![]() ![]() Spatial Multivariate Trees for Big Data Bayesian Regression Michele Peruzzi, David B. TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems Kaixuan Wei, Angelica Aviles-Rivero, Jingwei Liang, Ying Fu, Hua Huang, Carola-Bibiane Schönlieb (16):1−48, 2022. Ghosh (14):1−41, 2022.Ī Stochastic Bundle Method for Interpolation Alasdair Paren, Leonard Berrada, Rudra P. On Generalizations of Some Distance Based Classifiers for HDLSS Data Sarbojit Roy, Soham Sarkar, Subhajit Dutta, Anil K. Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality Dimitris Bertsimas, Ryan Cory-Wright, Jean Pauphilet (13):1−35, 2022. Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes Ali Kara, Serdar Yuksel (11):1−46, 2022.Īpproximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan (12):1−83, 2022. ![]() Interpolating Predictors in High-Dimensional Factor Regression Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp (10):1−60, 2022. Scaling Laws from the Data Manifold Dimension Utkarsh Sharma, Jared Kaplan (9):1−34, 2022. ![]() David, Sayan Mukherjee (7):1−42, 2022.ĭeep Learning in Target Space Michael Fairbank, Spyridon Samothrakis, Luca Citi (8):1−46, 2022. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou (6):1−28, 2022.īayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes Justin D. XAI Beyond Classification: Interpretable Neural Clustering Xi Peng, Yunfan Li, Ivor W. Recovering shared structure from multiple networks with unknown edge distributions Keith Levin, Asad Lodhia, Elizaveta Levina (3):1−48, 2022.Įxploiting locality in high-dimensional Factorial hidden Markov models Lorenzo Rimella, Nick Whiteley (4):1−34, 2022.Įmpirical Risk Minimization under Random Censorship Guillaume Ausset, Stephan Clémençon, François Portier (5):1−59, 2022. Though recognized by all Vietnamese as a selfless patriot, Chau left behind him an ideological and organizational vacuum which the communists did not fail to exploit after World War II.Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models Subhabrata Majumdar, George Michailidis (1):1−53, 2022.ĭebiased Distributed Learning for Sparse Partial Linear Models in High Dimensions Shaogao Lv, Heng Lian (2):1−32, 2022. His failure had significant consequences. ![]() Finally, he was more a romantic than a Lenin-style revolutionary-prone to desperate acts and to quick discouragement, too impatient to undertake the painstaking work necessary to build a strongly disciplined organization. Failing to see the importance of mass support, he did not attempt to win the active allegiance of the Vietnamese peasantry. He failed to formulate a cohesive ideological position, shifting from traditionalism, to a Meiji-style constitutional monarch, and then to republicanism. A careful analysis of his activities and his major writings indicates that Chau's failure is related to his own weaknesses as a revolutionary leader. Yet he failed to leave behind him a disciplined and well-organized party with broad support throughout the country. Phan Boi Chau was the recognized leader of the Vietnamese struggle for independence during the first quarter of the twentieth century. ![]()
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