Journal of Machine Learning Research, Vol. 23, 2022
1. Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models. --Subhabrata Majumdar, George Michailidis.
2. Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions. --Shaogao Lv, Heng Lian.
3. Recovering shared structure from multiple networks with unknown edge distributions. --Keith Levin, Asad Lodhia, Elizaveta Levina.
4. Exploiting locality in high-dimensional Factorial hidden Markov models. --Lorenzo Rimella, Nick Whiteley.
5. Empirical Risk Minimization under Random Censorship. --Guillaume Ausset, Stephan Clémençon, François Portier.
6. XAI Beyond Classification: Interpretable Neural Clustering. --Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou.
7. Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes. --Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee.
8. Deep Learning in Target Space. --Michael Fairbank, Spyridon Samothrakis, Luca Citi.
9. Scaling Laws from the Data Manifold Dimension. --Utkarsh Sharma, Jared Kaplan.
10. Interpolating Predictors in High-Dimensional Factor Regression. --Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp.
11. Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes. --Ali Kara, Serdar Yuksel.
12. Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems. --Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan.
etc...
2. Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions. --Shaogao Lv, Heng Lian.
3. Recovering shared structure from multiple networks with unknown edge distributions. --Keith Levin, Asad Lodhia, Elizaveta Levina.
4. Exploiting locality in high-dimensional Factorial hidden Markov models. --Lorenzo Rimella, Nick Whiteley.
5. Empirical Risk Minimization under Random Censorship. --Guillaume Ausset, Stephan Clémençon, François Portier.
6. XAI Beyond Classification: Interpretable Neural Clustering. --Xi Peng, Yunfan Li, Ivor W. Tsang, Hongyuan Zhu, Jiancheng Lv, Joey Tianyi Zhou.
7. Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes. --Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee.
8. Deep Learning in Target Space. --Michael Fairbank, Spyridon Samothrakis, Luca Citi.
9. Scaling Laws from the Data Manifold Dimension. --Utkarsh Sharma, Jared Kaplan.
10. Interpolating Predictors in High-Dimensional Factor Regression. --Florentina Bunea, Seth Strimas-Mackey, Marten Wegkamp.
11. Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes. --Ali Kara, Serdar Yuksel.
12. Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems. --Jayakumar Subramanian, Amit Sinha, Raihan Seraj, Aditya Mahajan.
etc...
Majumdar, Subhabrata - Personal Name
Kara, Ali - Personal Name
Bunea, Florentina - Personal Name
Sharma, Utkarsh - Personal Name
Fairbank, Michael - Personal Name
Silverman, Justin D. - Personal Name
Peng, Xi - Personal Name
Ausset, Guillaume - Personal Name
Rimella, Lorenzo - Personal Name
Levin, Keith - Personal Name
Shaogao Lv - Personal Name
Subramanian, Jayakumar - Personal Name
Kara, Ali - Personal Name
Bunea, Florentina - Personal Name
Sharma, Utkarsh - Personal Name
Fairbank, Michael - Personal Name
Silverman, Justin D. - Personal Name
Peng, Xi - Personal Name
Ausset, Guillaume - Personal Name
Rimella, Lorenzo - Personal Name
Levin, Keith - Personal Name
Shaogao Lv - Personal Name
Subramanian, Jayakumar - Personal Name
Vol. 23, 2022
1533-7928
e-Journal TI
Inggris
JMLR Inc. - Microtome Publishing
2022
USA
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