paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v35i9.16977 | Balanced Open Set Domain Adaptation via Centroid Alignment | https://ojs.aaai.org/index.php/AAAI/article/view/16977 | https://ojs.aaai.org/index.php/AAAI/article/download/16977/16784 | [
"Mengmeng Jing",
"Jingjing Li",
"Lei Zhu",
"Zhengming Ding",
"Ke Lu",
"Yang Yang"
] | Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain. Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making them unbalanced OSDA me... | main | Machine Learning | 10.1609/aaai.v35i9.16977 | 35 | 9 | 8013-8020 | official | null | null |
10.1609/aaai.v35i9.16978 | Linearly Replaceable Filters for Deep Network Channel Pruning | https://ojs.aaai.org/index.php/AAAI/article/view/16978 | https://ojs.aaai.org/index.php/AAAI/article/download/16978/16785 | [
"Donggyu Joo",
"Eojindl Yi",
"Sunghyun Baek",
"Junmo Kim"
] | Convolutional neural networks (CNNs) have achieved remarkable results; however, despite the development of deep learning, practical user applications are fairly limited because heavy networks can be used solely with the latest hardware and software supports. Therefore, network pruning is gaining attention for general a... | main | Machine Learning | 10.1609/aaai.v35i9.16978 | 35 | 9 | 8021-8029 | official | null | null |
10.1609/aaai.v35i9.16979 | A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/16979 | https://ojs.aaai.org/index.php/AAAI/article/download/16979/16786 | [
"Krishna C. Kalagarla",
"Rahul Jain",
"Pierluigi Nuzzo"
] | Constrained Markov decision processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various cost functions. In this paper, we consider the setting of episodic fixed-horizon CMDPs. We propose an online algorithm which leverages the li... | main | Machine Learning | 10.1609/aaai.v35i9.16979 | 35 | 9 | 8030-8037 | official | 2009.11348 | title_snapshot |
10.1609/aaai.v35i9.16980 | Winning Lottery Tickets in Deep Generative Models | https://ojs.aaai.org/index.php/AAAI/article/view/16980 | https://ojs.aaai.org/index.php/AAAI/article/download/16980/16787 | [
"Neha Mukund Kalibhat",
"Yogesh Balaji",
"Soheil Feizi"
] | The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance to that of the original network. Prior works in lottery tickets have primarily focused on the supervised learning setup, with several papers ... | main | Machine Learning | 10.1609/aaai.v35i9.16980 | 35 | 9 | 8038-8046 | official | 2010.02350 | title_snapshot |
10.1609/aaai.v35i9.16981 | Exploration via State influence Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/16981 | https://ojs.aaai.org/index.php/AAAI/article/download/16981/16788 | [
"Yongxin Kang",
"Enmin Zhao",
"Kai Li",
"Junliang Xing"
] | This paper studies the challenging problem of reinforcement learning (RL) in hard exploration tasks with sparse rewards. It focuses on the exploration stage before the agent gets the first positive reward, in which case, traditional RL algorithms with simple exploration strategies often work poorly. Unlike previous met... | main | Machine Learning | 10.1609/aaai.v35i9.16981 | 35 | 9 | 8047-8054 | official | null | null |
10.1609/aaai.v35i9.16982 | Deep Probabilistic Canonical Correlation Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/16982 | https://ojs.aaai.org/index.php/AAAI/article/download/16982/16789 | [
"Mahdi Karami",
"Dale Schuurmans"
] | We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shar... | main | Machine Learning | 10.1609/aaai.v35i9.16982 | 35 | 9 | 8055-8063 | official | null | null |
10.1609/aaai.v35i9.16943 | Topology Distance: A Topology-Based Approach for Evaluating Generative Adversarial Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16943 | https://ojs.aaai.org/index.php/AAAI/article/download/16943/16750 | [
"Danijela Horak",
"Simiao Yu",
"Gholamreza Salimi-Khorshidi"
] | Automatic evaluation of the goodness of Generative Adversarial Networks (GANs) has been a challenge for the field of machine learning. In this work, we propose a distance complementary to existing measures: Topology Distance (TD), the main idea behind which is to compare the geometric and topological features of the la... | main | Machine Learning | 10.1609/aaai.v35i9.16943 | 35 | 9 | 7721-7728 | official | 2002.12054 | title_snapshot |
10.1609/aaai.v35i9.16944 | Storage Fit Learning with Feature Evolvable Streams | https://ojs.aaai.org/index.php/AAAI/article/view/16944 | https://ojs.aaai.org/index.php/AAAI/article/download/16944/16751 | [
"Bo-Jian Hou",
"Yu-Hu Yan",
"Peng Zhao",
"Zhi-Hua Zhou"
] | Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no l... | main | Machine Learning | 10.1609/aaai.v35i9.16944 | 35 | 9 | 7729-7736 | official | 2007.11280 | title_snapshot |
10.1609/aaai.v35i9.16945 | Reinforcement Learning Based Multi-Agent Resilient Control: From Deep Neural Networks to an Adaptive Law | https://ojs.aaai.org/index.php/AAAI/article/view/16945 | https://ojs.aaai.org/index.php/AAAI/article/download/16945/16752 | [
"Jian Hou",
"Fangyuan Wang",
"Lili Wang",
"Zhiyong Chen"
] | Recent advances in Multi-agent Reinforcement Learning (MARL) have made it possible to implement various tasks in cooperative as well as competitive scenarios through trial and error, and deep neural networks. These successes motivate us to bring the mechanism of MARL into the Multi-agent Resilient Consensus (MARC) prob... | main | Machine Learning | 10.1609/aaai.v35i9.16945 | 35 | 9 | 7737-7745 | official | null | null |
10.1609/aaai.v35i9.16946 | Slimmable Generative Adversarial Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16946 | https://ojs.aaai.org/index.php/AAAI/article/download/16946/16753 | [
"Liang Hou",
"Zehuan Yuan",
"Lei Huang",
"Huawei Shen",
"Xueqi Cheng",
"Changhu Wang"
] | Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models make them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying c... | main | Machine Learning | 10.1609/aaai.v35i9.16946 | 35 | 9 | 7746-7753 | official | 2012.05660 | title_snapshot |
10.1609/aaai.v35i9.16947 | Disentangled Representation Learning in Heterogeneous Information Network for Large-scale Android Malware Detection in the COVID-19 Era and Beyond | https://ojs.aaai.org/index.php/AAAI/article/view/16947 | https://ojs.aaai.org/index.php/AAAI/article/download/16947/16754 | [
"Shifu Hou",
"Yujie Fan",
"Mingxuan Ju",
"Yanfang Ye",
"Wenqiang Wan",
"Kui Wang",
"Yinming Mei",
"Qi Xiong",
"Fudong Shao"
] | In the fight against the COVID-19 pandemic, many social activities have moved online; society's overwhelming reliance on the complex cyberspace makes its security more important than ever. In this paper, we propose and develop an intelligent system named Dr.HIN to protect users against the evolving Android malware atta... | main | Machine Learning | 10.1609/aaai.v35i9.16947 | 35 | 9 | 7754-7761 | official | null | null |
10.1609/aaai.v35i9.16948 | Gaussian Process Priors for View-Aware Inference | https://ojs.aaai.org/index.php/AAAI/article/view/16948 | https://ojs.aaai.org/index.php/AAAI/article/download/16948/16755 | [
"Yuxin Hou",
"Ari Heljakka",
"Arno Solin"
] | While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention. Even though probabilistic machine learning provides the ability to encode correlation as prior kn... | main | Machine Learning | 10.1609/aaai.v35i9.16948 | 35 | 9 | 7762-7770 | official | 1912.03249 | title_snapshot |
10.1609/aaai.v35i9.16949 | Boosting Multi-task Learning Through Combination of Task Labels - with Applications in ECG Phenotyping | https://ojs.aaai.org/index.php/AAAI/article/view/16949 | https://ojs.aaai.org/index.php/AAAI/article/download/16949/16756 | [
"Ming-En Hsieh",
"Vincent Tseng"
] | Multi-task learning has increased in importance due to its superior performance by learning multiple different tasks simultaneously and its ability to perform several different tasks using a single model. In medical phenotyping, task labels are costly to acquire and might contain a certain degree of label noise. This d... | main | Machine Learning | 10.1609/aaai.v35i9.16949 | 35 | 9 | 7771-7779 | official | null | null |
10.1609/aaai.v35i9.16950 | OPQ: Compressing Deep Neural Networks with One-shot Pruning-Quantization | https://ojs.aaai.org/index.php/AAAI/article/view/16950 | https://ojs.aaai.org/index.php/AAAI/article/download/16950/16757 | [
"Peng Hu",
"Xi Peng",
"Hongyuan Zhu",
"Mohamed M. Sabry Aly",
"Jie Lin"
] | As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy these large DNN models on resource-constrained hardware platforms, e.g., smartphones. Numerous network compression methods such as pruning and quantization are proposed to reduce the model si... | main | Machine Learning | 10.1609/aaai.v35i9.16950 | 35 | 9 | 7780-7788 | official | 2205.11141 | title_snapshot |
10.1609/aaai.v35i9.16951 | Multi-scale Graph Fusion for Co-saliency Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16951 | https://ojs.aaai.org/index.php/AAAI/article/download/16951/16758 | [
"Rongyao Hu",
"Zhenyun Deng",
"Xiaofeng Zhu"
] | The key challenge of co-saliency detection is to extract discriminative features to distinguish the common salient foregrounds from backgrounds in a group of relevant images. In this paper, we propose a new co-saliency detection framework which includes two strategies to improve the discriminative ability of the featur... | main | Machine Learning | 10.1609/aaai.v35i9.16951 | 35 | 9 | 7789-7796 | official | null | null |
10.1609/aaai.v35i9.16952 | Continual Learning by Using Information of Each Class Holistically | https://ojs.aaai.org/index.php/AAAI/article/view/16952 | https://ojs.aaai.org/index.php/AAAI/article/download/16952/16759 | [
"Wenpeng Hu",
"Qi Qin",
"Mengyu Wang",
"Jinwen Ma",
"Bing Liu"
] | Continual learning (CL) incrementally learns a sequence of tasks while solving the catastrophic forgetting (CF) problem. Existing methods mainly try to deal with CF directly. In this paper, we propose to avoid CF by considering the features of each class holistically rather than only the discriminative information for ... | main | Machine Learning | 10.1609/aaai.v35i9.16952 | 35 | 9 | 7797-7805 | official | null | null |
10.1609/aaai.v35i9.16953 | Predictive Adversarial Learning from Positive and Unlabeled Data | https://ojs.aaai.org/index.php/AAAI/article/view/16953 | https://ojs.aaai.org/index.php/AAAI/article/download/16953/16760 | [
"Wenpeng Hu",
"Ran Le",
"Bing Liu",
"Feng Ji",
"Jinwen Ma",
"Dongyan Zhao",
"Rui Yan"
] | This paper studies learning from positive and unlabeled examples, known as PU learning. It proposes a novel PU learning method called Predictive Adversarial Networks (PAN) based on GAN (Generative Adversarial Networks). GAN learns a generator to generate data (e.g., images) to fool a discriminator which tries to determ... | main | Machine Learning | 10.1609/aaai.v35i9.16953 | 35 | 9 | 7806-7814 | official | null | null |
10.1609/aaai.v35i9.16954 | Multidimensional Uncertainty-Aware Evidential Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16954 | https://ojs.aaai.org/index.php/AAAI/article/download/16954/16761 | [
"Yibo Hu",
"Yuzhe Ou",
"Xujiang Zhao",
"Jin-Hee Cho",
"Feng Chen"
] | Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may e... | main | Machine Learning | 10.1609/aaai.v35i9.16954 | 35 | 9 | 7815-7822 | official | 2012.13676 | title_snapshot |
10.1609/aaai.v35i9.16955 | Adversarial Defence by Diversified Simultaneous Training of Deep Ensembles | https://ojs.aaai.org/index.php/AAAI/article/view/16955 | https://ojs.aaai.org/index.php/AAAI/article/download/16955/16762 | [
"Bo Huang",
"Zhiwei Ke",
"Yi Wang",
"Wei Wang",
"Linlin Shen",
"Feng Liu"
] | Learning-based classifiers are susceptible to adversarial examples. Existing defence methods are mostly devised on individual classifiers. Recent studies showed that it is viable to increase adversarial robustness by promoting diversity over an ensemble of models. In this paper, we propose adversarial defence by encour... | main | Machine Learning | 10.1609/aaai.v35i9.16955 | 35 | 9 | 7823-7831 | official | null | null |
10.1609/aaai.v35i9.16956 | Accelerating Continuous Normalizing Flow with Trajectory Polynomial Regularization | https://ojs.aaai.org/index.php/AAAI/article/view/16956 | https://ojs.aaai.org/index.php/AAAI/article/download/16956/16763 | [
"Han-Hsien Huang",
"Mi-Yen Yeh"
] | In this paper, we propose an approach to effectively accelerating the computation of continuous normalizing flow (CNF), which has been proven to be a powerful tool for the tasks such as variational inference and density estimation. The training time cost of CNF can be extremely high because the required number of funct... | main | Machine Learning | 10.1609/aaai.v35i9.16956 | 35 | 9 | 7832-7839 | official | 2012.04228 | title_snapshot |
10.1609/aaai.v35i9.16957 | Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition | https://ojs.aaai.org/index.php/AAAI/article/view/16957 | https://ojs.aaai.org/index.php/AAAI/article/download/16957/16764 | [
"Siteng Huang",
"Min Zhang",
"Yachen Kang",
"Donglin Wang"
] | The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recent approaches have introduced auxiliary semantic modalities into effective metric-learning frameworks that aim to learn a feature similarity ... | main | Machine Learning | 10.1609/aaai.v35i9.16957 | 35 | 9 | 7840-7847 | official | 2009.04724 | title_snapshot |
10.1609/aaai.v35i9.16958 | Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16958 | https://ojs.aaai.org/index.php/AAAI/article/download/16958/16765 | [
"Wenzhen Huang",
"Qiyue Yin",
"Junge Zhang",
"Kaiqi Huang"
] | Model-based reinforcement learning (RL) is more sample efficient than model-free RL by using imaginary trajectories generated by the learned dynamics model. When the model is inaccurate or biased, imaginary trajectories may be deleterious for training the action-value and policy functions. To alleviate such problem, th... | main | Machine Learning | 10.1609/aaai.v35i9.16958 | 35 | 9 | 7848-7856 | official | 2104.04174 | title_snapshot |
10.1609/aaai.v35i9.16959 | ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/16959 | https://ojs.aaai.org/index.php/AAAI/article/download/16959/16766 | [
"Xunpeng Huang",
"Runxin Xu",
"Hao Zhou",
"Zhe Wang",
"Zhengyang Liu",
"Lei Li"
] | Stochastic gradient descent (SGD) is a widely used method for its outstanding generalization ability and simplicity. Adaptive gradient methods have been proposed to further accelerate the optimization process. In this paper, we revisit existing adaptive gradient optimization methods with a new interpretation. Such new ... | main | Machine Learning | 10.1609/aaai.v35i9.16959 | 35 | 9 | 7857-7864 | official | 2006.07065 | title_snapshot |
10.1609/aaai.v35i9.16960 | Personalized Cross-Silo Federated Learning on Non-IID Data | https://ojs.aaai.org/index.php/AAAI/article/view/16960 | https://ojs.aaai.org/index.php/AAAI/article/download/16960/16767 | [
"Yutao Huang",
"Lingyang Chu",
"Zirui Zhou",
"Lanjun Wang",
"Jiangchuan Liu",
"Jian Pei",
"Yong Zhang"
] | Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate more. We establish t... | main | Machine Learning | 10.1609/aaai.v35i9.16960 | 35 | 9 | 7865-7873 | official | 2007.03797 | title_snapshot |
10.1609/aaai.v35i9.16961 | Reward-Biased Maximum Likelihood Estimation for Linear Stochastic Bandits | https://ojs.aaai.org/index.php/AAAI/article/view/16961 | https://ojs.aaai.org/index.php/AAAI/article/download/16961/16768 | [
"Yu-Heng Hung",
"Ping-Chun Hsieh",
"Xi Liu",
"P. R. Kumar"
] | Modifying the reward-biased maximum likelihood method originally proposed in the adaptive control literature, we propose novel learning algorithms to handle the explore-exploit trade-off in linear bandits problems as well as generalized linear bandits problems. We develop novel index policies that we prove achieve orde... | main | Machine Learning | 10.1609/aaai.v35i9.16961 | 35 | 9 | 7874-7882 | official | 2010.04091 | title_snapshot |
10.1609/aaai.v35i9.16962 | Large Batch Optimization for Deep Learning Using New Complete Layer-Wise Adaptive Rate Scaling | https://ojs.aaai.org/index.php/AAAI/article/view/16962 | https://ojs.aaai.org/index.php/AAAI/article/download/16962/16769 | [
"Zhouyuan Huo",
"Bin Gu",
"Heng Huang"
] | Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications. Warmup is one of nontrivial techniques to stabilize the convergence of large batch training. However, warmup is an empirical method and it is still unknown whether there is a better algorithm wi... | main | Machine Learning | 10.1609/aaai.v35i9.16962 | 35 | 9 | 7883-7890 | official | null | null |
10.1609/aaai.v35i9.16923 | Increasing Iterate Averaging for Solving Saddle-Point Problems | https://ojs.aaai.org/index.php/AAAI/article/view/16923 | https://ojs.aaai.org/index.php/AAAI/article/download/16923/16730 | [
"Yuan Gao",
"Christian Kroer",
"Donald Goldfarb"
] | Many problems in machine learning and game theory can be formulated as saddle-point problems, for which various first-order methods have been developed and proven efficient in practice. Under the general convex-concave assumption, most first-order methods only guarantee an ergodic convergence rate, that is, the uniform... | main | Machine Learning | 10.1609/aaai.v35i9.16923 | 35 | 9 | 7537-7544 | official | 1903.10646 | title_snapshot |
10.1609/aaai.v35i9.16924 | Uncertainty-Aware Multi-View Representation Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16924 | https://ojs.aaai.org/index.php/AAAI/article/download/16924/16731 | [
"Yu Geng",
"Zongbo Han",
"Changqing Zhang",
"Qinghua Hu"
] | Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dimensional features tend to contain noise, and furthermore, quality of data usually varies for different samples (even for different views), i... | main | Machine Learning | 10.1609/aaai.v35i9.16924 | 35 | 9 | 7545-7553 | official | 2201.05776 | title_snapshot |
10.1609/aaai.v35i9.16925 | Justicia: A Stochastic SAT Approach to Formally Verify Fairness | https://ojs.aaai.org/index.php/AAAI/article/view/16925 | https://ojs.aaai.org/index.php/AAAI/article/download/16925/16732 | [
"Bishwamittra Ghosh",
"Debabrota Basu",
"Kuldeep S. Meel"
] | As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally... | main | Machine Learning | 10.1609/aaai.v35i9.16925 | 35 | 9 | 7554-7563 | official | 2009.06516 | title_snapshot |
10.1609/aaai.v35i9.16926 | The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/16926 | https://ojs.aaai.org/index.php/AAAI/article/download/16926/16733 | [
"Naman Goel",
"Alfonso Amayuelas",
"Amit Deshpande",
"Amit Sharma"
] | Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This missingness, if ignored, nullifies any fairness guarantee of th... | main | Machine Learning | 10.1609/aaai.v35i9.16926 | 35 | 9 | 7564-7573 | official | 2012.11448 | title_snapshot |
10.1609/aaai.v35i9.16927 | Attribute-Guided Adversarial Training for Robustness to Natural Perturbations | https://ojs.aaai.org/index.php/AAAI/article/view/16927 | https://ojs.aaai.org/index.php/AAAI/article/download/16927/16734 | [
"Tejas Gokhale",
"Rushil Anirudh",
"Bhavya Kailkhura",
"Jayaraman J. Thiagarajan",
"Chitta Baral",
"Yezhou Yang"
] | While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknow... | main | Machine Learning | 10.1609/aaai.v35i9.16927 | 35 | 9 | 7574-7582 | official | 2012.01806 | title_snapshot |
10.1609/aaai.v35i9.16928 | Efficient On-Chip Learning for Optical Neural Networks Through Power-Aware Sparse Zeroth-Order Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/16928 | https://ojs.aaai.org/index.php/AAAI/article/download/16928/16735 | [
"Jiaqi Gu",
"Chenghao Feng",
"Zheng Zhao",
"Zhoufeng Ying",
"Ray T. Chen",
"David Z. Pan"
] | Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption. However, current learning protocols fail to provide scalable and efficient solutions to photonic circuit optimization in practical app... | main | Machine Learning | 10.1609/aaai.v35i9.16928 | 35 | 9 | 7583-7591 | official | 2012.11148 | title_snapshot |
10.1609/aaai.v35i9.16929 | Attentive Neural Point Processes for Event Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/16929 | https://ojs.aaai.org/index.php/AAAI/article/download/16929/16736 | [
"Yulong Gu"
] | Event sequence, where each event is associated with a marker and a timestamp, is increasingly ubiquitous in various applications. Accordingly, event forecasting emerges to be a crucial problem, which aims to predict the next event based on the historical sequence. In this paper, we propose ANPP, an Attentive Neural Poi... | main | Machine Learning | 10.1609/aaai.v35i9.16929 | 35 | 9 | 7592-7600 | official | null | null |
10.1609/aaai.v35i9.16930 | Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization | https://ojs.aaai.org/index.php/AAAI/article/view/16930 | https://ojs.aaai.org/index.php/AAAI/article/download/16930/16737 | [
"Yinuo Guo",
"Hualei Zhu",
"Zeqi Lin",
"Bei Chen",
"Jian-Guang Lou",
"Dongmei Zhang"
] | Human intelligence exhibits compositional generalization (i.e., the capacity to understand and produce unseen combinations of seen components), but current neural seq2seq models lack such ability. In this paper, we revisit iterative back-translation, a simple yet effective semi-supervised method, to investigate whether... | main | Machine Learning | 10.1609/aaai.v35i9.16930 | 35 | 9 | 7601-7609 | official | 2012.04276 | title_snapshot |
10.1609/aaai.v35i9.16931 | Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16931 | https://ojs.aaai.org/index.php/AAAI/article/download/16931/16738 | [
"Umang Gupta",
"Aaron M Ferber",
"Bistra Dilkina",
"Greg Ver Steeg"
] | Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the data so that it is statistically independent of group membership, but this may throw away too much information when a reasonable compromise be... | main | Machine Learning | 10.1609/aaai.v35i9.16931 | 35 | 9 | 7610-7619 | official | 2101.04108 | title_snapshot |
10.1609/aaai.v35i9.16932 | Towards Reusable Network Components by Learning Compatible Representations | https://ojs.aaai.org/index.php/AAAI/article/view/16932 | https://ojs.aaai.org/index.php/AAAI/article/download/16932/16739 | [
"Michael Gygli",
"Jasper Uijlings",
"Vittorio Ferrari"
] | This paper proposes to make a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the training process to produce network components that are compatible across tasks. In particular, we split a network into two components, a featu... | main | Machine Learning | 10.1609/aaai.v35i9.16932 | 35 | 9 | 7620-7629 | official | 2004.03898 | title_snapshot |
10.1609/aaai.v35i9.16933 | High-Dimensional Bayesian Optimization via Tree-Structured Additive Models | https://ojs.aaai.org/index.php/AAAI/article/view/16933 | https://ojs.aaai.org/index.php/AAAI/article/download/16933/16740 | [
"Eric Han",
"Ishank Arora",
"Jonathan Scarlett"
] | Bayesian Optimization (BO) has shown significant success in tackling expensive low-dimensional black-box optimization problems. Many optimization problems of interest are high-dimensional, and scaling BO to such settings remains an important challenge. In this paper, we consider generalized additive models in which low... | main | Machine Learning | 10.1609/aaai.v35i9.16933 | 35 | 9 | 7630-7638 | official | 2012.13088 | title_snapshot |
10.1609/aaai.v35i9.16934 | Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability | https://ojs.aaai.org/index.php/AAAI/article/view/16934 | https://ojs.aaai.org/index.php/AAAI/article/download/16934/16741 | [
"Tao Han",
"Wei-Wei Tu",
"Yu-Feng Li"
] | Unlabeled data exploitation and interpretability are usually both required in reality. They, however, are conducted independently, and very few works try to connect the two. For unlabeled data exploitation, state-of-the-art semi-supervised learning (SSL) results have been achieved via encouraging the consistency of mod... | main | Machine Learning | 10.1609/aaai.v35i9.16934 | 35 | 9 | 7639-7646 | official | null | null |
10.1609/aaai.v35i9.16935 | DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16935 | https://ojs.aaai.org/index.php/AAAI/article/download/16935/16742 | [
"Mohammadhosein Hasanbeig",
"Natasha Yogananda Jeppu",
"Alessandro Abate",
"Tom Melham",
"Daniel Kroening"
] | This paper proposes DeepSynth, a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of c... | main | Machine Learning | 10.1609/aaai.v35i9.16935 | 35 | 9 | 7647-7656 | official | 1911.10244 | title_snapshot |
10.1609/aaai.v35i9.16936 | Liquid Time-constant Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16936 | https://ojs.aaai.org/index.php/AAAI/article/download/16936/16743 | [
"Ramin Hasani",
"Mathias Lechner",
"Alexander Amini",
"Daniela Rus",
"Radu Grosu"
] | We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varyin... | main | Machine Learning | 10.1609/aaai.v35i9.16936 | 35 | 9 | 7657-7666 | official | 2006.04439 | title_snapshot |
10.1609/aaai.v35i9.16937 | Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs | https://ojs.aaai.org/index.php/AAAI/article/view/16937 | https://ojs.aaai.org/index.php/AAAI/article/download/16937/16744 | [
"Aria HasanzadeZonuzy",
"Archana Bura",
"Dileep Kalathil",
"Srinivas Shakkottai"
] | Many physical systems have underlying safety considerations that require that the policy employed ensures the satisfaction of a set of constraints. The analytical formulation usually takes the form of a Constrained Markov Decision Process (CMDP). We focus on the case where the CMDP is unknown, and RL algorithms obtain ... | main | Machine Learning | 10.1609/aaai.v35i9.16937 | 35 | 9 | 7667-7674 | official | 2008.00311 | title_snapshot |
10.1609/aaai.v35i9.16938 | Analysing the Noise Model Error for Realistic Noisy Label Data | https://ojs.aaai.org/index.php/AAAI/article/view/16938 | https://ojs.aaai.org/index.php/AAAI/article/download/16938/16745 | [
"Michael A. Hedderich",
"Dawei Zhu",
"Dietrich Klakow"
] | Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors. A popular technique to overcome the negative effects of these noisy labels is noise modelling where the underlying noise process is modelled. ... | main | Machine Learning | 10.1609/aaai.v35i9.16938 | 35 | 9 | 7675-7684 | official | 2101.09763 | title_snapshot |
10.1609/aaai.v35i9.16939 | Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size | https://ojs.aaai.org/index.php/AAAI/article/view/16939 | https://ojs.aaai.org/index.php/AAAI/article/download/16939/16746 | [
"Christoph Hertrich",
"Martin Skutella"
] | The development of a satisfying and rigorous mathematical understanding of the performance of neural networks is a major challenge in artificial intelligence. Against this background, we study the expressive power of neural networks through the example of the classical NP-hard Knapsack Problem. Our main contribution is... | main | Machine Learning | 10.1609/aaai.v35i9.16939 | 35 | 9 | 7685-7693 | official | 2005.14105 | title_snapshot |
10.1609/aaai.v35i9.16940 | Scaling-Up Robust Gradient Descent Techniques | https://ojs.aaai.org/index.php/AAAI/article/view/16940 | https://ojs.aaai.org/index.php/AAAI/article/download/16940/16747 | [
"Matthew J. Holland"
] | We study a scalable alternative to robust gradient descent (RGD) techniques that can be used when losses and/or gradients can be heavy-tailed, though this will be unknown to the learner. The core technique is simple: instead of trying to robustly aggregate gradients at each step, which is costly and leads to sub-optima... | main | Machine Learning | 10.1609/aaai.v35i9.16940 | 35 | 9 | 7694-7701 | official | null | null |
10.1609/aaai.v35i9.16941 | Learning Model-Based Privacy Protection under Budget Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/16941 | https://ojs.aaai.org/index.php/AAAI/article/download/16941/16748 | [
"Junyuan Hong",
"Haotao Wang",
"Zhangyang Wang",
"Jiayu Zhou"
] | Protecting privacy in gradient-based learning has become increasingly critical as more sensitive information is being used. Many existing solutions seek to protect the sensitive gradients by constraining the overall privacy cost within a constant budget, where the protection is hand-designed and empirically calibrated ... | main | Machine Learning | 10.1609/aaai.v35i9.16941 | 35 | 9 | 7702-7710 | official | null | null |
10.1609/aaai.v35i9.16942 | Graph Game Embedding | https://ojs.aaai.org/index.php/AAAI/article/view/16942 | https://ojs.aaai.org/index.php/AAAI/article/download/16942/16749 | [
"Xiaobin Hong",
"Tong Zhang",
"Zhen Cui",
"Yuge Huang",
"Pengcheng Shen",
"Shaoxin Li",
"Jian Yang"
] | Graph embedding aims to encode nodes/edges into low-dimensional continuous features, and has become a crucial tool for graph analysis including graph/node classification, link prediction, etc. In this paper we propose a novel graph learning framework, named graph game embedding, to learn discriminative node representat... | main | Machine Learning | 10.1609/aaai.v35i9.16942 | 35 | 9 | 7711-7720 | official | null | null |
10.1609/aaai.v35i9.16917 | HiGAN: Handwriting Imitation Conditioned on Arbitrary-Length Texts and Disentangled Styles | https://ojs.aaai.org/index.php/AAAI/article/view/16917 | https://ojs.aaai.org/index.php/AAAI/article/download/16917/16724 | [
"Ji Gan",
"Weiqiang Wang"
] | Given limited handwriting scripts, humans can easily visualize (or imagine) what the handwritten words/texts would look like with other arbitrary textual contents. Moreover, a person also is able to imitate the handwriting styles of provided reference samples. Humans can do such hallucinations, perhaps because they can... | main | Machine Learning | 10.1609/aaai.v35i9.16917 | 35 | 9 | 7484-7492 | official | null | null |
10.1609/aaai.v35i9.16918 | Diffusion Network Inference from Partial Observations | https://ojs.aaai.org/index.php/AAAI/article/view/16918 | https://ojs.aaai.org/index.php/AAAI/article/download/16918/16725 | [
"Ting Gan",
"Keqi Han",
"Hao Huang",
"Shi Ying",
"Yunjun Gao",
"Zongpeng Li"
] | To infer the structure of a diffusion network from observed diffusion results, existing approaches customarily assume that observed data are complete and contain the final infection status of each node, as well as precise timestamps of node infections. Due to high cost and uncertainties in the monitoring of node infect... | main | Machine Learning | 10.1609/aaai.v35i9.16918 | 35 | 9 | 7493-7500 | official | null | null |
10.1609/aaai.v35i9.16919 | Stabilizing Q Learning Via Soft Mellowmax Operator | https://ojs.aaai.org/index.php/AAAI/article/view/16919 | https://ojs.aaai.org/index.php/AAAI/article/download/16919/16726 | [
"Yaozhong Gan",
"Zhe Zhang",
"Xiaoyang Tan"
] | Learning complicated value functions in high dimensional state space by function approximation is a challenging task, partially due to that the max-operator used in temporal difference updates can theoretically cause instability for most linear or non-linear approximation schemes. Mellowmax is a recently proposed diffe... | main | Machine Learning | 10.1609/aaai.v35i9.16919 | 35 | 9 | 7501-7509 | official | 2012.09456 | title_snapshot |
10.1609/aaai.v35i9.16920 | On the Convergence of Communication-Efficient Local SGD for Federated Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16920 | https://ojs.aaai.org/index.php/AAAI/article/download/16920/16727 | [
"Hongchang Gao",
"An Xu",
"Heng Huang"
] | Federated Learning (FL) has attracted increasing attention in recent years. A leading training algorithm in FL is local SGD, which updates the model parameter on each worker and averages model parameters across different workers only once in a while. Although it has fewer communication rounds than the classical paralle... | main | Machine Learning | 10.1609/aaai.v35i9.16920 | 35 | 9 | 7510-7518 | official | null | null |
10.1609/aaai.v35i9.16921 | A Trace-restricted Kronecker-Factored Approximation to Natural Gradient | https://ojs.aaai.org/index.php/AAAI/article/view/16921 | https://ojs.aaai.org/index.php/AAAI/article/download/16921/16728 | [
"Kaixin Gao",
"Xiaolei Liu",
"Zhenghai Huang",
"Min Wang",
"Zidong Wang",
"Dachuan Xu",
"Fan Yu"
] | Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. In this work, inspired by diagonal approximations and factored approximations suc... | main | Machine Learning | 10.1609/aaai.v35i9.16921 | 35 | 9 | 7519-7527 | official | 2011.10741 | title_snapshot |
10.1609/aaai.v35i9.16922 | Addressing Domain Gap via Content Invariant Representation for Semantic Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16922 | https://ojs.aaai.org/index.php/AAAI/article/download/16922/16729 | [
"Li Gao",
"Lefei Zhang",
"Qian Zhang"
] | The problem of unsupervised domain adaptation in semantic segmentation is a major challenge for numerous computer vision tasks because acquiring pixel-level labels is time-consuming with expensive human labor. A large gap exists among data distributions in different domains, which will cause severe performance loss whe... | main | Machine Learning | 10.1609/aaai.v35i9.16922 | 35 | 9 | 7528-7536 | official | null | null |
10.1609/aaai.v35i10.17123 | Tempered Sigmoid Activations for Deep Learning with Differential Privacy | https://ojs.aaai.org/index.php/AAAI/article/view/17123 | https://ojs.aaai.org/index.php/AAAI/article/download/17123/16930 | [
"Nicolas Papernot",
"Abhradeep Thakurta",
"Shuang Song",
"Steve Chien",
"Úlfar Erlingsson"
] | Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer differential privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running training with a different optimizer, but using the model architectu... | main | Machine Learning | 10.1609/aaai.v35i10.17123 | 35 | 10 | 9312-9321 | official | 2007.14191 | title_snapshot |
10.1609/aaai.v35i10.17124 | Vector Quantized Bayesian Neural Network Inference for Data Streams | https://ojs.aaai.org/index.php/AAAI/article/view/17124 | https://ojs.aaai.org/index.php/AAAI/article/download/17124/16931 | [
"Namuk Park",
"Taekyu Lee",
"Songkuk Kim"
] | Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far less widely used than non-Bayesian NNs in practice since they need iterative NN executions to predict a result for one data, and it gives rise to prohibitive computat... | main | Machine Learning | 10.1609/aaai.v35i10.17124 | 35 | 10 | 9322-9330 | official | 1907.05911 | title_snapshot |
10.1609/aaai.v35i10.17125 | Maximum Roaming Multi-Task Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17125 | https://ojs.aaai.org/index.php/AAAI/article/download/17125/16932 | [
"Lucas Pascal",
"Pietro Michiardi",
"Xavier Bost",
"Benoit Huet",
"Maria A. Zuluaga"
] | Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance. Nonetheless, the joint optimization of parameters with respect to multiple tasks remains an active research topic. Sub-partitioning the parameters between different tasks has proven to be an effic... | main | Machine Learning | 10.1609/aaai.v35i10.17125 | 35 | 10 | 9331-9341 | official | 2006.09762 | title_snapshot |
10.1609/aaai.v35i10.17126 | Fast PCA in 1-D Wasserstein Spaces via B-splines Representation and Metric Projection | https://ojs.aaai.org/index.php/AAAI/article/view/17126 | https://ojs.aaai.org/index.php/AAAI/article/download/17126/16933 | [
"Matteo Pegoraro",
"Mario Beraha"
] | We address the problem of performing Principal Component Analysis over a family of probability measures on the real line, using the Wasserstein geometry. We present a novel representation of the 2-Wasserstein space, based on a well known isometric bijection and a B-spline expansion. Thanks to this representation, we ar... | main | Machine Learning | 10.1609/aaai.v35i10.17126 | 35 | 10 | 9342-9349 | official | null | null |
10.1609/aaai.v35i10.17103 | Top-k Ranking Bayesian Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/17103 | https://ojs.aaai.org/index.php/AAAI/article/download/17103/16910 | [
"Quoc Phong Nguyen",
"Sebastian Tay",
"Bryan Kian Hsiang Low",
"Patrick Jaillet"
] | This paper presents a novel approach to top-k ranking Bayesian optimization (top-k ranking BO) which is a practical and significant generalization of preferential BO to handle top-k ranking and tie/indifference observations. We first design a surrogate model that is not only capable of catering to the above observation... | main | Machine Learning | 10.1609/aaai.v35i10.17103 | 35 | 10 | 9135-9143 | official | 2012.10688 | title_snapshot |
10.1609/aaai.v35i10.17104 | Distributional Reinforcement Learning via Moment Matching | https://ojs.aaai.org/index.php/AAAI/article/view/17104 | https://ojs.aaai.org/index.php/AAAI/article/download/17104/16911 | [
"Thanh Nguyen-Tang",
"Sunil Gupta",
"Svetha Venkatesh"
] | We consider the problem of learning a set of probability distributions from the empirical Bellman dynamics in distributional reinforcement learning (RL), a class of state-of-the-art methods that estimate the distribution, as opposed to only the expectation, of the total return. We formulate a method that learns a finit... | main | Machine Learning | 10.1609/aaai.v35i10.17104 | 35 | 10 | 9144-9152 | official | 2007.12354 | title_snapshot |
10.1609/aaai.v35i10.17105 | Precision-based Boosting | https://ojs.aaai.org/index.php/AAAI/article/view/17105 | https://ojs.aaai.org/index.php/AAAI/article/download/17105/16912 | [
"Mohammad Hossein Nikravan",
"Marjan Movahedan",
"Sandra Zilles"
] | AdaBoost is a highly popular ensemble classification method for which many variants have been published. This paper proposes a generic refinement of all of these AdaBoost variants. Instead of assigning weights based on the total error of the base classifiers (as in AdaBoost), our method uses class-specific error rates.... | main | Machine Learning | 10.1609/aaai.v35i10.17105 | 35 | 10 | 9153-9160 | official | null | null |
10.1609/aaai.v35i10.17106 | Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries | https://ojs.aaai.org/index.php/AAAI/article/view/17106 | https://ojs.aaai.org/index.php/AAAI/article/download/17106/16913 | [
"Kun-Peng Ning",
"Lue Tao",
"Songcan Chen",
"Sheng-Jun Huang"
] | In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improving the model robustness by training with noise perturbations. Most existing studies assume a fixed perturbation level for all training examp... | main | Machine Learning | 10.1609/aaai.v35i10.17106 | 35 | 10 | 9161-9169 | official | 2103.14824 | title_snapshot |
10.1609/aaai.v35i10.17107 | Learning of Structurally Unambiguous Probabilistic Grammars | https://ojs.aaai.org/index.php/AAAI/article/view/17107 | https://ojs.aaai.org/index.php/AAAI/article/download/17107/16914 | [
"Dolav Nitay",
"Dana Fisman",
"Michal Ziv-Ukelson"
] | The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic gramma... | main | Machine Learning | 10.1609/aaai.v35i10.17107 | 35 | 10 | 9170-9178 | official | 2011.07472 | title_snapshot |
10.1609/aaai.v35i10.17108 | RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices | https://ojs.aaai.org/index.php/AAAI/article/view/17108 | https://ojs.aaai.org/index.php/AAAI/article/download/17108/16915 | [
"Wei Niu",
"Mengshu Sun",
"Zhengang Li",
"Jou-An Chen",
"Jiexiong Guan",
"Xipeng Shen",
"Yanzhi Wang",
"Sijia Liu",
"Xue Lin",
"Bin Ren"
] | Mobile devices are becoming an important carrier for deep learning tasks, as they are being equipped with powerful, high-end mobile CPUs and GPUs. However, it is still a challenging task to execute 3D Convolutional Neural Networks (CNNs) targeting for real-time performance, besides high inference accuracy. The reason i... | main | Machine Learning | 10.1609/aaai.v35i10.17108 | 35 | 10 | 9179-9187 | official | 2007.09835 | title_snapshot |
10.1609/aaai.v35i10.17109 | Warm Starting CMA-ES for Hyperparameter Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/17109 | https://ojs.aaai.org/index.php/AAAI/article/download/17109/16916 | [
"Masahiro Nomura",
"Shuhei Watanabe",
"Youhei Akimoto",
"Yoshihiko Ozaki",
"Masaki Onishi"
] | Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CMA-ES is a promising BBO approach with a high degree of parallelism, and has been applied to HPO tasks, often under parallel implementation, ... | main | Machine Learning | 10.1609/aaai.v35i10.17109 | 35 | 10 | 9188-9196 | official | 2012.06932 | title_snapshot |
10.1609/aaai.v35i10.17110 | Inverse Reinforcement Learning From Like-Minded Teachers | https://ojs.aaai.org/index.php/AAAI/article/view/17110 | https://ojs.aaai.org/index.php/AAAI/article/download/17110/16917 | [
"Ritesh Noothigattu",
"Tom Yan",
"Ariel D. Procaccia"
] | We study the problem of learning a policy in a Markov decision process (MDP) based on observations of the actions taken by multiple teachers. We assume that the teachers are like-minded in that their reward functions -- while different from each other -- are random perturbations of an underlying reward function. Under ... | main | Machine Learning | 10.1609/aaai.v35i10.17110 | 35 | 10 | 9197-9204 | official | null | null |
10.1609/aaai.v35i10.17111 | Multinomial Logit Contextual Bandits: Provable Optimality and Practicality | https://ojs.aaai.org/index.php/AAAI/article/view/17111 | https://ojs.aaai.org/index.php/AAAI/article/download/17111/16918 | [
"Min-hwan Oh",
"Garud Iyengar"
] | We consider a sequential assortment selection problem where the user choice is given by a multinomial logit (MNL) choice model whose parameters are unknown. In each period, the learning agent observes a d-dimensional contextual information about the user and the N available items, and offers an assortment of size K to ... | main | Machine Learning | 10.1609/aaai.v35i10.17111 | 35 | 10 | 9205-9213 | official | 2103.13929 | title_snapshot |
10.1609/aaai.v35i10.17112 | Learning Deep Generative Models for Queuing Systems | https://ojs.aaai.org/index.php/AAAI/article/view/17112 | https://ojs.aaai.org/index.php/AAAI/article/download/17112/16919 | [
"Cesar Ojeda",
"Kostadin Cvejoski",
"Bodgan Georgiev",
"Christian Bauckhage",
"Jannis Schuecker",
"Ramses J. Sanchez"
] | Modern society is heavily dependent on large scale client-server systems with applications ranging from Internet and Communication Services to sophisticated logistics and deployment of goods. To maintain and improve such a system, a careful study of client and server dynamics is needed – e.g. response/service times, av... | main | Machine Learning | 10.1609/aaai.v35i10.17112 | 35 | 10 | 9214-9222 | official | null | null |
10.1609/aaai.v35i10.17113 | OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport | https://ojs.aaai.org/index.php/AAAI/article/view/17113 | https://ojs.aaai.org/index.php/AAAI/article/download/17113/16920 | [
"Derek Onken",
"Samy Wu Fung",
"Xingjian Li",
"Lars Ruthotto"
] | A normalizing flow is an invertible mapping between an arbitrary probability distribution and a standard normal distribution; it can be used for density estimation and statistical inference. Computing the flow follows the change of variables formula and thus requires invertibility of the mapping and an efficient way to... | main | Machine Learning | 10.1609/aaai.v35i10.17113 | 35 | 10 | 9223-9232 | official | 2006.00104 | title_snapshot |
10.1609/aaai.v35i10.17114 | FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/17114 | https://ojs.aaai.org/index.php/AAAI/article/download/17114/16921 | [
"Boris N. Oreshkin",
"Arezou Amini",
"Lucy Coyle",
"Mark Coates"
] | Forecasting of multivariate time-series is an important problem that has applications in traffic management, cellular network configuration, and quantitative finance. A special case of the problem arises when there is a graph available that captures the relationships between the time-series. In this paper we propose a ... | main | Machine Learning | 10.1609/aaai.v35i10.17114 | 35 | 10 | 9233-9241 | official | 2007.15531 | title_snapshot |
10.1609/aaai.v35i10.17115 | Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/17115 | https://ojs.aaai.org/index.php/AAAI/article/download/17115/16922 | [
"Boris N. Oreshkin",
"Dmitri Carpov",
"Nicolas Chapados",
"Yoshua Bengio"
] | Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms... | main | Machine Learning | 10.1609/aaai.v35i10.17115 | 35 | 10 | 9242-9250 | official | 2002.02887 | title_snapshot |
10.1609/aaai.v35i10.17116 | Augmented Experiment in Material Engineering Using Machine Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17116 | https://ojs.aaai.org/index.php/AAAI/article/download/17116/16923 | [
"Aomar Osmani",
"Massinissa Hamidi",
"Salah Bouhouche"
] | The synthesis of materials using the principle of thermogravimetric analysis to discover new anticorrosive paints requires several costly experiments. This paper presents an approach combining empirical data and domain analytical models to reduce the number of real experiments required to obtain the desired synthesis. ... | main | Machine Learning | 10.1609/aaai.v35i10.17116 | 35 | 10 | 9251-9258 | official | null | null |
10.1609/aaai.v35i10.17117 | Second Order Techniques for Learning Time-series with Structural Breaks | https://ojs.aaai.org/index.php/AAAI/article/view/17117 | https://ojs.aaai.org/index.php/AAAI/article/download/17117/16924 | [
"Takayuki Osogami"
] | We study fundamental problems in learning nonstationary time-series: how to effectively regularize time-series models and how to adaptively tune forgetting rates. The effectiveness of L2 regularization depends on the choice of coordinates, and the variables need to be appropriately normalized. In nonstationary environm... | main | Machine Learning | 10.1609/aaai.v35i10.17117 | 35 | 10 | 9259-9267 | official | null | null |
10.1609/aaai.v35i10.17118 | Defending against Backdoors in Federated Learning with Robust Learning Rate | https://ojs.aaai.org/index.php/AAAI/article/view/17118 | https://ojs.aaai.org/index.php/AAAI/article/download/17118/16925 | [
"Mustafa Safa Ozdayi",
"Murat Kantarcioglu",
"Yulia R. Gel"
] | Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving applications. At the same time, FL is susceptible to adversarial attacks due to decentralized and unvetted data. One important line of attacks a... | main | Machine Learning | 10.1609/aaai.v35i10.17118 | 35 | 10 | 9268-9276 | official | 2007.03767 | title_snapshot |
10.1609/aaai.v35i10.17119 | Robustness Guarantees for Mode Estimation with an Application to Bandits | https://ojs.aaai.org/index.php/AAAI/article/view/17119 | https://ojs.aaai.org/index.php/AAAI/article/download/17119/16926 | [
"Aldo Pacchiano",
"Heinrich Jiang",
"Michael I. Jordan"
] | Mode estimation is a classical problem in statistics with a wide range of applications in machine learning. Despite this, there is little understanding in its robustness properties under possibly adversarial data contamination. In this paper, we give precise robustness guarantees as well as privacy guarantees under sim... | main | Machine Learning | 10.1609/aaai.v35i10.17119 | 35 | 10 | 9277-9284 | official | 2003.02932 | title_snapshot |
10.1609/aaai.v35i10.17120 | Disentangled Information Bottleneck | https://ojs.aaai.org/index.php/AAAI/article/view/17120 | https://ojs.aaai.org/index.php/AAAI/article/download/17120/16927 | [
"Ziqi Pan",
"Li Niu",
"Jianfu Zhang",
"Liqing Zhang"
] | The information bottleneck (IB) method is a technique for extracting information that is relevant for predicting the target random variable from the source random variable, which is typically implemented by optimizing the IB Lagrangian that balances the compression and prediction terms. However, the IB Lagrangian is ha... | main | Machine Learning | 10.1609/aaai.v35i10.17120 | 35 | 10 | 9285-9293 | official | 2012.07372 | title_snapshot |
10.1609/aaai.v35i10.17121 | NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search | https://ojs.aaai.org/index.php/AAAI/article/view/17121 | https://ojs.aaai.org/index.php/AAAI/article/download/17121/16928 | [
"Rameswar Panda",
"Michele Merler",
"Mayoore S Jaiswal",
"Hui Wu",
"Kandan Ramakrishnan",
"Ulrich Finkler",
"Chun-Fu Richard Chen",
"Minsik Cho",
"Rogerio Feris",
"David Kung",
"Bishwaranjan Bhattacharjee"
] | Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existing NAS methods makes it difficult to directly search the architectures on large-scale tasks. The typical way of conducting large scale NAS is... | main | Machine Learning | 10.1609/aaai.v35i10.17121 | 35 | 10 | 9294-9302 | official | 2006.13314 | title_snapshot |
10.1609/aaai.v35i10.17122 | Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation | https://ojs.aaai.org/index.php/AAAI/article/view/17122 | https://ojs.aaai.org/index.php/AAAI/article/download/17122/16929 | [
"Bo Pang",
"Zhong-Ping Jiang"
] | This paper studies the robustness of reinforcement learning algorithms to errors in the learning process. Specifically, we revisit the benchmark problem of discrete-time linear quadratic regulation (LQR) and study the long-standing open question: Under what conditions is the policy iteration method robustly stable from... | main | Machine Learning | 10.1609/aaai.v35i10.17122 | 35 | 10 | 9303-9311 | official | 2008.11592 | title_snapshot |
10.1609/aaai.v35i10.17083 | Policy Optimization as Online Learning with Mediator Feedback | https://ojs.aaai.org/index.php/AAAI/article/view/17083 | https://ojs.aaai.org/index.php/AAAI/article/download/17083/16890 | [
"Alberto Maria Metelli",
"Matteo Papini",
"Pierluca D'Oro",
"Marcello Restelli"
] | Policy Optimization (PO) is a widely used approach to address continuous control tasks. In this paper, we introduce the notion of mediator feedback that frames PO as an online learning problem over the policy space. The additional available information, compared to the standard bandit feedback, allows reusing samples g... | main | Machine Learning | 10.1609/aaai.v35i10.17083 | 35 | 10 | 8958-8966 | official | 2012.08225 | title_snapshot |
10.1609/aaai.v35i10.17084 | Consistency and Finite Sample Behavior of Binary Class Probability Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/17084 | https://ojs.aaai.org/index.php/AAAI/article/download/17084/16891 | [
"Alexander Mey",
"Marco Loog"
] | We investigate to which extent one can recover class probabilities within the empirical risk minimization (ERM) paradigm. We extend existing results and emphasize the tight relations between empirical risk minimization and class probability estimation. Following previous literature on excess risk bounds and proper scor... | main | Machine Learning | 10.1609/aaai.v35i10.17084 | 35 | 10 | 8967-8974 | official | 1908.11823 | title_snapshot |
10.1609/aaai.v35i10.17085 | Discovering Fully Oriented Causal Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17085 | https://ojs.aaai.org/index.php/AAAI/article/download/17085/16892 | [
"Osman A Mian",
"Alexander Marx",
"Jilles Vreeken"
] | We study the problem of inferring causal graphs from observational data. We are particularly interested in discovering graphs where all edges are oriented, as opposed to the partially directed graph that the state of the art discover. To this end, we base our approach on the algorithmic Markov condition. Unlike the sta... | main | Machine Learning | 10.1609/aaai.v35i10.17085 | 35 | 10 | 8975-8982 | official | null | null |
10.1609/aaai.v35i10.17086 | Generative Semi-supervised Learning for Multivariate Time Series Imputation | https://ojs.aaai.org/index.php/AAAI/article/view/17086 | https://ojs.aaai.org/index.php/AAAI/article/download/17086/16893 | [
"Xiaoye Miao",
"Yangyang Wu",
"Jun Wang",
"Yunjun Gao",
"Xudong Mao",
"Jianwei Yin"
] | The missing values, widely existed in multivariate time series data, hinder the effective data analysis. Existing time series imputation methods do not make full use of the label information in real-life time series data. In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGA... | main | Machine Learning | 10.1609/aaai.v35i10.17086 | 35 | 10 | 8983-8991 | official | null | null |
10.1609/aaai.v35i10.17087 | A General Class of Transfer Learning Regression without Implementation Cost | https://ojs.aaai.org/index.php/AAAI/article/view/17087 | https://ojs.aaai.org/index.php/AAAI/article/download/17087/16894 | [
"Shunya Minami",
"Song Liu",
"Stephen Wu",
"Kenji Fukumizu",
"Ryo Yoshida"
] | We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By ... | main | Machine Learning | 10.1609/aaai.v35i10.17087 | 35 | 10 | 8992-8999 | official | 2006.13228 | title_snapshot |
10.1609/aaai.v35i10.17088 | Scheduling of Time-Varying Workloads Using Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17088 | https://ojs.aaai.org/index.php/AAAI/article/download/17088/16895 | [
"Shanka Subhra Mondal",
"Nikhil Sheoran",
"Subrata Mitra"
] | Resource usage of production workloads running on shared compute clusters often fluctuate significantly across time. While simultaneous spike in the resource usage between two workloads running on the same machine can create performance degradation, unused resources in a machine results in wastage and undesirable opera... | main | Machine Learning | 10.1609/aaai.v35i10.17088 | 35 | 10 | 9000-9008 | official | null | null |
10.1609/aaai.v35i10.17089 | Improved Mutual Information Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/17089 | https://ojs.aaai.org/index.php/AAAI/article/download/17089/16896 | [
"Youssef Mroueh",
"Igor Melnyk",
"Pierre Dognin",
"Jarret Ross",
"Tom Sercu"
] | We propose to estimate the KL divergence using a relaxed likelihood ratio estimation in a Reproducing Kernel Hilbert space. We show that the dual of our ratio estimator for KL in the particular case of Mutual Information estimation corresponds to a lower bound on the MI that is related to the so called Donsker Varadhan... | main | Machine Learning | 10.1609/aaai.v35i10.17089 | 35 | 10 | 9009-9017 | official | null | null |
10.1609/aaai.v35i10.17090 | Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines | https://ojs.aaai.org/index.php/AAAI/article/view/17090 | https://ojs.aaai.org/index.php/AAAI/article/download/17090/16897 | [
"Keerthiram Murugesan",
"Mattia Atzeni",
"Pavan Kapanipathi",
"Pushkar Shukla",
"Sadhana Kumaravel",
"Gerald Tesauro",
"Kartik Talamadupula",
"Mrinmaya Sachan",
"Murray Campbell"
] | Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. Such knowledge would allow agents to ef... | main | Machine Learning | 10.1609/aaai.v35i10.17090 | 35 | 10 | 9018-9027 | official | 2010.03790 | title_snapshot |
10.1609/aaai.v35i10.17091 | Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate | https://ojs.aaai.org/index.php/AAAI/article/view/17091 | https://ojs.aaai.org/index.php/AAAI/article/download/17091/16898 | [
"Mirco Mutti",
"Lorenzo Pratissoli",
"Marcello Restelli"
] | In a reward-free environment, what is a suitable intrinsic objective for an agent to pursue so that it can learn an optimal task-agnostic exploration policy? In this paper, we argue that the entropy of the state distribution induced by finite-horizon trajectories is a sensible target. Especially, we present a novel and... | main | Machine Learning | 10.1609/aaai.v35i10.17091 | 35 | 10 | 9028-9036 | official | 2007.04640 | title_snapshot |
10.1609/aaai.v35i10.17092 | Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent | https://ojs.aaai.org/index.php/AAAI/article/view/17092 | https://ojs.aaai.org/index.php/AAAI/article/download/17092/16899 | [
"Giorgi Nadiradze",
"Ilia Markov",
"Bapi Chatterjee",
"Vyacheslav Kungurtsev",
"Dan Alistarh"
] | One key element behind the recent progress of machine learning has been the ability to train machine learning models in large-scale distributed shared-memory and message-passing environments. Most of these models are trained employing variants of stochastic gradient descent (SGD) based optimization, but most methods in... | main | Machine Learning | 10.1609/aaai.v35i10.17092 | 35 | 10 | 9037-9045 | official | 2001.05918 | title_judge |
10.1609/aaai.v35i10.17093 | Game of Gradients: Mitigating Irrelevant Clients in Federated Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17093 | https://ojs.aaai.org/index.php/AAAI/article/download/17093/16900 | [
"Lokesh Nagalapatti",
"Ramasuri Narayanam"
] | The paradigm of Federated learning (FL) deals with multiple clients participating in collaborative training of a machine learning model under the orchestration of a central server. In this setup, each client’s data is private to itself and is not transferable to other clients or the server. Though FL paradigm has recei... | main | Machine Learning | 10.1609/aaai.v35i10.17093 | 35 | 10 | 9046-9054 | official | 2110.12257 | title_snapshot |
10.1609/aaai.v35i10.17094 | Objective-Based Hierarchical Clustering of Deep Embedding Vectors | https://ojs.aaai.org/index.php/AAAI/article/view/17094 | https://ojs.aaai.org/index.php/AAAI/article/download/17094/16901 | [
"Stanislav Naumov",
"Grigory Yaroslavtsev",
"Dmitrii Avdiukhin"
] | We initiate a comprehensive experimental study of objective-based hierarchical clustering methods on massive datasets consisting of deep embedding vectors from computer vision and NLP applications. This includes a large variety of image embedding (ImageNet, ImageNetV2, NaBirds), word embedding (Twitter, Wikipedia), and... | main | Machine Learning | 10.1609/aaai.v35i10.17094 | 35 | 10 | 9055-9063 | official | 2012.08466 | title_snapshot |
10.1609/aaai.v35i10.17095 | 5* Knowledge Graph Embeddings with Projective Transformations | https://ojs.aaai.org/index.php/AAAI/article/view/17095 | https://ojs.aaai.org/index.php/AAAI/article/download/17095/16902 | [
"Mojtaba Nayyeri",
"Sahar Vahdati",
"Can Aykul",
"Jens Lehmann"
] | Performing link prediction using knowledge graph embedding models has become a popular approach for knowledge graph completion. Such models employ a transformation function that maps nodes via edges into a vector space in order to measure the likelihood of the links. While mapping the individual nodes, the structure of... | main | Machine Learning | 10.1609/aaai.v35i10.17095 | 35 | 10 | 9064-9072 | official | 2006.04986 | title_snapshot |
10.1609/aaai.v35i10.17096 | Advice-Guided Reinforcement Learning in a non-Markovian Environment | https://ojs.aaai.org/index.php/AAAI/article/view/17096 | https://ojs.aaai.org/index.php/AAAI/article/download/17096/16903 | [
"Daniel Neider",
"Jean-Raphael Gaglione",
"Ivan Gavran",
"Ufuk Topcu",
"Bo Wu",
"Zhe Xu"
] | We study a class of reinforcement learning tasks in which the agent receives its reward for complex, temporally-extended behaviors sparsely. For such tasks, the problem is how to augment the state-space so as to make the reward function Markovian in an efficient way. While some existing solutions assume that the reward... | main | Machine Learning | 10.1609/aaai.v35i10.17096 | 35 | 10 | 9073-9080 | official | null | null |
10.1609/aaai.v35i10.17097 | Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17097 | https://ojs.aaai.org/index.php/AAAI/article/download/17097/16904 | [
"A. Tuan Nguyen",
"Hyewon Jeong",
"Eunho Yang",
"Sung Ju Hwang"
] | Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yiel... | main | Machine Learning | 10.1609/aaai.v35i10.17097 | 35 | 10 | 9081-9091 | official | 2006.12777 | title_snapshot |
10.1609/aaai.v35i10.17098 | Modular Graph Transformer Networks for Multi-Label Image Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17098 | https://ojs.aaai.org/index.php/AAAI/article/download/17098/16905 | [
"Hoang D. Nguyen",
"Xuan-Son Vu",
"Duc-Trong Le"
] | With the recent advances in graph neural networks, there is a rising number of studies on graph-based multi-label classification with the consideration of object dependencies within visual data. Nevertheless, graph representations can become indistinguishable due to the complex nature of label relationships. We propose... | main | Machine Learning | 10.1609/aaai.v35i10.17098 | 35 | 10 | 9092-9100 | official | null | null |
10.1609/aaai.v35i10.17099 | Differentially Private k-Means via Exponential Mechanism and Max Cover | https://ojs.aaai.org/index.php/AAAI/article/view/17099 | https://ojs.aaai.org/index.php/AAAI/article/download/17099/16906 | [
"Huy L. Nguyen",
"Anamay Chaturvedi",
"Eric Z Xu"
] | We introduce a new (ϵₚ, δₚ)-differentially private algorithm for the k-means clustering problem. Given a dataset in Euclidean space, the k-means clustering problem requires one to find k points in that space such that the sum of squares of Euclidean distances between each data point and its closest respective point amo... | main | Machine Learning | 10.1609/aaai.v35i10.17099 | 35 | 10 | 9101-9108 | official | 2009.01220 | title_judge |
10.1609/aaai.v35i10.17100 | Minimum Robust Multi-Submodular Cover for Fairness | https://ojs.aaai.org/index.php/AAAI/article/view/17100 | https://ojs.aaai.org/index.php/AAAI/article/download/17100/16907 | [
"Lan N. Nguyen",
"My T. Thai"
] | In this paper, we study a novel problem, Minimum Robust Multi-Submodular Cover for Fairness (MinRF), as follows: given a ground set V; m monotone submodular functions f_1,...,f_m; m thresholds T_1,...,T_m and a non-negative integer r; MinRF asks for the smallest set S such that f_i(S \ X) ≥ T_i for all i ∈ [m] and |X| ... | main | Machine Learning | 10.1609/aaai.v35i10.17100 | 35 | 10 | 9109-9116 | official | 2012.07936 | title_snapshot |
10.1609/aaai.v35i10.17101 | Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/17101 | https://ojs.aaai.org/index.php/AAAI/article/download/17101/16908 | [
"Nam Nguyen",
"Brian Quanz"
] | Probabilistic forecasting of high dimensional multivariate time series is a notoriously challenging task, both in terms of computational burden and distribution modeling. Most previous work either makes simple distribution assumptions or abandons modeling cross-series correlations. A promising line of work exploits sca... | main | Machine Learning | 10.1609/aaai.v35i10.17101 | 35 | 10 | 9117-9125 | official | 2101.10460 | title_snapshot |
10.1609/aaai.v35i10.17102 | An Information-Theoretic Framework for Unifying Active Learning Problems | https://ojs.aaai.org/index.php/AAAI/article/view/17102 | https://ojs.aaai.org/index.php/AAAI/article/download/17102/16909 | [
"Quoc Phong Nguyen",
"Bryan Kian Hsiang Low",
"Patrick Jaillet"
] | This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant. We first introduce a novel active learning criterion that subsumes an existing LSE algorithm and achieves state-of-the-art performance in L... | main | Machine Learning | 10.1609/aaai.v35i10.17102 | 35 | 10 | 9126-9134 | official | 2012.10695 | title_snapshot |
10.1609/aaai.v35i10.17063 | Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors | https://ojs.aaai.org/index.php/AAAI/article/view/17063 | https://ojs.aaai.org/index.php/AAAI/article/download/17063/16870 | [
"Su Lu",
"Han-Jia Ye",
"De-Chuan Zhan"
] | Few-Shot Learning (FSL) is essential for visual recognition. Many methods tackle this challenging problem via learning an embedding function from seen classes and transfer it to unseen classes with a few labeled instances. Researchers recently found it beneficial to incorporate task-specific feature adaptation into FSL... | main | Machine Learning | 10.1609/aaai.v35i10.17063 | 35 | 10 | 8776-8783 | official | null | null |
10.1609/aaai.v35i10.17064 | PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector | https://ojs.aaai.org/index.php/AAAI/article/view/17064 | https://ojs.aaai.org/index.php/AAAI/article/download/17064/16871 | [
"Chuan Luo",
"Pu Zhao",
"Chen Chen",
"Bo Qiao",
"Chao Du",
"Hongyu Zhang",
"Wei Wu",
"Shaowei Cai",
"Bing He",
"Saravanakumar Rajmohan",
"Qingwei Lin"
] | Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relie... | main | Machine Learning | 10.1609/aaai.v35i10.17064 | 35 | 10 | 8784-8792 | official | null | null |
10.1609/aaai.v35i10.17065 | Revisiting Co-Occurring Directions: Sharper Analysis and Efficient Algorithm for Sparse Matrices | https://ojs.aaai.org/index.php/AAAI/article/view/17065 | https://ojs.aaai.org/index.php/AAAI/article/download/17065/16872 | [
"Luo Luo",
"Cheng Chen",
"Guangzeng Xie",
"Haishan Ye"
] | We study the streaming model for approximate matrix multiplication (AMM). We are interested in the scenario that the algorithm can only take one pass over the data with limited memory. The state-of-the-art deterministic sketching algorithm for streaming AMM is the co-occurring directions (COD), which has much smaller a... | main | Machine Learning | 10.1609/aaai.v35i10.17065 | 35 | 10 | 8793-8800 | official | 2009.02553 | title_snapshot |
10.1609/aaai.v35i10.17066 | Semi-supervised Medical Image Segmentation through Dual-task Consistency | https://ojs.aaai.org/index.php/AAAI/article/view/17066 | https://ojs.aaai.org/index.php/AAAI/article/download/17066/16873 | [
"Xiangde Luo",
"Jieneng Chen",
"Tao Song",
"Guotai Wang"
] | Deep learning-based semi-supervised learning (SSL) algorithms have led to promising results in medical images segmentation and can alleviate doctors' expensive annotations by leveraging unlabeled data. However, most of the existing SSL algorithms in literature tend to regularize the model training by perturbing network... | main | Machine Learning | 10.1609/aaai.v35i10.17066 | 35 | 10 | 8801-8809 | official | 2009.04448 | title_snapshot |
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