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.v35i12.17287 | Personalized Adaptive Meta Learning for Cold-start User Preference Prediction | https://ojs.aaai.org/index.php/AAAI/article/view/17287 | https://ojs.aaai.org/index.php/AAAI/article/download/17287/17094 | [
"Runsheng Yu",
"Yu Gong",
"Xu He",
"Yu Zhu",
"Qingwen Liu",
"Wenwu Ou",
"Bo An"
] | A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot l... | main | Machine Learning | 10.1609/aaai.v35i12.17287 | 35 | 12 | 10772-10780 | official | 2012.11842 | title_snapshot |
10.1609/aaai.v35i12.17288 | Measuring Dependence with Matrix-based Entropy Functional | https://ojs.aaai.org/index.php/AAAI/article/view/17288 | https://ojs.aaai.org/index.php/AAAI/article/download/17288/17095 | [
"Shujian Yu",
"Francesco Alesiani",
"Xi Yu",
"Robert Jenssen",
"Jose Principe"
] | Measuring the dependence of data plays a central role in statistics and machine learning. In this work, we summarize and generalize the main idea of existing information-theoretic dependence measures into a higher-level perspective by the Shearer's inequality. Based on our generalization, we then propose two measures, ... | main | Machine Learning | 10.1609/aaai.v35i12.17288 | 35 | 12 | 10781-10789 | official | 2101.10160 | title_snapshot |
10.1609/aaai.v35i12.17289 | Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis | https://ojs.aaai.org/index.php/AAAI/article/view/17289 | https://ojs.aaai.org/index.php/AAAI/article/download/17289/17096 | [
"Wenmeng Yu",
"Hua Xu",
"Ziqi Yuan",
"Jiele Wu"
] | Representation Learning is a significant and challenging task in multimodal learning. Effective modality representations should contain two parts of characteristics: the consistency and the difference. Due to the unified multimodal annota- tion, existing methods are restricted in capturing differenti- ated information.... | main | Machine Learning | 10.1609/aaai.v35i12.17289 | 35 | 12 | 10790-10797 | official | 2102.04830 | title_snapshot |
10.1609/aaai.v35i12.17290 | Knowledge-Guided Object Discovery with Acquired Deep Impressions | https://ojs.aaai.org/index.php/AAAI/article/view/17290 | https://ojs.aaai.org/index.php/AAAI/article/download/17290/17097 | [
"Jinyang Yuan",
"Bin Li",
"Xiangyang Xue"
] | We present a framework called Acquired Deep Impressions (ADI) which continuously learns knowledge of objects as ``impressions'' for compositional scene understanding. In this framework, the model first acquires knowledge from scene images containing a single object in a supervised manner, and then continues to learn fr... | main | Machine Learning | 10.1609/aaai.v35i12.17290 | 35 | 12 | 10798-10806 | official | 2103.10611 | title_snapshot |
10.1609/aaai.v35i12.17291 | Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17291 | https://ojs.aaai.org/index.php/AAAI/article/download/17291/17098 | [
"Syed Zawad",
"Ahsan Ali",
"Pin-Yu Chen",
"Ali Anwar",
"Yi Zhou",
"Nathalie Baracaldo",
"Yuan Tian",
"Feng Yan"
] | Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks. This paper focuses on characterizing and understanding its impact on backdooring attacks in federated learning through comprehensive experiments using synthetic ... | main | Machine Learning | 10.1609/aaai.v35i12.17291 | 35 | 12 | 10807-10814 | official | 2102.00655 | title_snapshot |
10.1609/aaai.v35i12.17292 | Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks | https://ojs.aaai.org/index.php/AAAI/article/view/17292 | https://ojs.aaai.org/index.php/AAAI/article/download/17292/17099 | [
"Huimin Zeng",
"Chen Zhu",
"Tom Goldstein",
"Furong Huang"
] | Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks. However, traditional defense mechanisms assume a uniform attack over the examples according to the underlying data distribution, which is apparently unrealistic a... | main | Machine Learning | 10.1609/aaai.v35i12.17292 | 35 | 12 | 10815-10823 | official | 2010.12989 | title_snapshot |
10.1609/aaai.v35i12.17293 | Contrastive Self-supervised Learning for Graph Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17293 | https://ojs.aaai.org/index.php/AAAI/article/download/17293/17100 | [
"Jiaqi Zeng",
"Pengtao Xie"
] | Graph classification is a widely studied problem and has broad applications. In many real-world problems, the number of labeled graphs available for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose two approaches based on contrastive self-su... | main | Machine Learning | 10.1609/aaai.v35i12.17293 | 35 | 12 | 10824-10832 | official | 2009.05923 | title_snapshot |
10.1609/aaai.v35i12.17294 | Data-driven Competitive Algorithms for Online Knapsack and Set Cover | https://ojs.aaai.org/index.php/AAAI/article/view/17294 | https://ojs.aaai.org/index.php/AAAI/article/download/17294/17101 | [
"Ali Zeynali",
"Bo Sun",
"Mohammad Hajiesmaili",
"Adam Wierman"
] | The design of online algorithms has tended to focus on algorithms with worst-case guarantees, e.g., bounds on the competitive ratio. However, it is well-known that such algorithms are often overly pessimistic, performing sub-optimally on non-worst-case inputs. In this paper, we develop an approach for data-driven desig... | main | Machine Learning | 10.1609/aaai.v35i12.17294 | 35 | 12 | 10833-10841 | official | 2012.05361 | title_snapshot |
10.1609/aaai.v35i12.17295 | A Hybrid Stochastic Gradient Hamiltonian Monte Carlo Method | https://ojs.aaai.org/index.php/AAAI/article/view/17295 | https://ojs.aaai.org/index.php/AAAI/article/download/17295/17102 | [
"Chao Zhang",
"Zhijian Li",
"Zebang Shen",
"Jiahao Xie",
"Hui Qian"
] | Recent theoretical analyses reveal that existing Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods need large mini-batches of samples (exponentially dependent on the dimension) to reduce the mean square error of gradient estimates and ensure non-asymptotic convergence guarantees when the target distributio... | main | Machine Learning | 10.1609/aaai.v35i12.17295 | 35 | 12 | 10842-10850 | official | null | null |
10.1609/aaai.v35i12.17296 | CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/17296 | https://ojs.aaai.org/index.php/AAAI/article/download/17296/17103 | [
"Chaoyun Zhang",
"Marco Fiore",
"Iain Murray",
"Paul Patras"
] | This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic Point-cloud Convolution (DConv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extr... | main | Machine Learning | 10.1609/aaai.v35i12.17296 | 35 | 12 | 10851-10858 | official | 1907.12410 | title_snapshot |
10.1609/aaai.v35i12.17297 | Exploration by Maximizing Renyi Entropy for Reward-Free RL Framework | https://ojs.aaai.org/index.php/AAAI/article/view/17297 | https://ojs.aaai.org/index.php/AAAI/article/download/17297/17104 | [
"Chuheng Zhang",
"Yuanying Cai",
"Longbo Huang",
"Jian Li"
] | Exploration is essential for reinforcement learning (RL). To face the challenges of exploration, we consider a reward-free RL framework that completely separates exploration from exploitation and brings new challenges for exploration algorithms. In the exploration phase, the agent learns an exploratory policy by intera... | main | Machine Learning | 10.1609/aaai.v35i12.17297 | 35 | 12 | 10859-10867 | official | 2006.06193 | title_snapshot |
10.1609/aaai.v35i12.17298 | Efficient Folded Attention for Medical Image Reconstruction and Segmentation | https://ojs.aaai.org/index.php/AAAI/article/view/17298 | https://ojs.aaai.org/index.php/AAAI/article/download/17298/17105 | [
"Hang Zhang",
"Jinwei Zhang",
"Rongguang Wang",
"Qihao Zhang",
"Pascal Spincemaille",
"Thanh D. Nguyen",
"Yi Wang"
] | Recently, 3D medical image reconstruction (MIR) and segmentation (MIS) based on deep neural networks have been developed with promising results, and attention mechanism has been further designed for performance enhancement. However, the large size of 3D volume images poses a great computational challenge to traditional... | main | Machine Learning | 10.1609/aaai.v35i12.17298 | 35 | 12 | 10868-10876 | official | 2009.05576 | title_judge |
10.1609/aaai.v35i12.17299 | Interpreting Multivariate Shapley Interactions in DNNs | https://ojs.aaai.org/index.php/AAAI/article/view/17299 | https://ojs.aaai.org/index.php/AAAI/article/download/17299/17106 | [
"Hao Zhang",
"Yichen Xie",
"Longjie Zheng",
"Die Zhang",
"Quanshi Zhang"
] | This paper aims to explain deep neural networks (DNNs) from the perspective of multivariate interactions. In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN. Input variables with strong interactions usually form a coalition and reflect prototype features, wh... | main | Machine Learning | 10.1609/aaai.v35i12.17299 | 35 | 12 | 10877-10886 | official | 2010.05045 | title_snapshot |
10.1609/aaai.v35i12.17300 | Sample Efficient Reinforcement Learning with REINFORCE | https://ojs.aaai.org/index.php/AAAI/article/view/17300 | https://ojs.aaai.org/index.php/AAAI/article/download/17300/17107 | [
"Junzi Zhang",
"Jongho Kim",
"Brendan O'Donoghue",
"Stephen Boyd"
] | Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based m... | main | Machine Learning | 10.1609/aaai.v35i12.17300 | 35 | 12 | 10887-10895 | official | 2010.11364 | title_snapshot |
10.1609/aaai.v35i12.17301 | Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating | https://ojs.aaai.org/index.php/AAAI/article/view/17301 | https://ojs.aaai.org/index.php/AAAI/article/download/17301/17108 | [
"Qingsong Zhang",
"Bin Gu",
"Cheng Deng",
"Heng Huang"
] | Vertical federated learning (VFL) attracts increasing attention due to the emerging demands of multi-party collaborative modeling and concerns of privacy leakage. In the real VFL applications, usually only one or partial parties hold labels, which makes it challenging for all parties to collaboratively learn the model ... | main | Machine Learning | 10.1609/aaai.v35i12.17301 | 35 | 12 | 10896-10904 | official | 2103.00958 | title_snapshot |
10.1609/aaai.v35i12.17302 | Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17302 | https://ojs.aaai.org/index.php/AAAI/article/download/17302/17109 | [
"Shangtong Zhang",
"Bo Liu",
"Shimon Whiteson"
] | We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP optimizing the variance of a per-step reward random variable. MVPI enjoys great flexibility in that any policy evaluation method and risk-neutral control method can be dropped in for risk-averse con... | main | Machine Learning | 10.1609/aaai.v35i12.17302 | 35 | 12 | 10905-10913 | official | 2004.10888 | title_snapshot |
10.1609/aaai.v35i12.17263 | Toward Understanding the Influence of Individual Clients in Federated Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17263 | https://ojs.aaai.org/index.php/AAAI/article/download/17263/17070 | [
"Yihao Xue",
"Chaoyue Niu",
"Zhenzhe Zheng",
"Shaojie Tang",
"Chengfei Lyu",
"Fan Wu",
"Guihai Chen"
] | Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guarantee of the global model, however, it is still unclear how each individual client influences the collaborative training process. In this work... | main | Machine Learning | 10.1609/aaai.v35i12.17263 | 35 | 12 | 10560-10567 | official | 2012.10936 | title_snapshot |
10.1609/aaai.v35i12.17264 | Adversarial Partial Multi-Label Learning with Label Disambiguation | https://ojs.aaai.org/index.php/AAAI/article/view/17264 | https://ojs.aaai.org/index.php/AAAI/article/download/17264/17071 | [
"Yan Yan",
"Yuhong Guo"
] | Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community. In this paper, we propose a novel adversarial learning model, PML-GAN, under a generalized encod... | main | Machine Learning | 10.1609/aaai.v35i12.17264 | 35 | 12 | 10568-10576 | official | null | null |
10.1609/aaai.v35i12.17265 | Near Lossless Transfer Learning for Spiking Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17265 | https://ojs.aaai.org/index.php/AAAI/article/download/17265/17072 | [
"Zhanglu Yan",
"Jun Zhou",
"Weng-Fai Wong"
] | Spiking neural networks (SNNs) significantly reduce energy consumption by replacing weight multiplications with additions. This makes SNNs suitable for energy-constrained platforms. However, due to its discrete activation, training of SNNs remains a challenge. A popular approach is to first train an equivalent CNN usin... | main | Machine Learning | 10.1609/aaai.v35i12.17265 | 35 | 12 | 10577-10584 | official | null | null |
10.1609/aaai.v35i12.17266 | DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation | https://ojs.aaai.org/index.php/AAAI/article/view/17266 | https://ojs.aaai.org/index.php/AAAI/article/download/17266/17073 | [
"Zhicong Yan",
"Gaolei Li",
"Yuan TIan",
"Jun Wu",
"Shenghong Li",
"Mingzhe Chen",
"H. Vincent Poor"
] | The threat of data-poisoning backdoor attacks on learning algorithms typically comes from the labeled data. However, in deep semi-supervised learning (SSL), unknown threats mainly stem from the unlabeled data. In this paper, we propose a novel deep hidden backdoor (DeHiB) attack scheme for SSL-based systems. In contras... | main | Machine Learning | 10.1609/aaai.v35i12.17266 | 35 | 12 | 10585-10593 | official | null | null |
10.1609/aaai.v35i12.17267 | Robust Bandit Learning with Imperfect Context | https://ojs.aaai.org/index.php/AAAI/article/view/17267 | https://ojs.aaai.org/index.php/AAAI/article/download/17267/17074 | [
"Jianyi Yang",
"Shaolei Ren"
] | A standard assumption in contextual multi-arm bandit is that the true context is perfectly known before arm selection. Nonetheless, in many practical applications (e.g., cloud resource management), prior to arm selection, the context information can only be acquired by prediction subject to errors or adversarial modifi... | main | Machine Learning | 10.1609/aaai.v35i12.17267 | 35 | 12 | 10594-10602 | official | 2102.05018 | title_snapshot |
10.1609/aaai.v35i12.17268 | Hierarchical Graph Capsule Network | https://ojs.aaai.org/index.php/AAAI/article/view/17268 | https://ojs.aaai.org/index.php/AAAI/article/download/17268/17075 | [
"Jinyu Yang",
"Peilin Zhao",
"Yu Rong",
"Chaochao Yan",
"Chunyuan Li",
"Hehuan Ma",
"Junzhou Huang"
] | Graph Neural Networks (GNNs) draw their strength from explicitly modeling the topological information of structured data. However, existing GNNs suffer from limited capability in capturing the hierarchical graph representation which plays an important role in graph classification. In this paper, we innovatively propose... | main | Machine Learning | 10.1609/aaai.v35i12.17268 | 35 | 12 | 10603-10611 | official | 2012.08734 | title_snapshot |
10.1609/aaai.v35i12.17269 | FracBits: Mixed Precision Quantization via Fractional Bit-Widths | https://ojs.aaai.org/index.php/AAAI/article/view/17269 | https://ojs.aaai.org/index.php/AAAI/article/download/17269/17076 | [
"Linjie Yang",
"Qing Jin"
] | Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-t... | main | Machine Learning | 10.1609/aaai.v35i12.17269 | 35 | 12 | 10612-10620 | official | 2007.02017 | title_snapshot |
10.1609/aaai.v35i12.17270 | On Convergence of Gradient Expected Sarsa(λ) | https://ojs.aaai.org/index.php/AAAI/article/view/17270 | https://ojs.aaai.org/index.php/AAAI/article/download/17270/17077 | [
"Long Yang",
"Gang Zheng",
"Yu Zhang",
"Qian Zheng",
"Pengfei Li",
"Gang Pan"
] | We study the convergence of Expected Sarsa(λ) with function approximation. We show that with off-line es- timate (multi-step bootstrapping) to ExpectedSarsa(λ) is unstable for off-policy learning. Furthermore, based on convex-concave saddle-point framework, we propose a con- vergent Gradient Expected Sarsa(λ) (GES(λ)) ... | main | Machine Learning | 10.1609/aaai.v35i12.17270 | 35 | 12 | 10621-10629 | official | 2012.07199 | title_snapshot |
10.1609/aaai.v35i12.17271 | Sample Complexity of Policy Gradient Finding Second-Order Stationary Points | https://ojs.aaai.org/index.php/AAAI/article/view/17271 | https://ojs.aaai.org/index.php/AAAI/article/download/17271/17078 | [
"Long Yang",
"Qian Zheng",
"Gang Pan"
] | The policy-based reinforcement learning (RL) can be considered as maximization of its objective. However, due to the inherent non-concavity of its objective, the policy gradient method to a first-order stationary point (FOSP) cannot guar- antee a maximal point. A FOSP can be a minimal or even a saddle point, which is u... | main | Machine Learning | 10.1609/aaai.v35i12.17271 | 35 | 12 | 10630-10638 | official | 2012.01491 | title_snapshot |
10.1609/aaai.v35i12.17272 | WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17272 | https://ojs.aaai.org/index.php/AAAI/article/download/17272/17079 | [
"Qisong Yang",
"Thiago D. Simão",
"Simon H Tindemans",
"Matthijs T. J. Spaan"
] | Safe exploration is regarded as a key priority area for reinforcement learning research. With separate reward and safety signals, it is natural to cast it as constrained reinforcement learning, where expected long-term costs of policies are constrained. However, it can be hazardous to set constraints on the expected sa... | main | Machine Learning | 10.1609/aaai.v35i12.17272 | 35 | 12 | 10639-10646 | official | null | null |
10.1609/aaai.v35i12.17273 | Characterizing the Evasion Attackability of Multi-label Classifiers | https://ojs.aaai.org/index.php/AAAI/article/view/17273 | https://ojs.aaai.org/index.php/AAAI/article/download/17273/17080 | [
"Zhuo Yang",
"Yufei Han",
"Xiangliang Zhang"
] | Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the attackability of the multi-label adversarial threat is the key to interpret the origin of the adversarial vulnerability and to understand how to miti... | main | Machine Learning | 10.1609/aaai.v35i12.17273 | 35 | 12 | 10647-10655 | official | 2012.09427 | title_snapshot |
10.1609/aaai.v35i12.17274 | SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17274 | https://ojs.aaai.org/index.php/AAAI/article/download/17274/17081 | [
"Ting Yao",
"Yiheng Zhang",
"Zhaofan Qiu",
"Yingwei Pan",
"Tao Mei"
] | A steady momentum of innovations and breakthroughs has convincingly pushed the limits of unsupervised image representation learning. Compared to static 2D images, video has one more dimension (time). The inherent supervision existing in such sequential structure offers a fertile ground for building unsupervised learnin... | main | Machine Learning | 10.1609/aaai.v35i12.17274 | 35 | 12 | 10656-10664 | official | 2008.00975 | title_snapshot |
10.1609/aaai.v35i12.17275 | ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17275 | https://ojs.aaai.org/index.php/AAAI/article/download/17275/17082 | [
"Zhewei Yao",
"Amir Gholami",
"Sheng Shen",
"Mustafa Mustafa",
"Kurt Keutzer",
"Michael Mahoney"
] | Incorporating second-order curvature information into machine learning optimization algorithms can be subtle, and doing so naïvely can lead to high per-iteration costs associated with forming the Hessian and performing the associated linear system solve. To address this, we introduce ADAHESSIAN, a new stochastic optimi... | main | Machine Learning | 10.1609/aaai.v35i12.17275 | 35 | 12 | 10665-10673 | official | 2006.00719 | title_snapshot |
10.1609/aaai.v35i12.17276 | Improving Sample Efficiency in Model-Free Reinforcement Learning from Images | https://ojs.aaai.org/index.php/AAAI/article/view/17276 | https://ojs.aaai.org/index.php/AAAI/article/download/17276/17083 | [
"Denis Yarats",
"Amy Zhang",
"Ilya Kostrikov",
"Brandon Amos",
"Joelle Pineau",
"Rob Fergus"
] | Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult. A promising approach is to learn a latent representation together with the control policy. However, fitting a high-capacity encoder using a scarce reward signal is sample inef... | main | Machine Learning | 10.1609/aaai.v35i12.17276 | 35 | 12 | 10674-10681 | official | 1910.01741 | title_snapshot |
10.1609/aaai.v35i12.17277 | Task Cooperation for Semi-Supervised Few-Shot Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17277 | https://ojs.aaai.org/index.php/AAAI/article/download/17277/17084 | [
"Han-Jia Ye",
"Xin-Chun Li",
"De-Chuan Zhan"
] | Training a model with limited data is an essential task for machine learning and visual recognition. Few-shot learning approaches meta-learn a task-level inductive bias from SEEN class few-shot tasks, and the meta-model is expected to facilitate the few-shot learning with UNSEEN classes. Inspired by the idea that unlab... | main | Machine Learning | 10.1609/aaai.v35i12.17277 | 35 | 12 | 10682-10690 | official | null | null |
10.1609/aaai.v35i12.17278 | Amata: An Annealing Mechanism for Adversarial Training Acceleration | https://ojs.aaai.org/index.php/AAAI/article/view/17278 | https://ojs.aaai.org/index.php/AAAI/article/download/17278/17085 | [
"Nanyang Ye",
"Qianxiao Li",
"Xiao-Yun Zhou",
"Zhanxing Zhu"
] | Despite the empirical success in various domains, it has been revealed that deep neural networks are vulnerable to maliciously perturbed input data that much degrade their performance. This is known as adversarial attacks. To counter adversarial attacks, adversarial training formulated as a form of robust optimization ... | main | Machine Learning | 10.1609/aaai.v35i12.17278 | 35 | 12 | 10691-10699 | official | 2012.08112 | title_snapshot |
10.1609/aaai.v35i12.17279 | Sequential Generative Exploration Model for Partially Observable Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17279 | https://ojs.aaai.org/index.php/AAAI/article/download/17279/17086 | [
"Haiyan Yin",
"Jianda Chen",
"Sinno Jialin Pan",
"Sebastian Tschiatschek"
] | Many challenging partially observable reinforcement learning problems have sparse rewards and most existing model-free algorithms struggle with such reward sparsity. In this paper, we propose a novel reward shaping approach to infer the intrinsic rewards for the agent from a sequential generative model. Specifically, t... | main | Machine Learning | 10.1609/aaai.v35i12.17279 | 35 | 12 | 10700-10708 | official | null | null |
10.1609/aaai.v35i12.17280 | Enhanced Audio Tagging via Multi- to Single-Modal Teacher-Student Mutual Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17280 | https://ojs.aaai.org/index.php/AAAI/article/download/17280/17087 | [
"Yifang Yin",
"Harsh Shrivastava",
"Ying Zhang",
"Zhenguang Liu",
"Rajiv Ratn Shah",
"Roger Zimmermann"
] | Recognizing ongoing events based on acoustic clues has been a critical yet challenging problem that has attracted significant research attention in recent years. Joint audio-visual analysis can improve the event detection accuracy but may not always be feasible as under many circumstances only audio recordings are avai... | main | Machine Learning | 10.1609/aaai.v35i12.17280 | 35 | 12 | 10709-10717 | official | null | null |
10.1609/aaai.v35i12.17281 | Image-to-Image Retrieval by Learning Similarity between Scene Graphs | https://ojs.aaai.org/index.php/AAAI/article/view/17281 | https://ojs.aaai.org/index.php/AAAI/article/download/17281/17088 | [
"Sangwoong Yoon",
"Woo Young Kang",
"Sungwook Jeon",
"SeongEun Lee",
"Changjin Han",
"Jonghun Park",
"Eun-Sol Kim"
] | As a scene graph compactly summarizes the high-level content of an image in a structured and symbolic manner, the similarity between scene graphs of two images reflects the relevance of their contents. Based on this idea, we propose a novel approach for image-to-image retrieval using scene graph similarity measured by ... | main | Machine Learning | 10.1609/aaai.v35i12.17281 | 35 | 12 | 10718-10726 | official | 2012.14700 | title_snapshot |
10.1609/aaai.v35i12.17282 | Learning Interpretable Models for Coupled Networks Under Domain Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/17282 | https://ojs.aaai.org/index.php/AAAI/article/download/17282/17089 | [
"Hongyuan You",
"Sikun Lin",
"Ambuj Singh"
] | Modeling the behavior of coupled networks is challenging due to their intricate dynamics. For example in neuroscience, it is of critical importance to understand the relationship between the functional neural processes and the anatomical connectivities. Modern neuroimaging techniques allow us to separately measure func... | main | Machine Learning | 10.1609/aaai.v35i12.17282 | 35 | 12 | 10727-10736 | official | 2104.09069 | title_snapshot |
10.1609/aaai.v35i12.17243 | Neural Architecture Search as Sparse Supernet | https://ojs.aaai.org/index.php/AAAI/article/view/17243 | https://ojs.aaai.org/index.php/AAAI/article/download/17243/17050 | [
"Yan Wu",
"Aoming Liu",
"Zhiwu Huang",
"Siwei Zhang",
"Luc Van Gool"
] | This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet ... | main | Machine Learning | 10.1609/aaai.v35i12.17243 | 35 | 12 | 10379-10387 | official | 2007.16112 | title_snapshot |
10.1609/aaai.v35i12.17244 | Learning to Purify Noisy Labels via Meta Soft Label Corrector | https://ojs.aaai.org/index.php/AAAI/article/view/17244 | https://ojs.aaai.org/index.php/AAAI/article/download/17244/17051 | [
"Yichen Wu",
"Jun Shu",
"Qi Xie",
"Qian Zhao",
"Deyu Meng"
] | Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by identifying suspected noisy labels and then correcting them. Current approaches to correcting corrupted labels usually need manually pre-defined label co... | main | Machine Learning | 10.1609/aaai.v35i12.17244 | 35 | 12 | 10388-10396 | official | 2008.00627 | title_snapshot |
10.1609/aaai.v35i12.17245 | Near-Optimal MNL Bandits Under Risk Criteria | https://ojs.aaai.org/index.php/AAAI/article/view/17245 | https://ojs.aaai.org/index.php/AAAI/article/download/17245/17052 | [
"Guangyu Xi",
"Chao Tao",
"Yuan Zhou"
] | We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria. Unlike the ordinary expected revenue, risk criteria are more general goals widely used in industries and business. We design algorithms for a broad class of risk criteria, including but not limited to the well-k... | main | Machine Learning | 10.1609/aaai.v35i12.17245 | 35 | 12 | 10397-10404 | official | 2009.12511 | title_snapshot |
10.1609/aaai.v35i12.17246 | Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17246 | https://ojs.aaai.org/index.php/AAAI/article/download/17246/17053 | [
"Wenhan Xian",
"Feihu Huang",
"Heng Huang"
] | Recently decentralized optimization attracts much attention in machine learning because it is more communication-efficient than the centralized fashion. Quantization is a promising method to reduce the communication cost via cutting down the budget of each single communication using the gradient compression. To further... | main | Machine Learning | 10.1609/aaai.v35i12.17246 | 35 | 12 | 10405-10413 | official | null | null |
10.1609/aaai.v35i12.17247 | Physics-constrained Automatic Feature Engineering for Predictive Modeling in Materials Science | https://ojs.aaai.org/index.php/AAAI/article/view/17247 | https://ojs.aaai.org/index.php/AAAI/article/download/17247/17054 | [
"Ziyu Xiang",
"Mingzhou Fan",
"Guillermo Vázquez Tovar",
"William Trehern",
"Byung-Jun Yoon",
"Xiaofeng Qian",
"Raymundo Arroyave",
"Xiaoning Qian"
] | Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks. Here, we develop AFE to extract dependency relationships that can be interpreted with functional formulas to discover physics meaning or new hypotheses for the problems of intere... | main | Machine Learning | 10.1609/aaai.v35i12.17247 | 35 | 12 | 10414-10421 | official | null | null |
10.1609/aaai.v35i12.17248 | Distant Transfer Learning via Deep Random Walk | https://ojs.aaai.org/index.php/AAAI/article/view/17248 | https://ojs.aaai.org/index.php/AAAI/article/download/17248/17055 | [
"Qiao Xiao",
"Yu Zhang"
] | Transfer learning, which is to improve the learning performance in the target domain by leveraging useful knowledge from the source domain, often requires that those two domains are very close, which limits its application scope. Recently, distant transfer learning has been studied to transfer knowledge between two dis... | main | Machine Learning | 10.1609/aaai.v35i12.17248 | 35 | 12 | 10422-10429 | official | 2006.07622 | title_snapshot |
10.1609/aaai.v35i12.17249 | Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation | https://ojs.aaai.org/index.php/AAAI/article/view/17249 | https://ojs.aaai.org/index.php/AAAI/article/download/17249/17056 | [
"Jianwen Xie",
"Zilong Zheng",
"Xiaolin Fang",
"Song-Chun Zhu",
"Ying Nian Wu"
] | This paper studies the unsupervised cross-domain translation problem by proposing a generative framework, in which the probability distribution of each domain is represented by a generative cooperative network that consists of an energy-based model and a latent variable model. The use of generative cooperative network ... | main | Machine Learning | 10.1609/aaai.v35i12.17249 | 35 | 12 | 10430-10440 | official | 2103.04285 | title_snapshot |
10.1609/aaai.v35i12.17250 | Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler | https://ojs.aaai.org/index.php/AAAI/article/view/17250 | https://ojs.aaai.org/index.php/AAAI/article/download/17250/17057 | [
"Jianwen Xie",
"Zilong Zheng",
"Ping Li"
] | Due to the intractable partition function, training energy-based models (EBMs) by maximum likelihood requires Markov chain Monte Carlo (MCMC) sampling to approximate the gradient of the Kullback-Leibler divergence between data and model distributions. However, it is non-trivial to sample from an EBM because of the diff... | main | Machine Learning | 10.1609/aaai.v35i12.17250 | 35 | 12 | 10441-10451 | official | 2012.14936 | title_snapshot |
10.1609/aaai.v35i12.17251 | Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation | https://ojs.aaai.org/index.php/AAAI/article/view/17251 | https://ojs.aaai.org/index.php/AAAI/article/download/17251/17058 | [
"Jinwei Xing",
"Takashi Nagata",
"Kexin Chen",
"Xinyun Zou",
"Emre Neftci",
"Jeffrey L. Krichmar"
] | Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real-world applicability of Deep RL, zero-shot policy transfer is still a challenging problem since even minor visual changes could make the tra... | main | Machine Learning | 10.1609/aaai.v35i12.17251 | 35 | 12 | 10452-10459 | official | 2102.05714 | title_snapshot |
10.1609/aaai.v35i12.17252 | Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling | https://ojs.aaai.org/index.php/AAAI/article/view/17252 | https://ojs.aaai.org/index.php/AAAI/article/download/17252/17059 | [
"Huaqing Xiong",
"Tengyu Xu",
"Yingbin Liang",
"Wei Zhang"
] | Despite the wide applications of Adam in reinforcement learning (RL), the theoretical convergence of Adam-type RL algorithms has not been established. This paper provides the first such convergence analysis for two fundamental RL algorithms of policy gradient (PG) and temporal difference (TD) learning that incorporate ... | main | Machine Learning | 10.1609/aaai.v35i12.17252 | 35 | 12 | 10460-10468 | official | 2002.06286 | title_snapshot |
10.1609/aaai.v35i12.17253 | Variational Disentanglement for Rare Event Modeling | https://ojs.aaai.org/index.php/AAAI/article/view/17253 | https://ojs.aaai.org/index.php/AAAI/article/download/17253/17060 | [
"Zidi Xiu",
"Chenyang Tao",
"Michael Gao",
"Connor Davis",
"Benjamin A. Goldstein",
"Ricardo Henao"
] | Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest... | main | Machine Learning | 10.1609/aaai.v35i12.17253 | 35 | 12 | 10469-10477 | official | 2009.08541 | title_snapshot |
10.1609/aaai.v35i12.17254 | Step-Ahead Error Feedback for Distributed Training with Compressed Gradient | https://ojs.aaai.org/index.php/AAAI/article/view/17254 | https://ojs.aaai.org/index.php/AAAI/article/download/17254/17061 | [
"An Xu",
"Zhouyuan Huo",
"Heng Huang"
] | Although the distributed machine learning methods can speed up the training of large deep neural networks, the communication cost has become the non-negligible bottleneck to constrain the performance. To address this challenge, the gradient compression based communication-efficient distributed learning methods were des... | main | Machine Learning | 10.1609/aaai.v35i12.17254 | 35 | 12 | 10478-10486 | official | 2008.05823 | title_snapshot |
10.1609/aaai.v35i12.17255 | Isolation Graph Kernel | https://ojs.aaai.org/index.php/AAAI/article/view/17255 | https://ojs.aaai.org/index.php/AAAI/article/download/17255/17062 | [
"Bi-Cun Xu",
"Kai Ming Ting",
"Yuan Jiang"
] | A recent Wasserstein Weisfeiler-Lehman (WWL) Graph Kernel has a distinctive feature: Representing the distribution of Weisfeiler-Lehman (WL)-embedded node vectors of a graph in a histogram that enables a dissimilarity measurement of two graphs using Wasserstein distance. It has been shown to produce better classificati... | main | Machine Learning | 10.1609/aaai.v35i12.17255 | 35 | 12 | 10487-10495 | official | null | null |
10.1609/aaai.v35i12.17256 | Multi-Task Recurrent Modular Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17256 | https://ojs.aaai.org/index.php/AAAI/article/download/17256/17063 | [
"Dongkuan Xu",
"Wei Cheng",
"Xin Dong",
"Bo Zong",
"Wenchao Yu",
"Jingchao Ni",
"Dongjin Song",
"Xuchao Zhang",
"Haifeng Chen",
"Xiang Zhang"
] | We consider the models of deep multi-task learning with recurrent architectures that exploit regularities across tasks to improve the performance of multiple sequence processing tasks jointly. Most existing architectures are painstakingly customized to learn task relationships for different problems, which is not flexi... | main | Machine Learning | 10.1609/aaai.v35i12.17256 | 35 | 12 | 10496-10504 | official | null | null |
10.1609/aaai.v35i12.17257 | Learning Graphons via Structured Gromov-Wasserstein Barycenters | https://ojs.aaai.org/index.php/AAAI/article/view/17257 | https://ojs.aaai.org/index.php/AAAI/article/download/17257/17064 | [
"Hongteng Xu",
"Dixin Luo",
"Lawrence Carin",
"Hongyuan Zha"
] | We propose a novel and principled method to learn a nonparametric graph model called graphon, which is defined in an infinite-dimensional space and represents arbitrary-size graphs. Based on the weak regularity lemma from the theory of graphons, we leverage a step function to approximate a graphon. We show that the cut... | main | Machine Learning | 10.1609/aaai.v35i12.17257 | 35 | 12 | 10505-10513 | official | 2012.05644 | title_snapshot |
10.1609/aaai.v35i12.17258 | Towards Generalized Implementation of Wasserstein Distance in GANs | https://ojs.aaai.org/index.php/AAAI/article/view/17258 | https://ojs.aaai.org/index.php/AAAI/article/download/17258/17065 | [
"Minkai Xu"
] | Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it does not always outperform other variants of GANs. This is mostly due to the imperfect implementation of the Lipschitz condition required by th... | main | Machine Learning | 10.1609/aaai.v35i12.17258 | 35 | 12 | 10514-10522 | official | 2012.03420 | title_snapshot |
10.1609/aaai.v35i12.17259 | Towards Feature Space Adversarial Attack by Style Perturbation | https://ojs.aaai.org/index.php/AAAI/article/view/17259 | https://ojs.aaai.org/index.php/AAAI/article/download/17259/17066 | [
"Qiuling Xu",
"Guanhong Tao",
"Siyuan Cheng",
"Xiangyu Zhang"
] | We propose a new adversarial attack to Deep Neural Networks for image classification. Different from most existing attacks that directly perturb input pixels, our attack focuses on perturbing abstract features, more specifically, features that denote styles, including interpretable styles such as vivid colors and sharp... | main | Machine Learning | 10.1609/aaai.v35i12.17259 | 35 | 12 | 10523-10531 | official | null | null |
10.1609/aaai.v35i12.17260 | MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records | https://ojs.aaai.org/index.php/AAAI/article/view/17260 | https://ojs.aaai.org/index.php/AAAI/article/download/17260/17067 | [
"Zhen Xu",
"David R. So",
"Andrew M. Dai"
] | One important challenge of applying deep learning to electronic health records (EHR) is the complexity of their multimodal structure. EHR usually contains a mixture of structured (codes) and unstructured (free-text) data with sparse and irregular longitudinal features -- all of which doctors utilize when making decisio... | main | Machine Learning | 10.1609/aaai.v35i12.17260 | 35 | 12 | 10532-10540 | official | 2102.02340 | title_snapshot |
10.1609/aaai.v35i12.17261 | Deep Frequency Principle Towards Understanding Why Deeper Learning Is Faster | https://ojs.aaai.org/index.php/AAAI/article/view/17261 | https://ojs.aaai.org/index.php/AAAI/article/download/17261/17068 | [
"Zhiqin John Xu",
"Hanxu Zhou"
] | Understanding the effect of depth in deep learning is a critical problem. In this work, we utilize the Fourier analysis to empirically provide a promising mechanism to understand why feedforward deeper learning is faster. To this end, we separate a deep neural network, trained by normal stochastic gradient descent, int... | main | Machine Learning | 10.1609/aaai.v35i12.17261 | 35 | 12 | 10541-10550 | official | 2007.14313 | title_snapshot |
10.1609/aaai.v35i12.17262 | Rethinking Bi-Level Optimization in Neural Architecture Search: A Gibbs Sampling Perspective | https://ojs.aaai.org/index.php/AAAI/article/view/17262 | https://ojs.aaai.org/index.php/AAAI/article/download/17262/17069 | [
"Chao Xue",
"Xiaoxing Wang",
"Junchi Yan",
"Yonggang Hu",
"Xiaokang Yang",
"Kewei Sun"
] | One-Shot architecture search, which aims to explore all possible operations jointly based on a single model, has been an active direction of Neural Architecture Search (NAS). As a well-known one-shot solution, Differentiable Architecture Search (DARTS) performs continuous relaxation on the architecture's importance and... | main | Machine Learning | 10.1609/aaai.v35i12.17262 | 35 | 12 | 10551-10559 | official | null | null |
10.1609/aaai.v35i12.17232 | Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases | https://ojs.aaai.org/index.php/AAAI/article/view/17232 | https://ojs.aaai.org/index.php/AAAI/article/download/17232/17039 | [
"Chris Wendler",
"Andisheh Amrollahi",
"Bastian Seifert",
"Andreas Krause",
"Markus Püschel"
] | Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain. In this work, we present a new family of algorithms for learning Fourier-sparse set functions. They requi... | main | Machine Learning | 10.1609/aaai.v35i12.17232 | 35 | 12 | 10283-10292 | official | 2010.00439 | title_snapshot |
10.1609/aaai.v35i12.17233 | BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search | https://ojs.aaai.org/index.php/AAAI/article/view/17233 | https://ojs.aaai.org/index.php/AAAI/article/download/17233/17040 | [
"Colin White",
"Willie Neiswanger",
"Yash Savani"
] | Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), which has long had success in hyperparameter optimization, has recently emerged as a very promising strategy for NAS when it is coupled with a neural predictor. Recent work has proposed differe... | main | Machine Learning | 10.1609/aaai.v35i12.17233 | 35 | 12 | 10293-10301 | official | 1910.11858 | title_snapshot |
10.1609/aaai.v35i12.17234 | Peer Collaborative Learning for Online Knowledge Distillation | https://ojs.aaai.org/index.php/AAAI/article/view/17234 | https://ojs.aaai.org/index.php/AAAI/article/download/17234/17041 | [
"Guile Wu",
"Shaogang Gong"
] | Traditional knowledge distillation uses a two-stage training strategy to transfer knowledge from a high-capacity teacher model to a compact student model, which relies heavily on the pre-trained teacher. Recent online knowledge distillation alleviates this limitation by collaborative learning, mutual learning and onlin... | main | Machine Learning | 10.1609/aaai.v35i12.17234 | 35 | 12 | 10302-10310 | official | 2006.04147 | title_snapshot |
10.1609/aaai.v35i12.17235 | Self-Supervised Attention-Aware Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17235 | https://ojs.aaai.org/index.php/AAAI/article/download/17235/17042 | [
"Haiping Wu",
"Khimya Khetarpal",
"Doina Precup"
] | Visual saliency has emerged as a major visualization tool for interpreting deep reinforcement learning (RL) agents. However, much of the existing research uses it as an analyzing tool rather than an inductive bias for policy learning. In this work, we use visual attention as an inductive bias for RL agents. We propose ... | main | Machine Learning | 10.1609/aaai.v35i12.17235 | 35 | 12 | 10311-10319 | official | null | null |
10.1609/aaai.v35i12.17236 | Training Spiking Neural Networks with Accumulated Spiking Flow | https://ojs.aaai.org/index.php/AAAI/article/view/17236 | https://ojs.aaai.org/index.php/AAAI/article/download/17236/17043 | [
"Hao Wu",
"Yueyi Zhang",
"Wenming Weng",
"Yongting Zhang",
"Zhiwei Xiong",
"Zheng-Jun Zha",
"Xiaoyan Sun",
"Feng Wu"
] | The fast development of neuromorphic hardwares promotes Spiking Neural Networks (SNNs) to a thrilling research avenue. Current SNNs, though much efficient, are less effective compared with leading Artificial Neural Networks (ANNs) especially in supervised learning tasks. Recent efforts further demonstrate the potential... | main | Machine Learning | 10.1609/aaai.v35i12.17236 | 35 | 12 | 10320-10328 | official | null | null |
10.1609/aaai.v35i12.17237 | Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients | https://ojs.aaai.org/index.php/AAAI/article/view/17237 | https://ojs.aaai.org/index.php/AAAI/article/download/17237/17044 | [
"Huimin Wu",
"Zhengmian Hu",
"Bin Gu"
] | Adversarial attacks by generating examples which are almost indistinguishable from natural examples, pose a serious threat to learning models. Defending against adversarial attacks is a critical element for a reliable learning system. Support vector machine (SVM) is a classical yet still important learning algorithm ev... | main | Machine Learning | 10.1609/aaai.v35i12.17237 | 35 | 12 | 10329-10337 | official | 2107.09937 | title_snapshot |
10.1609/aaai.v35i12.17238 | Fine-grained Generalization Analysis of Vector-Valued Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17238 | https://ojs.aaai.org/index.php/AAAI/article/download/17238/17045 | [
"Liang Wu",
"Antoine Ledent",
"Yunwen Lei",
"Marius Kloft"
] | Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on different specific algorithms under the empirical risk minimization principle, a unifying analysis ... | main | Machine Learning | 10.1609/aaai.v35i12.17238 | 35 | 12 | 10338-10346 | official | 2104.14173 | title_snapshot |
10.1609/aaai.v35i12.17239 | Frugal Optimization for Cost-related Hyperparameters | https://ojs.aaai.org/index.php/AAAI/article/view/17239 | https://ojs.aaai.org/index.php/AAAI/article/download/17239/17046 | [
"Qingyun Wu",
"Chi Wang",
"Silu Huang"
] | The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost. But this effect is largely ignored in existing HPO methods, which are incap... | main | Machine Learning | 10.1609/aaai.v35i12.17239 | 35 | 12 | 10347-10354 | official | 2005.01571 | title_snapshot |
10.1609/aaai.v35i12.17240 | Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models | https://ojs.aaai.org/index.php/AAAI/article/view/17240 | https://ojs.aaai.org/index.php/AAAI/article/download/17240/17047 | [
"Ruiyuan Wu",
"Anna Scaglione",
"Hoi-To Wai",
"Nurullah Karakoc",
"Kari Hreinsson",
"Wing-Kin Ma"
] | In federated learning, models are learned from users’ data that are held private in their edge devices, by aggregating them in the service provider’s “cloud” to obtain a global model. Such global model is of great commercial value in, e.g., improving the customers’ experience. In this paper we focus on two possible are... | main | Machine Learning | 10.1609/aaai.v35i12.17240 | 35 | 12 | 10355-10362 | official | 2012.13900 | title_snapshot |
10.1609/aaai.v35i12.17241 | Curriculum-Meta Learning for Order-Robust Continual Relation Extraction | https://ojs.aaai.org/index.php/AAAI/article/view/17241 | https://ojs.aaai.org/index.php/AAAI/article/download/17241/17048 | [
"Tongtong Wu",
"Xuekai Li",
"Yuan-Fang Li",
"Gholamreza Haffari",
"Guilin Qi",
"Yujin Zhu",
"Guoqiang Xu"
] | Continual relation extraction is an important task that focuses on extracting new facts incrementally from unstructured text. Given the sequential arrival order of the relations, this task is prone to two serious challenges, namely catastrophic forgetting and order-sensitivity. We propose a novel curriculum-meta learni... | main | Machine Learning | 10.1609/aaai.v35i12.17241 | 35 | 12 | 10363-10369 | official | 2101.01926 | title_snapshot |
10.1609/aaai.v35i12.17242 | Fractal Autoencoders for Feature Selection | https://ojs.aaai.org/index.php/AAAI/article/view/17242 | https://ojs.aaai.org/index.php/AAAI/article/download/17242/17049 | [
"Xinxing Wu",
"Qiang Cheng"
] | Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE). It trains a neural network to pinpoint informative features for global exploring of repr... | main | Machine Learning | 10.1609/aaai.v35i12.17242 | 35 | 12 | 10370-10378 | official | 2010.09430 | title_snapshot |
10.1609/aaai.v35i13.17348 | Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition | https://ojs.aaai.org/index.php/AAAI/article/view/17348 | https://ojs.aaai.org/index.php/AAAI/article/download/17348/17155 | [
"Thomy Phan",
"Lenz Belzner",
"Thomas Gabor",
"Andreas Sedlmeier",
"Fabian Ritz",
"Claudia Linnhoff-Popien"
] | We focus on resilience in cooperative multi-agent systems, where agents can change their behavior due to udpates or failures of hardware and software components. Current state-of-the-art approaches to cooperative multi-agent reinforcement learning (MARL) have either focused on idealized settings without any changes or ... | main | Multiagent Systems | 10.1609/aaai.v35i13.17348 | 35 | 13 | 11308-11316 | official | null | null |
10.1609/aaai.v35i13.17357 | Coordination Between Individual Agents in Multi-Agent Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17357 | https://ojs.aaai.org/index.php/AAAI/article/download/17357/17164 | [
"Yang Zhang",
"Qingyu Yang",
"Dou An",
"Chengwei Zhang"
] | The existing multi-agent reinforcement learning methods (MARL) for determining the coordination between agents focus on either global-level or neighborhood-level coordination between agents. However the problem of coordination between individual agents is remain to be solved. It is crucial for learning an optimal coord... | main | Multiagent Systems | 10.1609/aaai.v35i13.17357 | 35 | 13 | 11387-11394 | official | null | null |
10.1609/aaai.v35i13.17356 | Efficient Querying for Cooperative Probabilistic Commitments | https://ojs.aaai.org/index.php/AAAI/article/view/17356 | https://ojs.aaai.org/index.php/AAAI/article/download/17356/17163 | [
"Qi Zhang",
"Edmund H. Durfee",
"Satinder Singh"
] | Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. Agents whose objectives are aligned, and where one agent can help another achieve greater reward by sacrificing some of its own reward, should choose a cooperative c... | main | Multiagent Systems | 10.1609/aaai.v35i13.17356 | 35 | 13 | 11378-11386 | official | 2012.07195 | title_snapshot |
10.1609/aaai.v35i13.17355 | Maintenance of Social Commitments in Multiagent Systems | https://ojs.aaai.org/index.php/AAAI/article/view/17355 | https://ojs.aaai.org/index.php/AAAI/article/download/17355/17162 | [
"Pankaj Telang",
"Munindar P. Singh",
"Neil Yorke-Smith"
] | We introduce and formalize a concept of a maintenance commitment, a kind of social commitment characterized by states whose truthhood an agent commits to maintain. This concept of maintenance commitments enables us to capture a richer variety of real-world scenarios than possible using achievement commitments with a te... | main | Multiagent Systems | 10.1609/aaai.v35i13.17355 | 35 | 13 | 11369-11377 | official | null | null |
10.1609/aaai.v35i13.17354 | Contract-based Inter-user Usage Coordination in Free-floating Car Sharing | https://ojs.aaai.org/index.php/AAAI/article/view/17354 | https://ojs.aaai.org/index.php/AAAI/article/download/17354/17161 | [
"Kentaro Takahira",
"Shigeo Matsubara"
] | We propose a novel distributed user-car matching method based on a contract between users to mitigate the imbalance problem between vehicle distribution and demand in free-floating car sharing. Previous regulation methods involved an incentive system based on the predictions of origin-destination (OD) demand obtained f... | main | Multiagent Systems | 10.1609/aaai.v35i13.17354 | 35 | 13 | 11361-11368 | official | null | null |
10.1609/aaai.v35i13.17353 | Value-Decomposition Multi-Agent Actor-Critics | https://ojs.aaai.org/index.php/AAAI/article/view/17353 | https://ojs.aaai.org/index.php/AAAI/article/download/17353/17160 | [
"Jianyu Su",
"Stephen Adams",
"Peter Beling"
] | The exploitation of extra state information has been an active research area in multi-agent reinforcement learning (MARL). QMIX represents the joint action-value using a non-negative function approximator and achieves the best performance on the StarCraft II micromanagement testbed, a common MARL benchmark. However, ou... | main | Multiagent Systems | 10.1609/aaai.v35i13.17353 | 35 | 13 | 11352-11360 | official | 2007.12306 | title_snapshot |
10.1609/aaai.v35i13.17352 | Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games | https://ojs.aaai.org/index.php/AAAI/article/view/17352 | https://ojs.aaai.org/index.php/AAAI/article/download/17352/17159 | [
"Stratis Skoulakis",
"Tanner Fiez",
"Ryann Sim",
"Georgios Piliouras",
"Lillian Ratliff"
] | The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game. In this paper, we move away from the artificial divide between dynamic agents and static games, to introduce a... | main | Multiagent Systems | 10.1609/aaai.v35i13.17352 | 35 | 13 | 11343-11351 | official | 2012.08382 | title_snapshot |
10.1609/aaai.v35i13.17351 | Synchronous Dynamical Systems on Directed Acyclic Graphs: Complexity and Algorithms | https://ojs.aaai.org/index.php/AAAI/article/view/17351 | https://ojs.aaai.org/index.php/AAAI/article/download/17351/17158 | [
"Daniel J. Rosenkrantz",
"Madhav Marathe",
"S. S. Ravi",
"Richard E. Stearns"
] | Discrete dynamical systems serve as useful formal models to study diffusion phenomena in social networks. Motivated by applications in systems biology, several recent papers have studied algorithmic and complexity aspects of diffusion problems for dynamical systems whose underlying graphs are directed, and may contain ... | main | Multiagent Systems | 10.1609/aaai.v35i13.17351 | 35 | 13 | 11334-11342 | official | null | null |
10.1609/aaai.v35i13.17350 | Newton Optimization on Helmholtz Decomposition for Continuous Games | https://ojs.aaai.org/index.php/AAAI/article/view/17350 | https://ojs.aaai.org/index.php/AAAI/article/download/17350/17157 | [
"Giorgia Ramponi",
"Marcello Restelli"
] | Many learning problems involve multiple agents optimizing different interactive functions. In these problems, the standard policy gradient algorithms fail due to the non-stationarity of the setting and the different interests of each agent. In fact, algorithms must take into account the complex dynamics of these system... | main | Multiagent Systems | 10.1609/aaai.v35i13.17350 | 35 | 13 | 11325-11333 | official | 2007.07804 | title_snapshot |
10.1609/aaai.v35i13.17349 | Anytime Heuristic and Monte Carlo Methods for Large-Scale Simultaneous Coalition Structure Generation and Assignment | https://ojs.aaai.org/index.php/AAAI/article/view/17349 | https://ojs.aaai.org/index.php/AAAI/article/download/17349/17156 | [
"Fredrik Präntare",
"Herman Appelgren",
"Fredrik Heintz"
] | Optimal simultaneous coalition structure generation and assignment is computationally hard. The state-of-the-art can only compute solutions to problems with severely limited input sizes, and no effective approximation algorithms that are guaranteed to yield high-quality solutions are expected to exist. Real-world optim... | main | Multiagent Systems | 10.1609/aaai.v35i13.17349 | 35 | 13 | 11317-11324 | official | null | null |
10.1609/aaai.v35i13.17347 | Time-Independent Planning for Multiple Moving Agents | https://ojs.aaai.org/index.php/AAAI/article/view/17347 | https://ojs.aaai.org/index.php/AAAI/article/download/17347/17154 | [
"Keisuke Okumura",
"Yasumasa Tamura",
"Xavier Défago"
] | Typical Multi-agent Path Finding (MAPF) solvers assume that agents move synchronously, thus neglecting the reality gap in timing assumptions, e.g., delays caused by an imperfect execution of asynchronous moves. So far, two policies enforce a robust execution of MAPF plans taken as input: either by forcing agents to syn... | main | Multiagent Systems | 10.1609/aaai.v35i13.17347 | 35 | 13 | 11299-11307 | official | 2005.13187 | title_snapshot |
10.1609/aaai.v35i13.17346 | Expected Value of Communication for Planning in Ad Hoc Teamwork | https://ojs.aaai.org/index.php/AAAI/article/view/17346 | https://ojs.aaai.org/index.php/AAAI/article/download/17346/17153 | [
"William Macke",
"Reuth Mirsky",
"Peter Stone"
] | A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as “ad hoc teamwork”, enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current p... | main | Multiagent Systems | 10.1609/aaai.v35i13.17346 | 35 | 13 | 11290-11298 | official | 2103.01171 | title_snapshot |
10.1609/aaai.v35i13.17345 | Dec-SGTS: Decentralized Sub-Goal Tree Search for Multi-Agent Coordination | https://ojs.aaai.org/index.php/AAAI/article/view/17345 | https://ojs.aaai.org/index.php/AAAI/article/download/17345/17152 | [
"Minglong Li",
"Zhongxuan Cai",
"Wenjing Yang",
"Lixia Wu",
"Yinghui Xu",
"Ji Wang"
] | Multi-agent coordination tends to benefit from efficient communication, where cooperation often happens based on exchanging information about what the agents intend to do, i.e. intention sharing. It becomes a key problem to model the intention by some proper abstraction. Currently, it is either too coarse such as final... | main | Multiagent Systems | 10.1609/aaai.v35i13.17345 | 35 | 13 | 11282-11289 | official | null | null |
10.1609/aaai.v35i13.17344 | Lifelong Multi-Agent Path Finding in Large-Scale Warehouses | https://ojs.aaai.org/index.php/AAAI/article/view/17344 | https://ojs.aaai.org/index.php/AAAI/article/download/17344/17151 | [
"Jiaoyang Li",
"Andrew Tinka",
"Scott Kiesel",
"Joseph W. Durham",
"T. K. Satish Kumar",
"Sven Koenig"
] | Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where agents are constantly engaged with new goal locations, such as in large-scale automated warehouses. We propose a new framework Rolling-Horizon... | main | Multiagent Systems | 10.1609/aaai.v35i13.17344 | 35 | 13 | 11272-11281 | official | 2005.07371 | title_snapshot |
10.1609/aaai.v35i13.17343 | Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory | https://ojs.aaai.org/index.php/AAAI/article/view/17343 | https://ojs.aaai.org/index.php/AAAI/article/download/17343/17150 | [
"Stefanos Leonardos",
"Georgios Piliouras"
] | Exploration-exploitation is a powerful and practical tool in multi-agent learning (MAL), however, its effects are far from understood. To make progress in this direction, we study a smooth analogue of Q-learning. We start by showing that our learning model has strong theoretical justification as an optimal model for st... | main | Multiagent Systems | 10.1609/aaai.v35i13.17343 | 35 | 13 | 11263-11271 | official | 2012.03083 | title_snapshot |
10.1609/aaai.v35i13.17338 | Improving Continuous-time Conflict Based Search | https://ojs.aaai.org/index.php/AAAI/article/view/17338 | https://ojs.aaai.org/index.php/AAAI/article/download/17338/17145 | [
"Anton Andreychuk",
"Konstantin Yakovlev",
"Eli Boyarski",
"Roni Stern"
] | Conflict-Based Search (CBS) is a powerful algorithmic framework for optimally solving classical multi-agent path finding (MAPF) problems, where time is discretized into the time steps. Continuous-time CBS (CCBS) is a recently proposed version of CBS that guarantees optimal solutions without the need to discretize time.... | main | Multiagent Systems | 10.1609/aaai.v35i13.17338 | 35 | 13 | 11220-11227 | official | 2101.09723 | title_snapshot |
10.1609/aaai.v35i13.17342 | The Influence of Memory in Multi-Agent Consensus | https://ojs.aaai.org/index.php/AAAI/article/view/17342 | https://ojs.aaai.org/index.php/AAAI/article/download/17342/17149 | [
"David Kohan Marzagão",
"Luciana Basualdo Bonatto",
"Tiago Madeira",
"Marcelo Matheus Gauy",
"Peter McBurney"
] | Multi-agent consensus problems can often be seen as a sequence of autonomous and independent local choices between a finite set of decision options, with each local choice undertaken simultaneously, and with a shared goal of achieving a global consensus state. Being able to estimate probabilities for the different outc... | main | Multiagent Systems | 10.1609/aaai.v35i13.17342 | 35 | 13 | 11254-11262 | official | 2105.04666 | title_snapshot |
10.1609/aaai.v35i13.17341 | Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search | https://ojs.aaai.org/index.php/AAAI/article/view/17341 | https://ojs.aaai.org/index.php/AAAI/article/download/17341/17148 | [
"Taoan Huang",
"Sven Koenig",
"Bistra Dilkina"
] | Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. On the high level, CBS repeatedly detects conflicts and resolves one of them by splitting the current problem into two subproblems. Previous work chooses the conflict to resolve by categorizing conflicts into three classes and alw... | main | Multiagent Systems | 10.1609/aaai.v35i13.17341 | 35 | 13 | 11246-11253 | official | 2012.06005 | title_snapshot |
10.1609/aaai.v35i13.17340 | Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances | https://ojs.aaai.org/index.php/AAAI/article/view/17340 | https://ojs.aaai.org/index.php/AAAI/article/download/17340/17147 | [
"Jingkai Chen",
"Jiaoyang Li",
"Chuchu Fan",
"Brian C. Williams"
] | We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances. We... | main | Multiagent Systems | 10.1609/aaai.v35i13.17340 | 35 | 13 | 11237-11245 | official | 2012.09052 | title_snapshot |
10.1609/aaai.v35i13.17339 | Inference-Based Deterministic Messaging For Multi-Agent Communication | https://ojs.aaai.org/index.php/AAAI/article/view/17339 | https://ojs.aaai.org/index.php/AAAI/article/download/17339/17146 | [
"Varun Bhatt",
"Michael Buro"
] | Communication is essential for coordination among humans and animals. Therefore, with the introduction of intelligent agents into the world, agent-to-agent and agent-to-human communication becomes necessary. In this paper, we first study learning in matrix-based signaling games to empirically show that decentralized me... | main | Multiagent Systems | 10.1609/aaai.v35i13.17339 | 35 | 13 | 11228-11236 | official | 2103.02150 | title_snapshot |
10.1609/aaai.v35i13.17391 | Decision-Guided Weighted Automata Extraction from Recurrent Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17391 | https://ojs.aaai.org/index.php/AAAI/article/download/17391/17198 | [
"Xiyue Zhang",
"Xiaoning Du",
"Xiaofei Xie",
"Lei Ma",
"Yang Liu",
"Meng Sun"
] | Recurrent Neural Networks (RNNs) have demonstrated their effectiveness in learning and processing sequential data (e.g., speech and natural language). However, due to the black-box nature of neural networks, understanding the decision logic of RNNs is quite challenging. Some recent progress has been made to approximate... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17391 | 35 | 13 | 11699-11707 | official | null | null |
10.1609/aaai.v35i13.17383 | Fair Influence Maximization: a Welfare Optimization Approach | https://ojs.aaai.org/index.php/AAAI/article/view/17383 | https://ojs.aaai.org/index.php/AAAI/article/download/17383/17190 | [
"Aida Rahmattalabi",
"Shahin Jabbari",
"Himabindu Lakkaraju",
"Phebe Vayanos",
"Max Izenberg",
"Ryan Brown",
"Eric Rice",
"Milind Tambe"
] | Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed to aid with the choice of ``peer leaders'' or ``infl... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17383 | 35 | 13 | 11630-11638 | official | 2006.07906 | title_snapshot |
10.1609/aaai.v35i13.17384 | Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation | https://ojs.aaai.org/index.php/AAAI/article/view/17384 | https://ojs.aaai.org/index.php/AAAI/article/download/17384/17191 | [
"Sam Sattarzadeh",
"Mahesh Sudhakar",
"Anthony Lem",
"Shervin Mehryar",
"Konstantinos N Plataniotis",
"Jongseong Jang",
"Hyunwoo Kim",
"Yeonjeong Jeong",
"Sangmin Lee",
"Kyunghoon Bae"
] | As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularit... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17384 | 35 | 13 | 11639-11647 | official | 2010.00672 | title_snapshot |
10.1609/aaai.v35i13.17385 | Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption | https://ojs.aaai.org/index.php/AAAI/article/view/17385 | https://ojs.aaai.org/index.php/AAAI/article/download/17385/17192 | [
"Xu Sun",
"Zhiyuan Zhang",
"Xuancheng Ren",
"Ruixuan Luo",
"Liangyou Li"
] | We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter. In this work, we propose an indicator to measure the robustness of neural network parameters by exploiting their vulnerability vi... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17385 | 35 | 13 | 11648-11656 | official | 2006.05620 | title_snapshot |
10.1609/aaai.v35i13.17386 | Ethically Compliant Sequential Decision Making | https://ojs.aaai.org/index.php/AAAI/article/view/17386 | https://ojs.aaai.org/index.php/AAAI/article/download/17386/17193 | [
"Justin Svegliato",
"Samer B. Nashed",
"Shlomo Zilberstein"
] | Enabling autonomous systems to comply with an ethical theory is critical given their accelerating deployment in domains that impact society. While many ethical theories have been studied extensively in moral philosophy, they are still challenging to implement by developers who build autonomous systems. This paper propo... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17386 | 35 | 13 | 11657-11665 | official | null | null |
10.1609/aaai.v35i13.17387 | Improving Robustness to Model Inversion Attacks via Mutual Information Regularization | https://ojs.aaai.org/index.php/AAAI/article/view/17387 | https://ojs.aaai.org/index.php/AAAI/article/download/17387/17194 | [
"Tianhao Wang",
"Yuheng Zhang",
"Ruoxi Jia"
] | This paper studies defense mechanisms against model inversion (MI) attacks -- a type of privacy attacks aimed at inferring information about the training data distribution given the access to a target machine learning model. Existing defense mechanisms rely on model-specific heuristics or noise injection. While being a... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17387 | 35 | 13 | 11666-11673 | official | 2009.05241 | title_snapshot |
10.1609/aaai.v35i13.17388 | Tightening Robustness Verification of Convolutional Neural Networks with Fine-Grained Linear Approximation | https://ojs.aaai.org/index.php/AAAI/article/view/17388 | https://ojs.aaai.org/index.php/AAAI/article/download/17388/17195 | [
"Yiting Wu",
"Min Zhang"
] | The robustness of neural networks can be quantitatively indicated by a lower bound within which any perturbation does not alter the original input’s classification result. A certified lower bound is also a criterion to evaluate the performance of robustness verification approaches. In this paper, we present a tighter l... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17388 | 35 | 13 | 11674-11681 | official | null | null |
10.1609/aaai.v35i13.17389 | Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors | https://ojs.aaai.org/index.php/AAAI/article/view/17389 | https://ojs.aaai.org/index.php/AAAI/article/download/17389/17196 | [
"Ruihan Zhang",
"Prashan Madumal",
"Tim Miller",
"Krista A. Ehinger",
"Benjamin I. P. Rubinstein"
] | Convolutional neural network (CNN) models for computer vision are powerful but lack explainability in their most basic form. This deficiency remains a key challenge when applying CNNs in important domains. Recent work on explanations through feature importance of approximate linear models has moved from input-level fea... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17389 | 35 | 13 | 11682-11690 | official | 2006.15417 | title_snapshot |
10.1609/aaai.v35i13.17390 | i-Algebra: Towards Interactive Interpretability of Deep Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17390 | https://ojs.aaai.org/index.php/AAAI/article/download/17390/17197 | [
"Xinyang Zhang",
"Ren Pang",
"Shouling Ji",
"Fenglong Ma",
"Ting Wang"
] | Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite. Despite the plethora of work on interpreting DNNs, most existing solutions offer interpretability in an ad hoc, one-shot, and static manner, without accounti... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17390 | 35 | 13 | 11691-11698 | official | 2101.09301 | title_snapshot |
10.1609/aaai.v35i13.17375 | Differentially Private Clustering via Maximum Coverage | https://ojs.aaai.org/index.php/AAAI/article/view/17375 | https://ojs.aaai.org/index.php/AAAI/article/download/17375/17182 | [
"Matthew Jones",
"Huy L. Nguyen",
"Thy D Nguyen"
] | This paper studies the problem of clustering in metric spaces while preserving the privacy of individual data. Specifically, we examine differentially private variants of the k-medians and Euclidean k-means problems. We present polynomial algorithms with constant multiplicative error and lower additive error than the p... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17375 | 35 | 13 | 11555-11563 | official | 2008.12388 | title_snapshot |
10.1609/aaai.v35i13.17382 | Comprehension and Knowledge | https://ojs.aaai.org/index.php/AAAI/article/view/17382 | https://ojs.aaai.org/index.php/AAAI/article/download/17382/17189 | [
"Pavel Naumov",
"Kevin Ros"
] | The ability of an agent to comprehend a sentence is tightly connected to the agent's prior experiences and background knowledge. The paper suggests to interpret comprehension as a modality and proposes a complete bimodal logical system that describes an interplay between comprehension and knowledge modalities. | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17382 | 35 | 13 | 11622-11629 | official | 2012.06561 | title_snapshot |
10.1609/aaai.v35i13.17381 | Ethical Dilemmas in Strategic Games | https://ojs.aaai.org/index.php/AAAI/article/view/17381 | https://ojs.aaai.org/index.php/AAAI/article/download/17381/17188 | [
"Pavel Naumov",
"Rui-Jie Yew"
] | An agent, or a coalition of agents, faces an ethical dilemma between several statements if she is forced to make a conscious choice between which of these statements will be true. This paper proposes to capture ethical dilemmas as a modality in strategic game settings with and without limit on sacrifice and for perfect... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17381 | 35 | 13 | 11613-11621 | official | 1911.00786 | title_snapshot |
10.1609/aaai.v35i13.17380 | Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations | https://ojs.aaai.org/index.php/AAAI/article/view/17380 | https://ojs.aaai.org/index.php/AAAI/article/download/17380/17187 | [
"Woo-Jeoung Nam",
"Jaesik Choi",
"Seong-Whan Lee"
] | The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and nonlinear transformations along deep hierarchies. In this paper, we propose a new attribution method, Relative Sectional Propagation (RSP), for fully decomposing the output predictions with the characteristics of class-... | main | Philosophy and Ethics of AI | 10.1609/aaai.v35i13.17380 | 35 | 13 | 11604-11612 | official | 2012.03434 | title_snapshot |
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