Michael I Jordan
36 papers · 2013–2023 · 4 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+14 more ↓ Show less ↑
π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (16) π Conference Polyglot (4)
πΊοΈ
Taxonomy Completionist
(16)
π
Conference Polyglot
(4)
π§
Keyword Pioneer
π
Keyword Trendsetter Combo
(5)
π
Conference Loyalist
(32)
π
Keyword Champion
(2)
π¬
Deep Specialist
(14)
π€
Dynamic Duo
(32)
β‘
Prolific Year
(8)
β
The Questioner
π
Century Club
(36)
ποΈ
Keyword Collector
(62)
π
Trend Setter
π₯
Unstoppable
(8)
Conferences
NIPS (32)
COLT (2)
JMLR (1)
UAI (1)
Top co-authors
Research topics
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