bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Reinforcement Learning for Multi-contact Motion Planning of Hexapod Robots
Authors: Fu Huiqiao , Tang Kaiqiang , Li Peng , Zhang Wenqi , Wang Xinpeng , Deng Guizhou , Wang Tao , Chen Chunlin
Published: August 2021

Legged locomotion in a complex environment requires careful planning of the footholds of legged robots. In this paper, a novel Deep Reinforcement Learning (DRL) method is proposed to implement multi-contact motion planning for hexapod robots moving on uneven plum-blossom piles. First, the motion of hexapod robots is formulated as a Markov Decision Process (MDP) with a speciﬁed reward function. Second, a transition feasibility model is proposed for hexapod robots, which describes the feasibility of the state transition under the condition of satisfying kinematics and dynamics, and in turn determines the rewards. Third, the footholds and Center-of-Mass (CoM) sequences are sampled from a diagonal Gaussian distribution and the sequences are optimized through learning the optimal policies using the designed DRL algorithm. Both of the simulation and experimental results on physical systems demonstrate the feasibility and efficiency of the proposed method. Videos are shown at https://videoviewpage.wixsite.com/mcrl.
On the Convergence of Stochastic Compositional Gradient Descent Ascent Method
Authors: Gao Hongchang , Wang Xiaoqian , Luo Lei , Shi Xinghua
Published: August 2021

The compositional minimax problem covers plenty of machine learning models such as the distributionally robust compositional optimization problem. However, it is yet another understudied problem to optimize the compositional minimax problem. In this paper, we develop a novel efficient stochastic compositional gradient descent ascent method for optimizing the compositional minimax problem. Moreover, we establish the theoretical convergence rate of our proposed method. To the best of our knowledge, this is the first work achieving such a convergence rate for the compositional minimax problem. Finally, we conduct extensive experiments to demonstrate the effectiveness of our proposed method.
Accomplice Manipulation of the Deferred Acceptance Algorithm
Authors: Hosseini Hadi , Umar Fatima , Vaish Rohit
Published: August 2021

The deferred acceptance algorithm is an elegant solution to the stable matching problem that guarantees optimality and truthfulness for one side of the market. Despite these desirable guarantees, it is susceptible to strategic misreporting of preferences by the agents on the other side. We study a novel model of strategic behavior under the deferred acceptance algorithm: manipulation through an accomplice. Here, an agent on the proposed-to side (say, a woman) partners with an agent on the proposing side---an accomplice---to manipulate on her behalf (possibly at the expense of worsening his match). We show that the optimal manipulation strategy for an accomplice comprises of promoting exactly one woman in his true list (i.e., an inconspicuous manipulation). This structural result immediately gives a polynomial-time algorithm for computing an optimal accomplice manipulation. We also study the conditions under which the manipulated matching is stable with respect to the true preferences. Our experimental results show that accomplice manipulation outperforms self manipulation both in terms of the frequency of occurrence as well as the quality of matched partners.
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
Authors: Ghosh Aritra , Lan Andrew
Published: August 2021

Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/item for each student given their responses to previous questions, effectively reducing test length. Existing CAT methods use item response theory (IRT) models to relate student ability to their responses to questions and static question selection algorithms designed to reduce the ability estimation error as quickly as possible; therefore, these algorithms cannot improve by learning from large-scale student response data. In this paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to directly learn a data-driven question selection algorithm from training data. BOBCAT is agnostic to the underlying student response model and is computationally efficient during the adaptive testing process. Through extensive experiments on five real-world student response datasets, we show that BOBCAT outperforms existing CAT methods (sometimes significantly) at reducing test length.
Method of Moments for Topic Models with Mixed Discrete and Continuous Features
Authors: Giesen Joachim , Kahlmeyer Paul , Laue Sören , Mitterreiter Matthias , Nussbaum Frank , Staudt Christoph , Zarrieß Sina
Published: August 2021

Topic models are characterized by a latent class variable that represents the different topics. Traditionally, their observable variables are modeled as discrete variables like, for instance, in the prototypical latent Dirichlet allocation (LDA) topic model. In LDA, words in text documents are encoded by discrete count vectors with respect to some dictionary. The classical approach for learning topic models optimizes a likelihood function that is non-concave due to the presence of the latent variable. Hence, this approach mostly boils down to using search heuristics like the EM algorithm for parameter estimation. Recently, it was shown that topic models can be learned with strong algorithmic and statistical guarantees through Pearson's method of moments. Here, we extend this line of work to topic models that feature discrete as well as continuous observable variables (features). Moving beyond discrete variables as in LDA allows for more sophisticated features and a natural extension of topic models to other modalities than text, like, for instance, images. We provide algorithmic and statistical guarantees for the method of moments applied to the extended topic model that we corroborate experimentally on synthetic data. We also demonstrate the applicability of our model on real-world document data with embedded images that we preprocess into continuous state-of-the-art feature vectors.
InverseNet: Augmenting Model Extraction Attacks with Training Data Inversion
Authors: Gong Xueluan , Chen Yanjiao , Yang Wenbin , Mei Guanghao , Wang Qian
Published: August 2021

Cloud service providers, including Google, Amazon, and Alibaba, have now launched machine-learning-as-a-service (MLaaS) platforms, allowing clients to access sophisticated cloud-based machine learning models via APIs. Unfortunately, however, the commercial value of these models makes them alluring targets for theft, and their strategic position as part of the IT infrastructure of many companies makes them an enticing springboard for conducting further adversarial attacks. In this paper, we put forth a novel and effective attack strategy, dubbed InverseNet, that steals the functionality of black-box cloud-based models with only a small number of queries. The crux of the innovation is that, unlike existing model extraction attacks that rely on public datasets or adversarial samples, InverseNet constructs inversed training samples to increase the similarity between the extracted substitute model and the victim model. Further, only a small number of data samples with high confidence scores (rather than an entire dataset) are used to reconstruct the inversed dataset, which substantially reduces the attack cost. Extensive experiments conducted on three simulated victim models and Alibaba Cloud's commercially-available API demonstrate that InverseNet yields a model with significantly greater functional similarity to the victim model than the current state-of-the-art attacks at a substantially lower query budget.
Hierarchical Class-Based Curriculum Loss
Authors: Goyal Palash , Choudhary Divya , Ghosh Shalini
Published: August 2021

Classification algorithms in machine learning often assume a flat label space. However, most real world data have dependencies between the labels, which can often be captured by using a hierarchy. Utilizing this relation can help develop a model capable of satisfying the dependencies and improving model accuracy and interpretability. Further, as different levels in the hierarchy correspond to different granularities, penalizing each label equally can be detrimental to model learning. In this paper, we propose a loss function, hierarchical curriculum loss, with two properties: (i) satisfy hierarchical constraints present in the label space, and (ii) provide non-uniform weights to labels based on their levels in the hierarchy, learned implicitly by the training paradigm. We theoretically show that the proposed hierarchical class-based curriculum loss is a tight bound of 0-1 loss among all losses satisfying the hierarchical constraints. We test our loss function on real world image data sets, and show that it significantly outperforms state-of-the-art baselines.
Fine-Grained Air Quality Inference via Multi-Channel Attention Model
Authors: Han Qilong , Lu Dan , Chen Rui
Published: August 2021

In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. We point out the importance of explicitly modeling both static and dynamic spatial correlations, and consequently propose a novel multi-channel attention model (MCAM) that models static and dynamic spatial correlations as separate channels. The static channel combines the beauty of attention mechanisms and graph-based spatial modeling via an adapted bilateral filtering technique, which considers not only locations' Euclidean distances but also their similarity of geo-context features. The dynamic channel learns stations' time-dependent spatial influence on a target location at each time step via long short-term memory (LSTM) networks and attention mechanisms. In addition, we introduce two novel ideas, atmospheric dispersion theories and the hysteretic nature of air pollutant dispersion, to better model the dynamic spatial correlation. We also devise a multi-channel graph convolutional fusion network to effectively fuse the graph outputs, along with other features, from both channels. Our extensive experiments on real-world benchmark datasets demonstrate that MCAM significantly outperforms the state-of-the-art solutions.
State-Based Recurrent SPMNs for Decision-Theoretic Planning under Partial Observability
Authors: Hayes Layton , Doshi Prashant , Pawar Swaraj , Tatavarti Hari Teja
Published: August 2021

The sum-product network (SPN) has been extended to model sequence data with the recurrent SPN (RSPN), and to decision-making problems with sum-product-max networks (SPMN). In this paper, we build on the concepts introduced by these extensions and present state-based recurrent SPMNs (S-RSPMNs) as a generalization of SPMNs to sequential decision-making problems where the state may not be perfectly observed. As with recurrent SPNs, S-RSPMNs utilize a repeatable template network to model sequences of arbitrary lengths. We present an algorithm for learning compact template structures by identifying unique belief states and the transitions between them through a state matching process that utilizes augmented data. In our knowledge, this is the first data-driven approach that learns graphical models for planning under partial observability, which can be solved efficiently. S-RSPMNs retain the linear solution complexity of SPMNs, and we demonstrate significant improvements in compactness of representation and the run time of structure learning and inference in sequential domains.
Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
Authors: He Yang , Yu Ning , Keuper Margret , Fritz Mario
Published: August 2021

The rapid advances in deep generative models over the past years have led to highly realistic media, known as deepfakes, that are commonly indistinguishable from real to human eyes. These advances make assessing the authenticity of visual data increasingly difficult and pose a misinformation threat to the trustworthiness of visual content in general. Although recent work has shown strong detection accuracy of such deepfakes, the success largely relies on identifying frequency artifacts in the generated images, which will not yield a sustainable detection approach as generative models continue evolving and closing the gap to real images. In order to overcome this issue, we propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. The re-synthesis procedure is flexible, allowing us to incorporate a series of visual tasks - we adopt super-resolution, denoising and colorization as the re-synthesis. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios involving multiple generators over CelebA-HQ, FFHQ, and LSUN datasets. Source code is available at https://github.com/SSAW14/BeyondtheSpectrum.
Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning
Authors: Huang Hua , Shang Fanhua , Liu Yuanyuan , Liu Hongying
Published: August 2021

Federated Learning (FL) has become an active and promising distributed machine learning paradigm. As a result of statistical heterogeneity, recent studies clearly show that the performance of popular FL methods (e.g., FedAvg) deteriorates dramatically due to the client drift caused by local updates. This paper proposes a novel Federated Learning algorithm (called IGFL), which leverages both Individual and Group behaviors to mimic distribution, thereby improving the ability to deal with heterogeneity. Unlike existing FL methods, our IGFL can be applied to both client and server optimization. As a by-product, we propose a new attention-based federated learning in the server optimization of IGFL. To the best of our knowledge, this is the first time to incorporate attention

mechanisms into federated optimization. We conduct extensive experiments and show that IGFL can significantly improve the performance of existing federated learning methods. Especially when the distributions of data among individuals are diverse, IGFL can improve the classification accuracy by about 13% compared with prior baselines.
On the Neural Tangent Kernel of Deep Networks with Orthogonal Initialization
Authors: Huang Wei , Du Weitao , Xu Richard Yi Da
Published: August 2021

The prevailing thinking is that orthogonal weights are crucial to enforcing dynamical isometry and speeding up training. The increase in learning speed that results from orthogonal initialization in linear networks has been well-proven. However, while the same is believed to also hold for nonlinear networks when the dynamical isometry condition is satisfied, the training dynamics behind this contention have not been thoroughly explored. In this work, we study the dynamics of ultra-wide networks across a range of architectures, including Fully Connected Networks (FCNs) and Convolutional Neural Networks (CNNs) with orthogonal initialization via neural tangent kernel (NTK). Through a series of propositions and lemmas, we prove that two NTKs, one corresponding to Gaussian weights and one to orthogonal weights, are equal when the network width is infinite. Further, during training, the NTK of an orthogonally-initialized infinite-width network should theoretically remain constant. This suggests that the orthogonal initialization cannot speed up training in the NTK (lazy training) regime, contrary to the prevailing thoughts. In order to explore under what circumstances can orthogonality accelerate training, we conduct a thorough empirical investigation outside the NTK regime. We find that when the hyper-parameters are set to achieve a linear regime in nonlinear activation, orthogonal initialization can improve the learning speed with a large learning rate or large depth.
Learning CNF Theories Using MDL and Predicate Invention
Authors: Jain Arcchit , Gautrais Clément , Kimmig Angelika , De Raedt Luc
Published: August 2021

We revisit the problem of learning logical theories from examples, one of the most quintessential problems in machine learning. More specifically, we develop an approach to learn CNF-formulae from satisfiability. This is a setting in which the examples correspond to partial interpretations and an example is classified as positive when it is logically consistent with the theory.

We present a novel algorithm, called Mistle -- Minimal SAT Theory Learner, for learning such theories. The distinguishing features are that 1) Mistle performs predicate invention and inverse resolution, 2) is based on the MDL principle to compress the data, and 3) combines this with frequent pattern mining to find the most interesting theories.

The experiments demonstrate that Mistle can learn CNF theories accurately and works well in tasks involving compression and classification.
Learning to Learn Personalized Neural Network for Ventricular Arrhythmias Detection on Intracardiac EGMs
Authors: Jia Zhenge , Wang Zhepeng , Hong Feng , PING Lichuan , Shi Yiyu , Hu Jingtong
Published: August 2021

Life-threatening ventricular arrhythmias (VAs) detection on intracardiac electrograms (IEGMs) is essential to Implantable Cardioverter Defibrillators (ICDs). However, current VAs detection methods count on a variety of heuristic detection criteria, and require frequent manual interventions to personalize criteria parameters for each patient to achieve accurate detection. In this work, we propose a one-dimensional convolutional neural network (1D-CNN) based life-threatening VAs detection on IEGMs. The network architecture is elaborately designed to satisfy the extreme resource constraints of the ICD while maintaining high detection accuracy. We further propose a meta-learning algorithm with a novel patient-wise training tasks formatting strategy to personalize the 1D-CNN. The algorithm generates a well-generalized model initialization containing across-patient knowledge, and performs a quick adaptation of the model to the specific patient's IEGMs. In this way, a new patient could be immediately assigned with personalized 1D-CNN model parameters using limited input data. Compared with the conventional VAs detection method, the proposed method achieves 2.2% increased sensitivity for detecting VAs rhythm and 8.6% increased specificity for non-VAs rhythm.
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Authors: Kim Taehyeon , Oh Jaehoon , Kim Nak Yil , Cho Sangwook , Yun Se-Young
Published: August 2021

Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures. Generally, the objective function of KD is the Kullback-Leibler (KL) divergence loss between the softened probability distributions of the teacher model and the student model with the temperature scaling hyperparameter τ. Despite its widespread use, few studies have discussed how such softening influences generalization. Here, we theoretically show that the KL divergence loss focuses on the logit matching when τ increases and the label matching when τ goes to 0 and empirically show that the logit matching is positively correlated to performance improvement in general. From this observation, we consider an intuitive KD loss function, the mean squared error (MSE) between the logit vectors, so that the student model can directly learn the logit of the teacher model. The MSE loss outperforms the KL divergence loss, explained by the penultimate layer representations difference between the two losses. Furthermore, we show that sequential distillation can improve performance and that KD, using the KL divergence loss with small τ particularly, mitigates the label noise. The code to reproduce the experiments is publicly available online at https://github.com/jhoon-oh/kd_data/.
Solving Continuous Control with Episodic Memory
Authors: Kuznetsov Igor , Filchenkov Andrey
Published: August 2021

Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance. Previous works on memory mechanisms show benefits of using episodic-based data structures for discrete action problems in terms of sample-efficiency. The application of episodic memory for continuous control with a large action space is not trivial. Our study aims to answer the question: can episodic memory be used to improve agent's performance in continuous control? Our proposed algorithm combines episodic memory with Actor-Critic architecture by modifying critic's objective. We further improve performance by introducing episodic-based replay buffer prioritization. We evaluate our algorithm on OpenAI gym domains and show greater sample-efficiency compared with the state-of-the art model-free off-policy algorithms.
Topological Uncertainty: Monitoring Trained Neural Networks through Persistence of Activation Graphs
Authors: Lacombe Théo , Ike Yuichi , Carrière Mathieu , Chazal Frédéric , Glisse Marc , Umeda Yuhei
Published: August 2021

Although neural networks are capable of reaching astonishing performance on a wide variety of contexts, properly training networks on complicated tasks requires expertise and can be expensive from a computational perspective. In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. Being able to monitor the presence of such variations without retraining the network is of crucial importance.

In this paper, we develop a method to monitor trained neural networks based on the topological properties of their activation graphs. To each new observation, we assign a Topological Uncertainty, a score that aims to assess the reliability of the predictions by investigating the whole network instead of its final layer only as typically done by practitioners. Our approach entirely works at a post-training level and does not require any assumption on the network architecture, optimization scheme, nor the use of data augmentation or auxiliary datasets; and can be faithfully applied on a large range of network architectures and data types. We showcase experimentally the potential of Topological Uncertainty in the context of trained network selection, Out-Of-Distribution detection, and shift-detection, both on synthetic and real datasets of images and graphs.
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Authors: Lee Hankook , Ahn Sungsoo , Seo Seung-Woo , Song You Young , Yang Eunho , Hwang Sung Ju , Shin Jinwoo
Published: August 2021

Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or generalize to unseen reaction templates (i.e., chemical reaction rules). In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. To this end, we design an efficient reactant selection framework, named RetCL (retrosynthesis via contrastive learning), for enumerating all of the candidate molecules based on selection scores computed by graph neural networks. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining. Extensive experiments demonstrate the benefits of the proposed selection-based approach. For example, when all 671k reactants in the USPTO database are given as candidates, our RetCL achieves top-1 exact match accuracy of 71.3% for the USPTO-50k benchmark, while a recent transformer-based approach achieves 59.6%. We also demonstrate that RetCL generalizes well to unseen templates in various settings in contrast to template-based approaches.
TextGTL: Graph-based Transductive Learning for Semi-supervised Text Classification via Structure-Sensitive Interpolation
Authors: Li Chen , Peng Xutan , Peng Hao , Li Jianxin , Wang Lihong
Published: August 2021

Compared with traditional sequential learning models, graph-based neural networks exhibit excellent properties when encoding text, such as the capacity of capturing global and local information simultaneously. Especially in the semi-supervised scenario, propagating information along the edge can effectively alleviate the sparsity of labeled data. In this paper, beyond the existing architecture of heterogeneous word-document graphs, for the first time, we investigate how to construct lightweight non-heterogeneous graphs based on different linguistic information to better serve free text representation learning. Then, a novel semi-supervised framework for text classification that refines graph topology under theoretical guidance and shares information across different text graphs, namely Text-oriented Graph-based Transductive Learning (TextGTL), is proposed. TextGTL also performs attribute space interpolation based on dense substructure in graphs to predict low-entropy labels with high-quality feature nodes for data augmentation. To verify the effectiveness of TextGTL, we conduct extensive experiments on various benchmark datasets, observing significant performance gains over conventional heterogeneous graphs. In addition, we also design ablation studies to dive deep into the validity of components in TextTGL.
Dynamic Proportional Rankings
Authors: Israel Jonas , Brill Markus
Published: August 2021

Proportional ranking rules aggregate approval-style preferences of agents into a collective ranking such that groups of agents with similar preferences are adequately represented. Motivated by the application of live Q&A platforms, where submitted questions need to be ranked based on the interests of the audience, we study a dynamic extension of the proportional rankings setting. In our setting, the goal is to maintain the proportionality of a ranking when alternatives (i.e., questions)---not necessarily from the top of the ranking---get selected sequentially. We propose generalizations of well-known aggregation rules to this setting and study their monotonicity and proportionality properties. We also evaluate the performance of these rules experimentally, using realistic probabilistic assumptions on the selection procedure.
Regularising Knowledge Transfer by Meta Functional Learning
Authors: Li Pan , Fu Yanwei , Gong Shaogang
Published: August 2021

Machine learning classifiers’ capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Moreover, we adopt an Iterative Update strategy on MFL (MFL-IU). This improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Experiments on three Few-Shot Learning (FSL) benchmarks (miniImageNet, CIFAR-FS and CUB) show that meta functional learning for regularisation knowledge transfer can benefit improving FSL classifiers.
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Authors: Li Pengyong , Wang Jun , Li Ziliang , Qiao Yixuan , Liu Xianggen , Ma Fei , Gao Peng , Song Sen , Xie Guotong
Published: August 2021

Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge for pre-training graph neural networks. In this paper, we propose a simple and effective self-supervised pre-training strategy, named Pairwise Half-graph Discrimination (PHD), that explicitly pre-trains a graph neural network at graph-level. PHD is designed as a simple binary classification task to discriminate whether two half-graphs come from the same source. Experiments demonstrate that the PHD is an effective pre-training strategy that offers comparable or superior performance on 13 graph classification tasks compared with state-of-the-art strategies, and achieves notable improvements when combined with node-level strategies. Moreover, the visualization of learned representation revealed that PHD strategy indeed empowers the model to learn graph-level knowledge like the molecular scaffold. These results have established PHD as a powerful and effective self-supervised learning strategy in graph-level representation learning.
Graph Filter-based Multi-view Attributed Graph Clustering
Authors: Lin Zhiping , Kang Zhao
Published: August 2021

Graph clustering has become an important research topic due to the proliferation of graph data. However, existing methods suffer from two major drawbacks. On the one hand, most methods can not simultaneously exploit attribute and graph structure information. On the other hand, most methods are incapable of handling multi-view data which contain sets of different features and graphs. In this paper, we propose a novel Multi-view Attributed Graph Clustering (MvAGC) method, which is simple yet effective. Firstly, a graph filter is applied to features to obtain a smooth representation without the need of learning the parameters of neural networks. Secondly, a novel strategy is designed to select a few anchor points, so as to reduce the computation complexity. Thirdly, a new regularizer is developed to explore high-order neighborhood information. Our extensive experiments indicate that our method works surprisingly well with respect to state-of-the-art deep neural network methods. The source code is available at https://github.com/sckangz/MvAGC.
Two-stage Training for Learning from Label Proportions
Authors: Liu Jiabin , Wang Bo , Shen Xin , Qi Zhiquan , Tian Yingjie
Published: August 2021

Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data. Existing deep learning based LLP methods utilize end-to-end pipelines to obtain the proportional loss with Kullback-Leibler divergence between the bag-level prior and posterior class distributions. However, the unconstrained optimization on this objective can hardly reach a solution in accordance with the given proportions. Besides, concerning the probabilistic classifier, this strategy unavoidably results in high-entropy conditional class distributions at the instance level. These issues further degrade the performance of the instance-level classification. In this paper, we regard these problems as noisy pseudo labeling, and instead impose the strict proportion consistency on the classifier with a constrained optimization as a continuous training stage for existing LLP classifiers. In addition, we introduce the mixup strategy and symmetric cross-entropy to further reduce the label noise. Our framework is model-agnostic, and demonstrates compelling performance improvement in extensive experiments, when incorporated into other deep LLP models as a post-hoc phase.
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Authors: Luo Ziwei , Hu Jing , Wang Xin , Lyu Siwei , Kong Bin , Yin Youbing , Song Qi , Wu Xi
Published: August 2021

Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces. In this paper, we draw inspiration from the recent success of the maximum entropy reinforcement learning framework designed for challenging continuous control problems to develop stochastic policies over high dimensional continuous spaces including image representation, generation, and control simultaneously. Central to this method is the Stochastic Actor-Executor-Critic (SAEC) which is an off-policy actor-critic model with an additional executor to generate realistic images. Specifically, the actor focuses on the high-level representation and control policy by a stochastic latent action, as well as explicitly directs the executor to generate low-level actions to manipulate the state. Experiments on several image-to-image translation tasks have demonstrated the effectiveness and robustness of the proposed SAEC when facing high-dimensional continuous space problems.
Hierarchical Temporal Multi-Instance Learning for Video-based Student Learning Engagement Assessment
Authors: Ma Jiayao , Jiang Xinbo , Xu Songhua , Qin Xueying
Published: August 2021

Video-based automatic assessment of a student's learning engagement on the fly can provide immense values for delivering personalized instructional services, a vehicle particularly important for massive online education. To train such an assessor, a major challenge lies in the collection of sufficient labels at the appropriate temporal granularity since a learner's engagement status may continuously change throughout a study session. Supplying labels at either frame or clip level incurs a high annotation cost. To overcome such a challenge, this paper proposes a novel hierarchical multiple instance learning (MIL) solution, which only requires labels anchored on full-length videos to learn to assess student engagement at an arbitrary temporal granularity and for an arbitrary duration in a study session. The hierarchical model mainly comprises a bottom module and a top module, respectively dedicated to learning the latent relationship between a clip and its constituent frames and that between a video and its constituent clips, with the constraints on the training stage that the average engagements of local clips is that of the video label. To verify the effectiveness of our method, we compare the performance of the proposed approach with that of several state-of-the-art peer solutions through extensive experiments.
Multi-Cause Effect Estimation with Disentangled Confounder Representation
Authors: Ma Jing , Guo Ruocheng , Zhang Aidong , Li Jundong
Published: August 2021

One fundamental problem in causality learning is to estimate the causal effects of one or multiple treatments (e.g., medicines in the prescription) on an important outcome (e.g., cure of a disease). One major challenge of causal effect estimation is the existence of unobserved confounders -- the unobserved variables that affect both the treatments and the outcome. Recent studies have shown that by modeling how instances are assigned with different treatments together, the patterns of unobserved confounders can be captured through their learned latent representations. However, the interpretability of the representations in these works is limited. In this paper, we focus on the multi-cause effect estimation problem from a new perspective by learning disentangled representations of confounders. The disentangled representations not only facilitate the treatment effect estimation but also strengthen the understanding of causality learning process. Experimental results on both synthetic and real-world datasets show the superiority of our proposed framework from different aspects.
What Changed? Interpretable Model Comparison
Authors: Nair Rahul , Mattetti Massimiliano , Daly Elizabeth , Wei Dennis , Alkan Oznur , Zhang Yunfeng
Published: August 2021

We consider the problem of distinguishing two machine learning (ML) models built for the same task in a human-interpretable way. As models can fail or succeed in different ways, classical accuracy metrics may mask crucial qualitative differences. This problem arises in a few contexts. In business applications with periodically retrained models, an updated model may deviate from its predecessor for some segments without a change in overall accuracy. In automated ML systems, where several ML pipelines are generated, the top pipelines have comparable accuracy but may have more subtle differences. We present a method for interpretable comparison of binary classification models by approximating them with Boolean decision rules. We introduce stabilization conditions that allow for the two rule sets to be more directly comparable. A method is proposed to compare two rule sets based on their statistical and semantic similarity by solving assignment problems and highlighting changes. An empirical evaluation on several benchmark datasets illustrates the insights that may be obtained and shows that artificially induced changes can be reliably recovered by our method.
PROPm Allocations of Indivisible Goods to Multiple Agents
Authors: Baklanov Artem , Garimidi Pranav , Gkatzelis Vasilis , Schoepflin Daniel
Published: August 2021

We study the classic problem of fairly allocating a set of indivisible goods among a group of agents, and focus on the notion of approximate proportionality known as PROPm. Prior work showed that there exists an allocation that satisfies this notion of fairness for instances involving up to five agents, but fell short of proving that this is true in general. We extend this result to show that a PROPm allocation is guaranteed to exist for all instances, independent of the number of agents or goods. Our proof is constructive, providing an algorithm that computes such an allocation and, unlike prior work, the running time of this algorithm is polynomial in both the number of agents and the number of goods.
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Authors: Qian Cheng , Kargas Nikos , Xiao Cao , Glass Lucas , Sidiropoulos Nicholas , Sun Jimeng
Published: August 2021

Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple delayed updates of recent temporal slices for some locations or diseases. Recovering such missing or noisy (under-reported) elements of the input tensor can be viewed as a generalized tensor completion problem. Existing tensor completion methods usually assume that i) missing elements are randomly distributed and ii) noise for each tensor element is i.i.d. zero-mean. Both assumptions can be violated for spatio-temporal tensor data. We often observe multiple versions of the input tensor with different under-reporting noise levels. The amount of noise can be time- or location-dependent as more updates are progressively introduced to the tensor. We model such dynamic data as a multi-version tensor with an extra tensor mode capturing the data updates. We propose a low-rank tensor model to predict the updates over time. We demonstrate that our method can accurately predict the ground-truth values of many real-world tensors. We obtain up to 27.2% lower root mean-squared-error compared to the best baseline method. Finally, we extend our method to track the tensor data over time, leading to significant computational savings.
Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation
Authors: Qiu Zhen , Zhang Yifan , Lin Hongbin , Niu Shuaicheng , Liu Yanxia , Du Qing , Tan Mingkui
Published: August 2021

We study a practical domain adaptation task, called source-free unsupervised domain adaptation (UDA) problem, in which we cannot access source domain data due to data privacy issues but only a pre-trained source model and unlabeled target data are available. This task, however, is very difficult due to one key challenge: the lack of source data and target domain labels makes model adaptation very challenging. To address this, we propose to mine the hidden knowledge in the source model and exploit it to generate source avatar prototypes (i.e. representative features for each source class) as well as target pseudo labels for domain alignment. To this end, we propose a Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically, CPGA consists of two stages: (1) prototype generation: by exploring the classification boundary information of the source model, we train a prototype generator to generate avatar prototypes via contrastive learning. (2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes. Extensive experiments on three UDA benchmark datasets demonstrate the effectiveness and superiority of the proposed method.
Exact Acceleration of K-Means++ and K-Means||
Authors: Raff Edward
Published: August 2021

K-Means++ and its distributed variant K-Means|| have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation,and theoretical grounding of the K-means++ and || methods have made them difficult to "best" from a holistic perspective. We focus on using triangle inequality based pruning methods to accelerate both of these algorithms to yield comparable or better run-time without sacrificing any of the benefits of these approaches. For both algorithms we are able to reduce distance computations by over 500×. For K-means++ this results in up to a 17×speedup in run-time and a551×speedup for K-means||. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms.
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
Authors: Shen Dazhong , Qin Chuan , Wang Chao , Zhu Hengshu , Chen Enhong , Xiong Hui
Published: August 2021

As one of the most popular generative models, Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. However, when the decoder network is sufficiently expressive, VAE may lead to posterior collapse; that is, uninformative latent representations may be learned. To this end, in this paper, we propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space, and thus the representation can be learned in a meaningful and compact manner. Specifically, we first theoretically demonstrate that it will result in better latent space with high diversity and low uncertainty awareness by controlling the distribution of posterior’s parameters across the whole data accordingly. Then, without the introduction of new loss terms or modifying training strategies, we propose to exploit Dropout on the variances and Batch-Normalization on the means simultaneously to regularize their distributions implicitly. Furthermore, to evaluate the generalization effect, we also exploit DU-VAE for inverse autoregressive flow based-VAE (VAE-IAF) empirically. Finally, extensive experiments on three benchmark datasets clearly show that our approach can outperform state-of-the-art baselines on both likelihood estimation and underlying classification tasks.
Positive-Unlabeled Learning from Imbalanced Data
Authors: Su Guangxin , Chen Weitong , Xu Miao
Published: August 2021

Positive-unlabeled (PU) learning deals with the binary classification problem when only positive (P) and unlabeled (U) data are available, without negative (N) data. Existing PU methods perform well on the balanced dataset. However, in real applications such as financial fraud detection or medical diagnosis, data are always imbalanced. It remains unclear whether existing PU methods can perform well on imbalanced data. In this paper, we explore this problem and propose a general learning objective for PU learning targeting specially at imbalanced data. By this general learning objective, state-of-the-art PU methods based on optimizing a consistent risk can be adapted to conquer the imbalance. We theoretically show that in expectation, optimizing our learning objective is equivalent to learning a classifier on the oversampled balanced data with both P and N data available, and further provide an estimation error bound. Finally, experimental results validate the effectiveness of our proposal compared to state-of-the-art PU methods.
Predicting Traffic Congestion Evolution: A Deep Meta Learning Approach
Authors: Sun Yidan , Jiang Guiyuan , Lam Siew Kei , He Peilan
Published: August 2021

Many efforts are devoted to predicting congestion evolution using propagation patterns that are mined from historical traffic data.

However, the prediction quality is limited to the intrinsic properties that are present in the mined patterns. In addition, these mined patterns frequently fail to sufficiently capture many realistic characteristics of true congestion evolution (e.g., asymmetric transitivity, local proximity). In this paper, we propose a representation learning framework to characterize and predict congestion evolution between any pair of road segments (connected via single or multiple paths). Specifically, we build dynamic attributed networks (DAN) to incorporate both dynamic and static impact factors while preserving dynamic topological structures. We propose a Deep Meta Learning Model (DMLM) for learning representations of road segments which support accurate prediction of congestion evolution. DMLM relies on matrix factorization techniques and meta-LSTM modules to exploit temporal correlations at multiple scales, and employ meta-Attention modules to merge heterogeneous features while learning the time-varying impacts of both dynamic and static features. Compared to all state-of-the-art methods, our framework achieves significantly better prediction performance on two congestion evolution behaviors (propagation and decay) when evaluated using real-world dataset.
Hyperspectral Band Selection via Spatial-Spectral Weighted Region-wise Multiple Graph Fusion-Based Spectral Clustering
Authors: Tang Chang , Liu Xinwang , Zhu En , Wang Lizhe , Zomaya Albert
Published: August 2021

In this paper, we propose a hyperspectral band selection method via spatial-spectral weighted region-wise multiple graph fusion-based spectral clustering, referred to as RMGF briefly. Considering that different objects have different reflection characteristics, we use a superpixel segmentation algorithm to segment the first principal component of original hyperspectral image cube into homogeneous regions. For each superpixel, we construct a corresponding similarity graph to reflect the similarity between band pairs. Then, a multiple graph diffusion strategy with theoretical convergence guarantee is designed to learn a unified graph for partitioning the whole hyperspectral cube into several subcubes via spectral clustering. During the graph diffusion process, the spatial and spectral information of each superpixel are embedded to make spatial/spectral similar superpixels contribute more to each other. Finally, the band containing minimum noise in each subcube is selected to represent the whole subcube. Extensive experiments are conducted on three public datasets to validate the superiority of the proposed method when compared with other state-of-the-art ones.
Fairness in Long-Term Participatory Budgeting
Authors: Lackner Martin , Maly Jan , Rey Simon
Published: August 2021

Participatory Budgeting (PB) processes are usually designed to span several years, with referenda for new budget allocations taking place regularly. This paper presents a first formal framework for long-term PB, based on a sequence of budgeting problems as main input. We introduce a theory of fairness for this setting, focusing on three main concepts that apply to types (groups) of voters:

(i) achieving equal welfare for all types, (ii) minimizing inequality of welfare (as measured by the Gini coefficient), and (iii) achieving equal welfare in the long run.

We investigate under which conditions these criteria can be satisfied, and analyze the computational complexity of verifying whether they hold.