Jordan, Michael I.

Machine learning: Trends, perspectives, and prospects

Jordan, M. I., & Mitchell, T. M. (2015) · Science

Machine Learning and AlgorithmsArtificial Intelligence Applications

Cited by 9,955

Adaptive Mixtures of Local Experts

Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991) · Neural Computation

Neural Networks and ApplicationsSpeech Recognition and Synthesis

Cited by 5,384

Trust region policy optimization

Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 4,578

Learning Transferable Features with Deep Adaptation Networks

Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 4,505

An Introduction to Variational Methods for Graphical Models

Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning

Bayesian Modeling and Causal InferenceGaussian Processes and Bayesian Inference

Cited by 3,791

An internal model for sensorimotor integration

Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995) · Science

Motor Control and AdaptationEmbodied and Extended Cognition

Cited by 3,553

Optimal feedback control as a theory of motor coordination

Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience

Motor Control and AdaptationMuscle activation and electromyography studies

Cited by 3,081

Deep generative modeling for single-cell transcriptomics

Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 2,530

An introduction to MCMC for machine learning.

Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning

Markov Chains and Monte Carlo MethodsGaussian Processes and Bayesian Inference

Cited by 2,451

High-Dimensional Continuous Control Using Generalized Advantage Estimation

Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 2,103

Theoretically principled trade-off between robustness and accuracy

Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., & Jordan, M. I. (2019) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 1,560

Multiple kernel learning, conic duality, and the SMO algorithm

Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04

Advanced Optimization Algorithms ResearchStochastic Gradient Optimization Techniques

Cited by 1,453

Forward models: supervised learning with a distal teacher

Jordan, M. I., & Rumelhart, D. E. (1992) · Cognitive Science

Reinforcement Learning in RoboticsNeural Networks and Applications

Cited by 1,451

Detecting large-scale system problems by mining console logs

Xu, W., Huang, L., Fox, A., Patterson, D., & Jordan, M. I. (2009) · Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles

Software System Performance and ReliabilitySoftware Engineering Research

Cited by 1,332

Factorial Hidden Markov Models

Ghahramani, Z., & Jordan, M. I. (1997) · Machine Learning

Bayesian Modeling and Causal InferenceBayesian Methods and Mixture Models

Cited by 1,157

A Python library for probabilistic analysis of single-cell omics data

Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 789

Dimensionality Reduction for Supervised Learning With Reproducing Kernel Hilbert Spaces

Fukumizu, K., Bach, F. R., & Jordan, M. I. (2003)

Face and Expression RecognitionStatistical Methods and Applications

Cited by 723

HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

Chen, J., Jordan, M. I., & Wainwright, M. J. (2020) · 2020 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 718

Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 691

RLlib: Abstractions for Distributed Reinforcement Learning

Liang, E., Liaw, R., Moritz, P., Nishihara, R., Fox, R., Goldberg, K., Gonzalez, J. E., Jordan, M. I., & Stoica, I. (2017) · arXiv

Reinforcement Learning in RoboticsParallel Computing and Optimization Techniques

Cited by 473

Skilful nowcasting of extreme precipitation with NowcastNet

Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M. I., & Wang, J. (2023) · Nature

Meteorological Phenomena and SimulationsPrecipitation Measurement and Analysis

Cited by 465

MultiVI: deep generative model for the integration of multi-modal data

Ashuach, T., Gabitto, M. I., Jordan, M. I., & Yosef, N. (2021) · Nature Methods

Single-cell and spatial transcriptomicsGenomics and Chromatin Dynamics

Cited by 385

DestVI identifies continuums of cell types in spatial transcriptomics data

Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 276

A critical assessment of Mus musculusgene function prediction using integrated genomic evidence

Peña-Castillo, L., Tasan, M., Myers, C. L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W. K., et al. (2008) · Genome Biology

Bioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

Cited by 248