← Authors

Michael I Jordan

36 papers · 2013–2023 · 4 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (8) 🐝 Cross-Pollinator (10) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (10) πŸ—ΊοΈ Taxonomy Completionist (16) 🌍 Conference Polyglot (4) 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (5) 🏠 Conference Loyalist (32) πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (14) 🀝 Dynamic Duo (32) ⚑ Prolific Year (8) ❓ The Questioner πŸ’Ž Century Club (36) πŸ—ƒοΈ Keyword Collector (62) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (8)

Conferences

NIPS (32) COLT (2) JMLR (1) UAI (1)

Top co-authors

Michael I. Jordan (32) Martin J. Wainwright (6) Chi Jin (6) Mingsheng Long (4) Xinghao Pan (4) Jianmin Wang (4) John Duchi (3) Stefanie Jegelka (3) Joseph E Gonzalez (3) Yuchen Zhang (3)

Research topics

Privacy (1)

Keywords

convergence rate (7) stochastic optimization (5) nonconvex optimization (4) stochastic gradient descent (4) expectation maximization (3) statistical learning (3) regret bound (3) domain adaptation (3) parallel algorithm (3) variational inference (3) gradient descent (2) unsupervised learning (2) distributed learning (2) probability estimation (2) canonical correlation analysis (2) parameter estimation (2) stochastic gradient (2) statistical learning theory (2) feature selection (2) transfer learning (2)

Papers

VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback JMLR 2023
Nonconvex stochastic scaled gradient descent and generalized eigenvector problems UAI 2023
On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration COLT 2020
Provably efficient reinforcement learning with linear function approximation COLT 2020
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks NIPS 2019
Acceleration via Symplectic Discretization of High-Resolution Differential Equations NIPS 2019
Information Constraints on Auto-Encoding Variational Bayes NIPS 2018
Generalized Zero-Shot Learning with Deep Calibration Network NIPS 2018
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation NIPS 2018
Stochastic Cubic Regularization for Fast Nonconvex Optimization NIPS 2018
On the Local Minima of the Empirical Risk NIPS 2018
Is Q-Learning Provably Efficient? NIPS 2018
Theoretical guarantees for EM under misspecified Gaussian mixture models NIPS 2018
Conditional Adversarial Domain Adaptation NIPS 2018
Non-convex Finite-Sum Optimization Via SCSG Methods NIPS 2017
Online control of the false discovery rate with decaying memory NIPS 2017
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization NIPS 2017
Kernel Feature Selection via Conditional Covariance Minimization NIPS 2017
Gradient Descent Can Take Exponential Time to Escape Saddle Points NIPS 2017
Unsupervised Domain Adaptation with Residual Transfer Networks NIPS 2016
Cyclades: Conflict-free Asynchronous Machine Learning NIPS 2016
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences NIPS 2016
On the Accuracy of Self-Normalized Log-Linear Models NIPS 2015
Variational Consensus Monte Carlo NIPS 2015
Parallel Correlation Clustering on Big Graphs NIPS 2015
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes NIPS 2015
Parallel Double Greedy Submodular Maximization NIPS 2014
On the Convergence Rate of Decomposable Submodular Function Minimization NIPS 2014
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing NIPS 2014
Communication-Efficient Distributed Dual Coordinate Ascent NIPS 2014
Estimation, Optimization, and Parallelism when Data is Sparse NIPS 2013
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation NIPS 2013
Streaming Variational Bayes NIPS 2013
Information-theoretic lower bounds for distributed statistical estimation with communication constraints NIPS 2013
Optimistic Concurrency Control for Distributed Unsupervised Learning NIPS 2013
A Comparative Framework for Preconditioned Lasso Algorithms NIPS 2013