JMLR workshop and conference proceedings最新文献

Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition. 基于熵正则化对偶分解的可伸缩凸多序列对齐。

JMLR workshop and conference proceedings Pub Date : 2017-04-01

Jiong Zhang, Ian E H Yen, Pradeep Ravikumar, Inderjit S Dhillon

{"title":"Scalable Convex Multiple Sequence Alignment via Entropy-Regularized Dual Decomposition.","authors":"Jiong Zhang, Ian E H Yen, Pradeep Ravikumar, Inderjit S Dhillon","doi":"","DOIUrl":"","url":null,"abstract":"Multiple Sequence Alignment (MSA) is one of the fundamental tasks in biological sequence analysis that underlies applications such as phylogenetic trees, profiles, and structure prediction. The task, however, is NP-hard, and the current practice resorts to heuristic and local-search methods. Recently, a convex optimization approach for MSA was proposed based on the concept of atomic norm [23], which demonstrates significant improvement over existing methods in the quality of alignments. However, the convex program is challenging to solve due to the constraint given by the intersection of two atomic-norm balls, for which the existing algorithm can only handle sequences of length up to 50, with an iteration complexity subject to constants of unknown relation to the natural parameters of MSA. In this work, we propose an accelerated dual decomposition algorithm that exploits entropy regularization to induce closed-form solutions for each atomic-norm-constrained subproblem, giving a single-loop algorithm of iteration complexity linear to the problem size (total length of all sequences). The proposed algorithm gives significantly better alignments than existing methods on sequences of length up to hundreds, where the existing convex programming method fails to converge in one day.","PeriodicalId":89793,"journal":{"name":"JMLR workshop and conference proceedings","volume":"54 ","pages":"1514-1522"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581665/pdf/nihms896524.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35472764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain. 大输出域MAP推理的乘法器贪心方向法。

JMLR workshop and conference proceedings Pub Date : 2017-04-01

Xiangru Huang, Qixing Huang, Ian E H Yen, Pradeep Ravikumar, Ruohan Zhang, Inderjit S Dhillon

{"title":"Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain.","authors":"Xiangru Huang, Qixing Huang, Ian E H Yen, Pradeep Ravikumar, Ruohan Zhang, Inderjit S Dhillon","doi":"","DOIUrl":"","url":null,"abstract":"Maximum-a-Posteriori (MAP) inference lies at the heart of Graphical Models and Structured Prediction. Despite the intractability of exact MAP inference, approximate methods based on LP relaxations have exhibited superior performance across a wide range of applications. Yet for problems involving large output domains (i.e., the state space for each variable is large), standard LP relaxations can easily give rise to a large number of variables and constraints which are beyond the limit of existing optimization algorithms. In this paper, we introduce an effective MAP inference method for problems with large output domains. The method builds upon alternating minimization of an Augmented Lagrangian that exploits the sparsity of messages through greedy optimization techniques. A key feature of our greedy approach is to introduce variables in an on-demand manner with a pre-built data structure over local factors. This results in a single-loop algorithm of sublinear cost per iteration and O(log(1/ε))-type iteration complexity to achieve ε sub-optimality. In addition, we introduce a variant of GDMM for binary MAP inference problems with a large number of factors. Empirically, the proposed algorithms demonstrate orders of magnitude speedup over state-of-the-art MAP inference techniques on MAP inference problems including Segmentation, Protein Folding, Graph Matching, and Multilabel prediction with pairwise interaction.","PeriodicalId":89793,"journal":{"name":"JMLR workshop and conference proceedings","volume":"54 ","pages":"1550-1559"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581664/pdf/nihms896523.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35472765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. 人工智能医生:通过循环神经网络预测临床事件。

JMLR workshop and conference proceedings Pub Date : 2016-08-01 Epub Date: 2016-12-10

Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, Jimeng Sun

引用次数: 0

A Hybrid Causal Search Algorithm for Latent Variable Models. 潜在变量模型的混合因果搜索算法。

JMLR workshop and conference proceedings Pub Date : 2016-08-01

Juan Miguel Ogarrio, Peter Spirtes, Joe Ramsey

引用次数: 0

Uncovering Voice Misuse Using Symbolic Mismatch. 利用符号错配揭露语音误用。

JMLR workshop and conference proceedings Pub Date : 2016-08-01

Marzyeh Ghassemi, Zeeshan Syed, Daryush D Mehta, Jarrad H Van Stan, Robert E Hillman, John V Guttag

引用次数: 0

Causal Discovery from Subsampled Time Series Data by Constraint Optimization. 通过约束优化从子采样时间序列数据中发现因果关系

JMLR workshop and conference proceedings Pub Date : 2016-08-01

Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, David Danks

引用次数: 0

Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies. 平方根图形模型:允许正相关的单变量指数族的多元推广。

JMLR workshop and conference proceedings Pub Date : 2016-06-01

David I Inouye, Pradeep Ravikumar, Inderjit S Dhillon

{"title":"Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies.","authors":"David I Inouye, Pradeep Ravikumar, Inderjit S Dhillon","doi":"","DOIUrl":"","url":null,"abstract":"We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York-modeled as an exponential distribution-is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix-a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with ℓ1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times.","PeriodicalId":89793,"journal":{"name":"JMLR workshop and conference proceedings","volume":"48 ","pages":"2445-2453"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995108/pdf/nihms808904.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34338585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain Adaptation with Conditional Transferable Components. 具有条件可转移组件的领域自适应。

JMLR workshop and conference proceedings Pub Date : 2016-06-01

Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf

{"title":"Domain Adaptation with Conditional Transferable Components.","authors":"Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf","doi":"","DOIUrl":"","url":null,"abstract":"Domain adaptation arises in supervised learning when the training (source domain) and test (target domain) data have different distributions. Let X and Y denote the features and target, respectively, previous work on domain adaptation mainly considers the covariate shift situation where the distribution of the features P(X) changes across domains while the conditional distribution P(Y∣X) stays the same. To reduce domain discrepancy, recent methods try to find invariant components [Formula: see text] that have similar [Formula: see text] on different domains by explicitly minimizing a distribution discrepancy measure. However, it is not clear if [Formula: see text] in different domains is also similar when P(Y∣X) changes. Furthermore, transferable components do not necessarily have to be invariant. If the change in some components is identifiable, we can make use of such components for prediction in the target domain. In this paper, we focus on the case where P(X∣Y) and P(Y) both change in a causal system in which Y is the cause for X. Under appropriate assumptions, we aim to extract conditional transferable components whose conditional distribution [Formula: see text] is invariant after proper location-scale (LS) transformations, and identify how P(Y) changes between domains simultaneously. We provide theoretical analysis and empirical evaluation on both synthetic and real-world data to show the effectiveness of our method.","PeriodicalId":89793,"journal":{"name":"JMLR workshop and conference proceedings","volume":"48 ","pages":"2839-2848"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321138/pdf/nihms-846268.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34766373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anytime Exploration for Multi-armed Bandits using Confidence Information. 利用信任信息随时探索多武装土匪。

JMLR workshop and conference proceedings Pub Date : 2016-06-01

Kwang-Sung Jun, Robert Nowak

引用次数: 0

Experimental Design on a Budget for Sparse Linear Models and Applications. 稀疏线性模型和应用的预算实验设计

JMLR workshop and conference proceedings Pub Date : 2016-06-01

Sathya N Ravi, Vamsi K Ithapu, Sterling C Johnson, Vikas Singh

引用次数: 0