Asian Conference on Machine Learning最新文献_第2页

Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning 协同多智能体强化学习中的实用沟通策略学习

Asian Conference on Machine Learning Pub Date : 2022-09-02 DOI: 10.48550/arXiv.2209.01288

Diyi Hu, Chi Zhang, V. Prasanna, Bhaskar, Krishnamachari

引用次数: 0

Sliced Wasserstein Variational Inference 切片Wasserstein变分推理

Asian Conference on Machine Learning Pub Date : 2022-07-26 DOI: 10.48550/arXiv.2207.13177

Mingxuan Yi, Song Liu

引用次数: 10

Domain Alignment Meets Fully Test-Time Adaptation 域对齐满足完全的测试时间适应

Asian Conference on Machine Learning Pub Date : 2022-07-09 DOI: 10.48550/arXiv.2207.04185

Kowshik Thopalli, P. Turaga, Jayaraman J. Thiagarajan

{"title":"Domain Alignment Meets Fully Test-Time Adaptation","authors":"Kowshik Thopalli, P. Turaga, Jayaraman J. Thiagarajan","doi":"10.48550/arXiv.2207.04185","DOIUrl":"https://doi.org/10.48550/arXiv.2207.04185","url":null,"abstract":"A foundational requirement of a deployed ML model is to generalize to data drawn from a testing distribution that is different from training. A popular solution to this problem is to adapt a pre-trained model to novel domains using only unlabeled data. In this paper, we focus on a challenging variant of this problem, where access to the original source data is restricted. While fully test-time adaptation (FTTA) and unsupervised domain adaptation (UDA) are closely related, the advances in UDA are not readily applicable to TTA, since most UDA methods require access to the source data. Hence, we propose a new approach, CATTAn, that bridges UDA and FTTA, by relaxing the need to access entire source data, through a novel deep subspace alignment strategy. With a minimal overhead of storing the subspace basis set for the source data, CATTAn enables unsupervised alignment between source and target data during adaptation. Through extensive experimental evaluation on multiple 2D and 3D vision benchmarks (ImageNet-C, Office-31, OfficeHome, DomainNet, PointDA-10) and model architectures, we demonstrate significant gains in FTTA performance. Furthermore, we make a number of crucial findings on the utility of the alignment objective even with inherently robust models, pre-trained ViT representations and under low sample availability in the target domain.","PeriodicalId":119756,"journal":{"name":"Asian Conference on Machine Learning","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123102536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE AS-IntroVAE:对抗性相似距离产生稳健的IntroVAE

Asian Conference on Machine Learning Pub Date : 2022-06-28 DOI: 10.48550/arXiv.2206.13903

Chang-Tien Lu, Shen Zheng, Zirui Wang, O. Dib, Gaurav Gupta

{"title":"AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE","authors":"Chang-Tien Lu, Shen Zheng, Zirui Wang, O. Dib, Gaurav Gupta","doi":"10.48550/arXiv.2206.13903","DOIUrl":"https://doi.org/10.48550/arXiv.2206.13903","url":null,"abstract":"Recently, introspective models like IntroVAE and S-IntroVAE have excelled in image generation and reconstruction tasks. The principal characteristic of introspective models is the adversarial learning of VAE, where the encoder attempts to distinguish between the real and the fake (i.e., synthesized) images. However, due to the unavailability of an effective metric to evaluate the difference between the real and the fake images, the posterior collapse and the vanishing gradient problem still exist, reducing the fidelity of the synthesized images. In this paper, we propose a new variation of IntroVAE called Adversarial Similarity Distance Introspective Variational Autoencoder (AS-IntroVAE). We theoretically analyze the vanishing gradient problem and construct a new Adversarial Similarity Distance (AS-Distance) using the 2-Wasserstein distance and the kernel trick. With weight annealing on AS-Distance and KL-Divergence, the AS-IntroVAE are able to generate stable and high-quality images. The posterior collapse problem is addressed by making per-batch attempts to transform the image so that it better fits the prior distribution in the latent space. Compared with the per-image approach, this strategy fosters more diverse distributions in the latent space, allowing our model to produce images of great diversity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of AS-IntroVAE on image generation and reconstruction tasks.","PeriodicalId":119756,"journal":{"name":"Asian Conference on Machine Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126557518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

FLVoogd: Robust And Privacy Preserving Federated Learning FLVoogd:鲁棒和隐私保护联邦学习

Asian Conference on Machine Learning Pub Date : 2022-06-24 DOI: 10.48550/arXiv.2207.00428

Yuhang Tian, Rui Wang, Yan Qiao, E. Panaousis, K. Liang

引用次数: 2

An online semi-definite programming with a generalised log-determinant regularizer and its applications 具有广义对数行列式正则化器的在线半确定规划及其应用

Asian Conference on Machine Learning Pub Date : 2022-03-25 DOI: 10.3390/math10071055

Yaxiong Liu, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto

{"title":"An online semi-definite programming with a generalised log-determinant regularizer and its applications","authors":"Yaxiong Liu, Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto","doi":"10.3390/math10071055","DOIUrl":"https://doi.org/10.3390/math10071055","url":null,"abstract":"We consider a variant of the online semi-definite programming problem (OSDP). Specifically, in our problem, the setting of the decision space is a set of positive semi-definite matrices constrained by two norms in parallel: the L∞ norm to the diagonal entries and the Γ-trace norm, which is a generalized trace norm with a positive definite matrix Γ. Our setting recovers the original one when Γ is an identity matrix. To solve this problem, we design a follow-the-regularized-leader algorithm with a Γ-dependent regularizer, which also generalizes the log-determinant function. Next, we focus on online binary matrix completion (OBMC) with side information and online similarity prediction with side information. By reducing to the OSDP framework and applying our proposed algorithm, we remove the logarithmic factors in the previous mistake bound of the above two problems. In particular, for OBMC, our bound is optimal. Furthermore, our result implies a better offline generalization bound for the algorithm, which is similar to those of SVMs with the best kernel, if the side information is involved in advance.","PeriodicalId":119756,"journal":{"name":"Asian Conference on Machine Learning","volume":"04 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127411502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Detecting Accounting Frauds in Publicly Traded U.S. Firms: A Machine Learning Approach 美国上市公司的会计欺诈检测:机器学习方法

Asian Conference on Machine Learning Pub Date : 2020-03-01 DOI: 10.2139/SSRN.2670703

Bin Li, Julia Yu, Jie Zhang, B. Ke

引用次数: 17

Active Change-Point Detection 主动变更点检测

Asian Conference on Machine Learning Pub Date : 2019-10-15 DOI: 10.1527/tjsai.35-5_e-ja10

S. Hayashi, Yoshinobu Kawahara, H. Kashima

引用次数: 4

Inverse Visual Question Answering with Multi-Level Attentions 多层次关注的逆向视觉问答

Asian Conference on Machine Learning Pub Date : 2019-09-17 DOI: 10.22215/etd/2019-13929

Yaser Alwatter, Yuhong Guo

引用次数: 1

Query Selection via Weighted Entropy in Graph-Based Semi-supervised Classification 基于加权熵的图半监督分类查询选择

Asian Conference on Machine Learning Pub Date : 2009-11-03 DOI: 10.1007/978-3-642-05224-8_22

Krikamol Muandet, S. Marukatat, C. Nattee

引用次数: 4