiments on both the artificial and real datasets demonstrate that FastDEC outperforms the state-of-the-art density methods including DPC.
Neural Networks with Feature Attribution and Contrastive Explanations

Interpretability is becoming an expected and even essential characteristic in GDPR Europe. In the majority of existing work on natural language processing (NLP), interpretability has focused on the problem of explanatory responses to questions like “Why p?” (identifying the causal attributes that support the prediction of "p.)” This type of local explainability focuses on explaining a single prediction made by a model for a single input, by quantifying the contribution of each feature to the predicted output class. Most of these methods are based on post-hoc approaches. In this paper, we propose a technique to learn centroid vectors concurrently while building the black-box in order to support answers to “Why p?” and “Why p and not q?,” where “q” is another class that is contrastive to “p.” Across multiple datasets, our approach achieves better results than traditional post-hoc methods.
Application of statistical analysis methods to increase the accuracy of the standard linear location algorithm

The work is devoted to the study of the accuracy of the standard algorithm for linear location of acoustic emission (AE) sources. To solve this problem, a series of experimental studies was carried out, consisting in the generation of acoustic signals of various amplitudes using an electronic simulator installed on the surface of a steel sample with a size of 500x50x3 mm. In the process of processing the registered signals, the correlation dependences between the error of the standard location algorithm and the parameters of the recorded AE pulses were determined. AE data processing was carried out using digita l signal processing and statistical analysis methods, including the use of a multiple linear regression model and an assessment of the significance of the parameters used using the Fisher statistical test. As a result of the research, analytical dependences of the additive and multiplicative errors were obtained, which are used to improve the standard algorithm for the linear location of AE sources. Approbation of the improved algorithm led to an increase in the minimum value of the probability of detecting AE sources from 0.06 to 0.57, which corresponds to an increase in location accuracy by 9.5 times.
Research of tipover detection and avoidance algorithms for mobile robots

The development of modern mobile robots is often connected with advanced reliability, especially in undetermined conditions. One widespread reason for robotic mission failures is robot tip-over. In this paper, a method to avoid tip-over is proposed based on the existing tip-over detection methods in application to a mobile robot with two arms. The method has been validated in Matlab/CoppeliaSim co-simulation environment.
Fast and Accurate Importance Weighting for Correcting Sample Bias

Bias in datasets can be very detrimental for appropriate statistical estimation. In response to this problem, importance weighting methods have been developed to match any biased distribution to its corresponding target unbiased distribution. The seminal Kernel Mean Matching (KMM) method is, nowadays, still considered as state of the art in this research field. However, one of the main drawbacks of this method is the computational burden for large datasets. Building on previous works by Huang et al. (2007) and de Mathelin et al. (2021), we derive a novel importance weighting algorithm which scales to large datasets by using a neural network to predict the instance weights. We show, on multiple public datasets, under various sample biases, that our proposed approach drastically reduces the computational time on large dataset while maintaining similar sample bias correction performance compared to other importance weighting methods. The proposed approach appears to be the only one able to give relevant reweighting in a reasonable time for large dataset with up to two million data.
Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers

The analysis of emotions expressed in text has numerous applications. In contrast to categorical analysis, focused on classifying emotions according to a pre-defined set of common classes, dimensional approaches can offer a more nuanced way to distinguish between different emotions. Still, dimensional methods have been less studied in the literature. Considering a valence-arousal dimensional space, this work assesses the use of pre-trained Transformers to predict these two dimensions on a continuous scale, with input texts from multiple languages and domains. We specifically combined multiple annotated datasets from previous studies, corresponding to either emotional lexica or short text documents, and evaluated models of multiple sizes and trained under different settings. Our results show that model size can have a significant impact on the quality of predictions, and that by fine-tuning a large model we can confidently predict valence and arousal in multiple languages. We make available the code, models, and supporting data.