# Tianbao Yang

According to our database1, Tianbao Yang authored at least 166 papers between 2009 and 2021.

Collaborative distances:

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## Bibliography

2021
Hybrid safe-strong rules for efficient optimization in lasso-type problems.
Comput. Stat. Data Anal., 2021

Momentum Accelerates the Convergence of Stochastic AUPRC Maximization.
CoRR, 2021

Memory-based Optimization Methods for Model-Agnostic Meta-Learning.
CoRR, 2021

A Simple yet Universal Strategy for Online Convex Optimization.
CoRR, 2021

Randomized Stochastic Variance-Reduced Methods for Stochastic Bilevel Optimization.
CoRR, 2021

On Stochastic Moving-Average Estimators for Non-Convex Optimization.
CoRR, 2021

Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence.
CoRR, 2021

Online Convex Optimization with Continuous Switching Constraint.
CoRR, 2021

Revisiting Smoothed Online Learning.
CoRR, 2021

Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity.
CoRR, 2021

Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity.
Proceedings of the 38th International Conference on Machine Learning, 2021

Stability and Generalization of Stochastic Gradient Methods for Minimax Problems.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity.
Mach. Learn., 2020

Hybrid-DCA: A double asynchronous approach for stochastic dual coordinate ascent.
J. Parallel Distributed Comput., 2020

A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints.
J. Mach. Learn. Res., 2020

Attentional Biased Stochastic Gradient for Imbalanced Classification.
CoRR, 2020

Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification.
CoRR, 2020

CoRR, 2020

Variance-Reduced Off-Policy Memory-Efficient Policy Search.
CoRR, 2020

A Practical Online Method for Distributionally Deep Robust Optimization.
CoRR, 2020

Nearly Optimal Robust Method for Convex Compositional Problems with Heavy-Tailed Noise.
CoRR, 2020

Fast Objective and Duality Gap Convergence for Non-convex Strongly-concave Min-max Problems.
CoRR, 2020

Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives.
CoRR, 2020

Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.
CoRR, 2020

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improved Schemes for Episodic Memory-based Lifelong Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints.
Proceedings of the 37th International Conference on Machine Learning, 2020

Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Optimization for Non-convex Inf-Projection Problems.
Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic AUC Maximization with Deep Neural Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

Proceedings of the 8th International Conference on Learning Representations, 2020

Accelerating Deep Learning with Millions of Classes.
Proceedings of the Computer Vision - ECCV 2020, 2020

A Simple and Effective Framework for Pairwise Deep Metric Learning.
Proceedings of the Computer Vision - ECCV 2020, 2020

Minimizing Dynamic Regret and Adaptive Regret Simultaneously.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Adversarial Localized Energy Network for Structured Prediction.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
A simple homotopy proximal mapping algorithm for compressive sensing.
Mach. Learn., 2019

Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion.
J. Mach. Learn. Res., 2019

Decentralized Parallel Algorithm for Training Generative Adversarial Nets.
CoRR, 2019

CoRR, 2019

Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√T) for Problems without Bilinear Structure.
CoRR, 2019

Advancing non-convex and constrained learning: challenges and opportunities.
AI Matters, 2019

Learning with Non-Convex Truncated Losses by SGD.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Stagewise Training Accelerates Convergence of Testing Error Over SGD.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence.
Proceedings of the 36th International Conference on Machine Learning, 2019

Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number.
Proceedings of the 36th International Conference on Machine Learning, 2019

Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions.
Proceedings of the 7th International Conference on Learning Representations, 2019

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching From Scratch.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

A Robust Zero-Sum Game Framework for Pool-based Active Learning.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Combining Link and Content for Community Detection.
Proceedings of the Encyclopedia of Social Network Analysis and Mining, 2nd Edition, 2018

RSG: Beating Subgradient Method without Smoothness and Strong Convexity.
J. Mach. Learn. Res., 2018

Why Does Stagewise Training Accelerate Convergence of Testing Error Over SGD?
CoRR, 2018

Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning.
CoRR, 2018

Learning Discriminators as Energy Networks in Adversarial Learning.
CoRR, 2018

EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching.
CoRR, 2018

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints.
CoRR, 2018

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization.
Big Data Cogn. Comput., 2018

Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Adaptive Negative Curvature Descent with Applications in Non-convex Optimization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data.
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

A Unified Analysis of Stochastic Momentum Methods for Deep Learning.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Dynamic Regret of Strongly Adaptive Methods.
Proceedings of the 35th International Conference on Machine Learning, 2018

Fast Stochastic AUC Maximization with O(1/n)-Convergence Rate.
Proceedings of the 35th International Conference on Machine Learning, 2018

Level-Set Methods for Finite-Sum Constrained Convex Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

Proceedings of the 35th International Conference on Machine Learning, 2018

Improving Sequential Determinantal Point Processes for Supervised Video Summarization.
Proceedings of the Computer Vision - ECCV 2018, 2018

How Local Is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization.
Proceedings of the Computer Vision - ECCV 2018, 2018

An Indirect-Direct event-triggered mechanism for networked control system against DoS attacks.
Proceedings of the 2018 Australian & New Zealand Control Conference (ANZCC), 2018

A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
A Multisource Domain Generalization Approach to Visual Attribute Detection.
Proceedings of the Domain Adaptation in Computer Vision Applications., 2017

Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.
J. Mach. Learn. Res., 2017

Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence.
CoRR, 2017

SEP-Nets: Small and Effective Pattern Networks.
CoRR, 2017

Strongly Adaptive Regret Implies Optimally Dynamic Regret.
CoRR, 2017

Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n<sup>2</sup>)-type of Risk Bounds.
CoRR, 2017

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Improved Dynamic Regret for Non-degenerate Functions.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

SVD-free Convex-Concave Approaches for Nuclear Norm Regularization.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates.
Proceedings of the 34th International Conference on Machine Learning, 2017

Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence.
Proceedings of the 34th International Conference on Machine Learning, 2017

Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds.
Proceedings of the 30th Conference on Learning Theory, 2017

A Framework of Online Learning with Imbalanced Streaming Data.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

A Two-Stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
On Data Preconditioning for Regularized Loss Minimization.
Mach. Learn., 2016

Accelerate Stochastic Subgradient Method by Leveraging Local Error Bound.
CoRR, 2016

Improved dynamic regret for non-degeneracy functions.
CoRR, 2016

Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/\epsilon).
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Improved Dropout for Shallow and Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Online Asymmetric Active Learning with Imbalanced Data.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Online Stochastic Linear Optimization under One-bit Feedback.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Learning Attributes Equals Multi-Source Domain Generalization.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

Sparse Learning for Large-Scale and High-Dimensional Data: A Randomized Convex-Concave Optimization Approach.
Proceedings of the Algorithmic Learning Theory - 27th International Conference, 2016

Fast and Accurate Refined Nyström-Based Kernel SVM.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Stochastic Optimization for Kernel PCA.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
An efficient primal dual prox method for non-smooth optimization.
Mach. Learn., 2015

Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data.
CoRR, 2015

Fast Sparse Least-Squares Regression with Non-Asymptotic Guarantees.
CoRR, 2015

Stochastic Proximal Gradient Descent for Nuclear Norm Regularization.
CoRR, 2015

Online Stochastic Linear Optimization under One-bit Feedback.
CoRR, 2015

Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion.
CoRR, 2015

An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Big Data Analytics: Optimization and Randomization.
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Theory of Dual-sparse Regularized Randomized Reduction.
Proceedings of the 32nd International Conference on Machine Learning, 2015

An Explicit Sampling Dependent Spectral Error Bound for Column Subset Selection.
Proceedings of the 32nd International Conference on Machine Learning, 2015

In-Situ Measurement and Prediction of Hearing Aid Outcomes Using Mobile Phones.
Proceedings of the 2015 International Conference on Healthcare Informatics, 2015

Hyper-class augmented and regularized deep learning for fine-grained image classification.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

A Simple Homotopy Algorithm for Compressive Sensing.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Online Bandit Learning for a Special Class of Non-Convex Losses.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
Combining Link and Content for Community Detection.
Encyclopedia of Social Network Analysis and Mining, 2014

Random Projections for Classification: A Recovery Approach.
IEEE Trans. Inf. Theory, 2014

Regret bounded by gradual variation for online convex optimization.
Mach. Learn., 2014

On Data Preconditioning for Regularized Loss Minimization.
CoRR, 2014

A Simple Homotopy Proximal Mapping for Compressive Sensing.
CoRR, 2014

Object-centric Sampling for Fine-grained Image Classification.
CoRR, 2014

Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Efficient Low-Rank Stochastic Gradient Descent Methods for Solving Semidefinite Programs.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Improved Bounds for the Nyström Method With Application to Kernel Classification.
IEEE Trans. Inf. Theory, 2013

Online Multiple Kernel Classification.
Mach. Learn., 2013

Efficient Stochastic Gradient Descent for Strongly Convex Optimization
CoRR, 2013

A New Analysis of Compressive Sensing by Stochastic Proximal Gradient Descent
CoRR, 2013

Sparse Multiple Kernel Learning with Geometric Convergence Rate
CoRR, 2013

On Theoretical Analysis of Distributed Stochastic Dual Coordinate Ascent.
CoRR, 2013

Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Stochastic Convex Optimization with Multiple Objectives.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Directed Inference Approach towards Multi-class Multi-model Fusion.
Proceedings of the Multiple Classifier Systems, 11th International Workshop, 2013

O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions.
Proceedings of the 30th International Conference on Machine Learning, 2013

Recovering the Optimal Solution by Dual Random Projection.
Proceedings of the COLT 2013, 2013

Community detection by popularity based models for authored networked data.
Proceedings of the Advances in Social Networks Analysis and Mining 2013, 2013

2012
Trading regret for efficiency: online convex optimization with long term constraints.
J. Mach. Learn. Res., 2012

Proceedings of the COLT 2012, 2012

Influence Analysis in the Blogosphere
CoRR, 2012

Online Stochastic Optimization with Multiple Objectives
CoRR, 2012

Recovering Optimal Solution by Dual Random Projection
CoRR, 2012

An Improved Bound for the Nystrom Method for Large Eigengap
CoRR, 2012

Efficient Constrained Regret Minimization
CoRR, 2012

Learning kernel combination from noisy pairwise constraints.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2012

Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Stochastic Gradient Descent with Only One Projection.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Multiple Kernel Learning from Noisy Labels by Stochastic Programming.
Proceedings of the 29th International Conference on Machine Learning, 2012

A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound.
Proceedings of the 29th International Conference on Machine Learning, 2012

Robust Ensemble Clustering by Matrix Completion.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Online Kernel Selection: Algorithms and Evaluations.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
Detecting communities and their evolutions in dynamic social networks - a Bayesian approach.
Mach. Learn., 2011

Regret Bound by Variation for Online Convex Optimization
CoRR, 2011

Improved Bound for the Nystrom's Method and its Application to Kernel Classification
CoRR, 2011

A kernel density based approach for large scale image retrieval.
Proceedings of the 1st International Conference on Multimedia Retrieval, 2011

Online AUC Maximization.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Directed Network Community Detection: A Popularity and Productivity Link Model.
Proceedings of the SIAM International Conference on Data Mining, 2010

Unsupervised transfer classification: application to text categorization.
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

Learning from Noisy Side Information by Generalized Maximum Entropy Model.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Online Multiple Kernel Learning: Algorithms and Mistake Bounds.
Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010

2009
A Bayesian Framework for Community Detection Integrating Content and Link.
Proceedings of the UAI 2009, 2009

A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks.
Proceedings of the SIAM International Conference on Data Mining, 2009

Combining link and content for community detection: a discriminative approach.
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009