Michael I. Jordan

Why Is Michael I. Jordan Influential?

According to Wikipedia, Michael Irwin Jordan is an American scientist, professor at the University of California, Berkeley and researcher in machine learning, statistics, and artificial intelligence. Jordan was elected a member of the National Academy of Engineering in 2010 for contributions to the foundations and applications of machine learning.

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Michael I. Jordan's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Papers

Latent Dirichlet Allocation (2001) (33009)
On Spectral Clustering: Analysis and an algorithm (2001) (8884)
Trust Region Policy Optimization (2015) (4517)
Adaptive Mixtures of Local Experts (1991) (4189)
Graphical Models, Exponential Families, and Variational Inference (2008) (4097)
An Introduction to Variational Methods for Graphical Models (1999) (3987)
GRAPHICAL MODELS (1998) (3947)
Hierarchical Dirichlet Processes (2006) (3676)
Machine learning: Trends, perspectives, and prospects (2015) (3512)
Learning Transferable Features with Deep Adaptation Networks (2015) (3361)
Distance Metric Learning with Application to Clustering with Side-Information (2002) (3162)
An internal model for sensorimotor integration. (1995) (3057)
Hierarchical Mixtures of Experts and the EM Algorithm (1993) (3029)
Optimal feedback control as a theory of motor coordination (2002) (2699)
Learning the Kernel Matrix with Semidefinite Programming (2002) (2533)
An Introduction to MCMC for Machine Learning (2004) (2441)
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes (2001) (2309)
Active Learning with Statistical Models (1996) (2153)
High-Dimensional Continuous Control Using Generalized Advantage Estimation (2015) (1959)
Loopy Belief Propagation for Approximate Inference: An Empirical Study (1999) (1820)
Kernel independent component analysis (2003) (1815)
Matching Words and Pictures (2003) (1792)
Kalman filtering with intermittent observations (2004) (1646)
Forward Models: Supervised Learning with a Distal Teacher (1992) (1634)
Multiple kernel learning, conic duality, and the SMO algorithm (2004) (1630)
Deep Transfer Learning with Joint Adaptation Networks (2016) (1546)
Variational inference for Dirichlet process mixtures (2006) (1379)
Variational inference for Dirichlet process mixtures (2006) (1379)
Learning in Graphical Models (1999) (1359)
Theoretically Principled Trade-off between Robustness and Accuracy (2019) (1324)
Convexity, Classification, and Risk Bounds (2006) (1260)
Modeling annotated data (2003) (1240)
Factorial Hidden Markov Models (1995) (1231)
Conditional Adversarial Domain Adaptation (2017) (1127)
Convex and Semi-Nonnegative Matrix Factorizations (2010) (1125)
Attractor dynamics and parallelism in a connectionist sequential machine (1990) (1121)
Unsupervised Domain Adaptation with Residual Transfer Networks (2016) (1119)
Hierarchical Topic Models and the Nested Chinese Restaurant Process (2003) (1106)
A Direct Formulation for Sparse Pca Using Semidefinite Programming (2004) (986)
Serial Order: A Parallel Distributed Processing Approach (1997) (958)
On the Convergence of Stochastic Iterative Dynamic Programming Algorithms (1993) (899)
Detecting large-scale system problems by mining console logs (2009) (874)
Scalable statistical bug isolation (2005) (862)
On Convergence Properties of the EM Algorithm for Gaussian Mixtures (1996) (843)
Kalman filtering with intermittent observations (2003) (767)
Deep Generative Modeling for Single-cell Transcriptomics (2018) (741)
A statistical framework for genomic data fusion (2004) (739)
Feature selection for high-dimensional genomic microarray data (2001) (720)
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces (2004) (718)
Local privacy and statistical minimax rates (2013) (718)
Learning with Mixtures of Trees (2001) (713)
Supervised learning from incomplete data via an EM approach (1993) (662)
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies (2007) (661)
How to Escape Saddle Points Efficiently (2017) (649)
Sensorimotor adaptation in speech production. (1998) (649)
Ray: A Distributed Framework for Emerging AI Applications (2017) (645)
Bug isolation via remote program sampling (2003) (623)
Managing data transfers in computer clusters with orchestra (2011) (619)
Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks (1990) (609)
Bayesian parameter estimation via variational methods (2000) (603)
Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization (2008) (594)
Learning Dependency-Based Compositional Semantics (2011) (587)
Joint covariate selection and joint subspace selection for multiple classification problems (2010) (530)
Is Q-learning Provably Efficient? (2018) (525)
Learning Spectral Clustering (2003) (508)
Gradient Descent Only Converges to Minimizers (2016) (499)
A Robust Minimax Approach to Classification (2003) (498)
A Probabilistic Interpretation of Canonical Correlation Analysis (2005) (491)
PEGASUS: A policy search method for large MDPs and POMDPs (2000) (490)
Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. (2004) (483)
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes (2004) (472)
Fast approximate spectral clustering (2009) (467)
RLlib: Abstractions for Distributed Reinforcement Learning (2017) (452)
Mean Field Theory for Sigmoid Belief Networks (1996) (452)
Hierarchical Beta Processes and the Indian Buffet Process (2007) (445)
A variational perspective on accelerated methods in optimization (2016) (443)
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification (2008) (440)
The Constrained Laplacian Rank Algorithm for Graph-Based Clustering (2016) (432)
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems (1994) (425)
Learning Without State-Estimation in Partially Observable Markovian Decision Processes (1994) (424)
Variational Bayesian Inference with Stochastic Search (2012) (423)
Nonparametric Latent Feature Models for Link Prediction (2009) (419)
Provably Efficient Reinforcement Learning with Linear Function Approximation (2019) (386)
Failure diagnosis using decision trees (2004) (386)
Reinforcement Learning with Soft State Aggregation (1994) (360)
Bridging Theory and Algorithm for Domain Adaptation (2019) (356)
Autonomous Helicopter Flight via Reinforcement Learning (2003) (355)
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation (2018) (352)
Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast (2003) (351)
Revisiting k-means: New Algorithms via Bayesian Nonparametrics (2011) (348)
A scalable bootstrap for massive data (2011) (348)
MLbase: A Distributed Machine-learning System (2013) (345)
A Sticky HDP-HMM With Application to Speaker Diarization (2009) (339)
A Kernelized Stein Discrepancy for Goodness-of-fit Tests (2016) (337)
Probabilistic Independence Networks for Hidden Markov Probability Models (1997) (331)
SUPPORT UNION RECOVERY IN HIGH-DIMENSIONAL MULTIVARIATE REGRESSION (2008) (329)
Convergence results for the EM approach to mixtures of experts architectures (1995) (325)
Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing (2014) (323)
Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations (2013) (321)
Communication-Efficient Distributed Dual Coordinate Ascent (2014) (320)
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning (2009) (316)
Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study (1995) (310)
A mantle plume below the Eifel volcanic fields, Germany (2001) (309)
Hierarchical Bayesian Nonparametric Models with Applications (2008) (309)
Stable algorithms for link analysis (2001) (308)
Learning Semantic Correspondences with Less Supervision (2009) (308)
Streaming Variational Bayes (2013) (301)
Exploiting Tractable Substructures in Intractable Networks (1995) (300)
An HDP-HMM for systems with state persistence (2008) (299)
Partial Transfer Learning with Selective Adversarial Networks (2017) (297)
Kernel dimension reduction in regression (2009) (293)
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack (2019) (293)
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems (2019) (292)
Learning Spectral Clustering, With Application To Speech Separation (2006) (291)
An Alternative Model for Mixtures of Experts (1994) (273)
Transferable Representation Learning with Deep Adaptation Networks (2019) (264)
Minimax Optimal Procedures for Locally Private Estimation (2016) (262)
A critical assessment of Mus musculus gene function prediction using integrated genomic evidence (2008) (260)
Privacy Aware Learning (2012) (259)
Link Analysis, Eigenvectors and Stability (2001) (253)
Communication-Efficient Distributed Statistical Inference (2016) (249)
Predictive low-rank decomposition for kernel methods (2005) (248)
Semiparametric latent factor models (2005) (248)
Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements. (1998) (247)
A General Analysis of the Convergence of ADMM (2015) (238)
A generalized mean field algorithm for variational inference in exponential families (2002) (238)
Information-theoretic lower bounds for distributed statistical estimation with communication constraints (2013) (235)
Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters (2009) (235)
Bayesian Nonparametrics: Hierarchical Bayesian nonparametric models with applications (2010) (231)
Sensorimotor adaptation of speech I: Compensation and adaptation. (2002) (229)
Characterizing, modeling, and generating workload spikes for stateful services (2010) (226)
A Lyapunov Analysis of Momentum Methods in Optimization (2016) (223)
A more biologically plausible learning rule for neural networks. (1991) (222)
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent (2017) (222)
Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization (2007) (221)
Loopy Belief Propogation and Gibbs Measures (2002) (221)
A variational approach to Bayesian logistic regression problems and their extensions (1996) (220)
CoCoA: A General Framework for Communication-Efficient Distributed Optimization (2016) (219)
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems (2008) (217)
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification (2018) (216)
Bayesian Nonparametric Inference of Switching Dynamic Linear Models (2010) (214)
The Infinite PCFG Using Hierarchical Dirichlet Processes (2007) (213)
Learning from Incomplete Data (1994) (212)
Particle gibbs with ancestor sampling (2014) (212)
Universal Domain Adaptation (2019) (211)
Underdamped Langevin MCMC: A non-asymptotic analysis (2017) (208)
Surface/surface intersection (1987) (205)
Generalization to Local Remappings of the Visuomotor Coordinate Transformation (1996) (204)
In-Network PCA and Anomaly Detection (2006) (204)
Supervised learning and systems with excess degrees of freedom (1988) (204)
Protein Molecular Function Prediction by Bayesian Phylogenomics (2005) (203)
Computational and statistical tradeoffs via convex relaxation (2012) (203)
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes (2008) (202)
A kernel-based learning approach to ad hoc sensor network localization (2005) (199)
Gradient Descent Converges to Minimizers (2016) (198)
Gradient Descent Can Take Exponential Time to Escape Saddle Points (2017) (198)
Non-convex Finite-Sum Optimization Via SCSG Methods (2017) (198)
Genomic privacy and limits of individual detection in a pool (2009) (196)
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? (2019) (196)
MLI: An API for Distributed Machine Learning (2013) (195)
Learning piecewise control strategies in a modular neural network architecture (1993) (195)
Divide-and-Conquer Matrix Factorization (2011) (194)
Nested Hierarchical Dirichlet Processes (2012) (190)
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization (2015) (189)
Variational Probabilistic Inference and the QMR-DT Network (2011) (189)
The Handbook of Brain Theory and Neural Networks (2002) (188)
Efficient Ranking from Pairwise Comparisons (2013) (185)
Variational methods for the Dirichlet process (2004) (183)
Hierarchies of Adaptive Experts (1991) (183)
Toward a protein profile of Escherichia coli: Comparison to its transcription profile (2003) (182)
A Linearly-Convergent Stochastic L-BFGS Algorithm (2015) (181)
Trading relations between tongue-body raising and lip rounding in production of the vowel /u/: a pilot "motor equivalence" study. (1993) (179)
Knowing when you're wrong: building fast and reliable approximate query processing systems (2014) (178)
Series foreword (2003) (173)
Semi-supervised Learning via Gaussian Processes (2004) (170)
Statistical debugging: simultaneous identification of multiple bugs (2006) (170)
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning (2018) (167)
Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones (1999) (166)
A Competitive Modular Connectionist Architecture (1990) (166)
A Berkeley View of Systems Challenges for AI (2017) (166)
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (2019) (166)
Tree-Structured Stick Breaking for Hierarchical Data (2010) (166)
Generalized Zero-Shot Learning with Deep Calibration Network (2018) (166)
Generic constraints on underspecified target trajectories (1989) (164)
Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model (2010) (163)
Genome-Wide Requirements for Resistance to Functionally Distinct DNA-Damaging Agents (2005) (163)
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning (2014) (162)
Learning from Dyadic Data (1998) (162)
Thin Junction Trees (2001) (162)
SparkNet: Training Deep Networks in Spark (2015) (160)
Understanding the acceleration phenomenon via high-resolution differential equations (2018) (158)
Sharing Features among Dynamical Systems with Beta Processes (2009) (157)
Adding vs. Averaging in Distributed Primal-Dual Optimization (2015) (156)
Near-Optimal Algorithms for Minimax Optimization (2020) (156)
The SCADS Director: Scaling a Distributed Storage System Under Stringent Performance Requirements (2011) (154)
First-order Methods Almost Always Avoid Saddle Points (2017) (153)
Computational models of sensorimotor integration (1997) (150)
Artificial Intelligence—The Revolution Hasn’t Happened Yet (2019) (150)
Matrix concentration inequalities via the method of exchangeable pairs (2012) (148)
Online System Problem Detection by Mining Patterns of Console Logs (2009) (146)
Distributed optimization with arbitrary local solvers (2015) (144)
Communication-Efficient Online Detection of Network-Wide Anomalies (2007) (143)
Why the logistic function? A tutorial discussion on probabilities and neural networks (1995) (142)
Automating model search for large scale machine learning (2015) (139)
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators (2008) (139)
Mixtures of Probabilistic Principal Component Analyzers (2001) (138)
Modeling Events with Cascades of Poisson Processes (2010) (136)
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data (2018) (136)
Learning to Control an Unstable System with Forward Modeling (1989) (136)
Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding (2008) (136)
Multiple Non-Redundant Spectral Clustering Views (2010) (135)
An Analysis of the Convergence of Graph Laplacians (2010) (135)
Obstacle Avoidance and a Perturbation Sensitivity Model for Motor Planning (1997) (133)
The Big Data Bootstrap (2012) (133)
Unsupervised Learning from Dyadic Data (1998) (133)
Nonparametric Link Prediction in Dynamic Networks (2012) (133)
ON surrogate loss functions and f-divergences (2005) (132)
Nonparametric empirical Bayes for the Dirichlet process mixture model (2006) (131)
Stochastic Cubic Regularization for Fast Nonconvex Optimization (2017) (130)
Computational Consequences of a Bias toward Short Connections (1992) (130)
Bayesian Bias Mitigation for Crowdsourcing (2011) (130)
Learning from measurements in exponential families (2009) (126)
Structured Prediction, Dual Extragradient and Bregman Projections (2006) (126)
Beyond Independent Components: Trees and Clusters (2003) (125)
Local Privacy and Statistical Minimax Rates (2013) (124)
Variational methods for inference and estimation in graphical models (1997) (122)
Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting (2018) (122)
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation (2013) (122)
Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint (2001) (121)
A Minimal Intervention Principle for Coordinated Movement (2002) (120)
Increased VO2 max with right-shifted Hb-O2 dissociation curve at a constant O2 delivery in dog muscle in situ. (1998) (118)
Minimax Probability Machine (2001) (118)
Blind One-microphone Speech Separation: A Spectral Learning Approach (2004) (118)
First-order methods almost always avoid strict saddle points (2019) (118)
Learning graphical models for stationary time series (2004) (117)
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks (2019) (117)
On the Computational Complexity of High-Dimensional Bayesian Variable Selection (2015) (116)
Learning Programs: A Hierarchical Bayesian Approach (2010) (115)
The Role of Inertial Sensitivity in Motor Planning (1998) (115)
Computing regularization paths for learning multiple kernels (2004) (114)
Mining Console Logs for Large-Scale System Problem Detection (2008) (114)
Lower bounds on the performance of polynomial-time algorithms for sparse linear regression (2014) (113)
Support vector machines for analog circuit performance representation (2003) (113)
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games (2019) (112)
Computational aspects of motor control and motor learning (2008) (111)
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox (2014) (110)
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences (2016) (109)
Hidden Markov Decision Trees (1996) (108)
Regression on manifolds using kernel dimension reduction (2007) (107)
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms (2019) (107)
Viewing the hand prior to movement improves accuracy of pointing performed toward the unseen contralateral hand (1997) (107)
Shaping and policy search in reinforcement learning (2003) (105)
Perceptual distortion contributes to the curvature of human reaching movements (1994) (104)
Provable Meta-Learning of Linear Representations (2020) (104)
Sampling can be faster than optimization (2018) (101)
Consistent probabilistic outputs for protein function prediction (2008) (100)
Estimation, Optimization, and Parallelism when Data is Sparse (2013) (100)
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points (2019) (99)
Bayesian Haplotype Inference via the Dirichlet Process (2007) (99)
On statistics, computation and scalability (2013) (98)
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures (2000) (97)
Sulfur and Nitrogen Limitation in Escherichia coli K-12: Specific Homeostatic Responses (2005) (97)
Word Alignment via Quadratic Assignment (2006) (96)
Bayesian Nonparametric Latent Feature Models (2011) (96)
On the Consistency of Ranking Algorithms (2010) (95)
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization (2007) (95)
Log-determinant relaxation for approximate inference in discrete Markov random fields (2006) (95)
Approximating Posterior Distributions in Belief Networks Using Mixtures (1997) (95)
Dried blood spots for HIV-1 drug resistance and viral load testing: A review of current knowledge and WHO efforts for global HIV drug resistance surveillance. (2010) (94)
On the Theory of Transfer Learning: The Importance of Task Diversity (2020) (94)
Graphical models: Probabilistic inference (2002) (93)
Robust Novelty Detection with Single-Class MPM (2002) (92)
MAD-Bayes: MAP-based Asymptotic Derivations from Bayes (2012) (92)
Boltzmann Chains and Hidden Markov Models (1994) (91)
Spectral Clustering with Perturbed Data (2008) (91)
Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization (2005) (91)
Nonparametric decentralized detection using kernel methods (2005) (90)
Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes (2007) (90)
Uncertainty Sets for Image Classifiers using Conformal Prediction (2020) (89)
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time (2015) (87)
Leo Breiman (2011) (86)
JOINT MODELING OF MULTIPLE TIME SERIES VIA THE BETA PROCESS WITH APPLICATION TO MOTION CAPTURE SEGMENTATION (2013) (85)
Local linear perceptrons for classification (1996) (84)
Information Constraints on Auto-Encoding Variational Bayes (2018) (84)
The Sticky HDP-HMM: Bayesian Nonparametric Hidden Markov Models with Persistent States (2009) (83)
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo (2018) (83)
Variational Learning for Switching State-Space Models (2001) (82)
Ergodic mirror descent (2011) (81)
Bayesian Nonparametric Methods for Learning Markov Switching Processes (2010) (81)
Probabilistic models of text and images (2004) (81)
Less than a Single Pass: Stochastically Controlled Stochastic Gradient (2016) (81)
Beta Processes, Stick-Breaking and Power Laws (2011) (81)
Phylogenetic Inference via Sequential Monte Carlo (2012) (80)
Computing upper and lower bounds on likelihoods in intractable networks (1996) (79)
Efficient Stepwise Selection in Decomposable Models (2001) (79)
Statistical Debugging of Sampled Programs (2003) (77)
An introduction to linear algebra in parallel distributed processing (1986) (77)
Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models (2019) (77)
A Dynamical Systems Perspective on Nesterov Acceleration (2019) (76)
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal (2019) (76)
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (2019) (76)
On Symplectic Optimization (2018) (75)
Goal-based speech motor control: A theoretical framework and some preliminary data (1995) (75)
Improving the Mean Field Approximation Via the Use of Mixture Distributions (1999) (75)
Acceleration via Symplectic Discretization of High-Resolution Differential Equations (2019) (75)
Robust design of biological experiments (2005) (75)
A latent variable model for chemogenomic profiling (2005) (75)
A more biologically plausible learning rule than backpropagation applied to a network model of cortical area 7a. (1991) (75)
Effect of NO, vasodilator prostaglandins, and adenosine on skeletal muscle angiogenic growth factor gene expression. (1999) (75)
Union support recovery in high-dimensional multivariate regression (2008) (74)
How Does Learning Rate Decay Help Modern Neural Networks (2019) (73)
A unified treatment of multiple testing with prior knowledge using the p-filter (2017) (73)
Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data (2018) (72)
Regularized Discriminant Analysis, Ridge Regression and Beyond (2010) (72)
50 Strategies for Teaching English Language Learners (2015) (72)
Active site prediction using evolutionary and structural information (2010) (71)
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes (2015) (71)
The Organization of Action Sequences: Evidence From a Relearning Task. (1995) (71)
Learning Graphical Models with Mercer Kernels (2002) (70)
Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models (2012) (70)
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm (2019) (69)
Averaging Stochastic Gradient Descent on Riemannian Manifolds (2018) (69)
Robust Optimization for Fairness with Noisy Protected Groups (2020) (68)
Parallel Correlation Clustering on Big Graphs (2015) (68)
Is There an Analog of Nesterov Acceleration for MCMC? (2019) (67)
A Model of the Learning of Arm Trajectories from Spatial Deviations (1994) (67)
A Swiss Army Infinitesimal Jackknife (2018) (67)
Covariances, Robustness, and Variational Bayes (2017) (66)
Local Privacy, Data Processing Inequalities, and Statistical Minimax Rates (2013) (66)
Combinatorial Clustering and the Beta Negative Binomial Process (2011) (65)
Feature allocations, probability functions, and paintboxes (2013) (64)
Structured Prediction via the Extragradient Method (2005) (64)
Kernel Feature Selection via Conditional Covariance Minimization (2017) (63)
Logos: a Modular Bayesian Model for de Novo Motif Detection (2004) (62)
Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data (2003) (62)
Constrained supervised learning (1992) (62)
Mixed Membership Matrix Factorization (2010) (61)
Cyclades: Conflict-free Asynchronous Machine Learning (2016) (61)
Modular and hierarchical learning systems (1998) (60)
Angiogenic growth factor mRNA responses to passive and contraction-induced hyperperfusion in skeletal muscle. (1998) (60)
Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference (2014) (59)
Genome-scale phylogenetic function annotation of large and diverse protein families. (2011) (59)
Learning in Boltzmann Trees (1994) (59)
Stochastic Gradient Descent Escapes Saddle Points Efficiently (2019) (58)
Distribution-Free, Risk-Controlling Prediction Sets (2021) (58)
Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization (2020) (58)
Regression with input-dependent noise: A Gaussian process treatment (1998) (57)
Extensions of the Informative Vector Machine (2004) (57)
Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates (2003) (56)
Ancestor Sampling for Particle Gibbs (2012) (56)
Optimality guarantees for distributed statistical estimation (2014) (55)
Bayesian semiparametric Wiener system identification (2013) (55)
A Python library for probabilistic analysis of single-cell omics data. (2022) (55)
The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features (2008) (55)
Bayesian inference for queueing networks and modeling of internet services (2010) (55)
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm (2019) (54)
A randomization test for controlling population stratification in whole-genome association studies. (2007) (54)
Multiple-sequence functional annotation and the generalized hidden Markov phylogeny (2004) (54)
Nonparametric Bayesian Co-clustering Ensembles (2011) (53)
Iterative Discovery of Multiple AlternativeClustering Views (2014) (53)
Real-Time Machine Learning: The Missing Pieces (2017) (53)
EP-GIG Priors and Applications in Bayesian Sparse Learning (2012) (52)
Online control of the false discovery rate with decaying memory (2017) (52)
SMaSH: a benchmarking toolkit for human genome variant calling (2013) (52)
Experience Mining Google's Production Console Logs (2010) (52)
Automatic exploration of datacenter performance regimes (2009) (51)
Hierarchical Models , Nested Models and Completely Random Measures (2010) (51)
Age-related changes in serum insulin-like growth factor-I, insulin-like growth factor-I binding protein-3 and articular cartilage structure in Thoroughbred horses. (2010) (51)
Distributed matrix completion and robust factorization (2011) (51)
Active Learning for Nonlinear System Identification with Guarantees (2020) (51)
Cluster Forests (2011) (50)
Estimating Dependency Structure as a Hidden Variable (1997) (50)
Variational Consensus Monte Carlo (2015) (50)
Active spectral clustering via iterative uncertainty reduction (2012) (50)
Hierarchical Bayesian Models for Applications in Information Retrieval (2003) (50)
A statistical approach to decision tree modeling (1994) (49)
Stick-Breaking Beta Processes and the Poisson Process (2012) (49)
Dimensionality Reduction for Spectral Clustering (2011) (49)
Discriminative training of hidden Markov models for multiple pitch tracking [speech processing examples] (2005) (48)
Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data (2004) (47)
Chapter 2 Computational aspects of motor control and motor learning (1996) (47)
Learning Fine Motion by Markov Mixtures of Experts (1995) (47)
ML-LOO: Detecting Adversarial Examples with Feature Attribution (2019) (47)
Active Learning for Crowd-Sourced Databases (2012) (47)
Variational inference in graphical models: The view from the marginal polytope (2008) (46)
Competing Bandits in Matching Markets (2019) (46)
Unsupervised Kernel Dimension Reduction (2010) (45)
On the inference of ancestries in admixed populations. (2008) (45)
Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture (2006) (45)
Multiway Spectral Clustering: A Margin-based Perspective (2008) (45)
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements (2019) (45)
scvi-tools: a library for deep probabilistic analysis of single-cell omics data (2021) (45)
Cluster and Feature Modeling from Combinatorial Stochastic Processes (2012) (44)
Recursive Algorithms for Approximating Probabilities in Graphical Models (1996) (44)
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm (2020) (43)
On the Convergence Rate of Decomposable Submodular Function Minimization (2014) (43)
Task Decomposition through Competition in A (1990) (43)
Are Reaching Movements Planned to be Straight and Invariant in the Extrinsic Space? Kinematic Comparison between Compliant and Unconstrained Motions (1999) (43)
Generalized Momentum-Based Methods: A Hamiltonian Perspective (2019) (42)
Joint Modeling of Multiple Related Time Series via the Beta Process (2011) (42)
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