Qihang Lin
29 papers · 2012–2025 · 5 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (12) π Conference Polyglot (5)
π
Renaissance Researcher
(5)
π§
Keyword Pioneer
π
Academic Marathon
(13)
π€
Dynamic Duo
(16)
π
Keyword Champion
π¬
Deep Specialist
(17)
ποΈ
Keyword Collector
(54)
π₯
Unstoppable
(12)
π
Conference Pioneer
β‘
Prolific Year
(5)
π
Trend Setter
π
Century Club
(29)
Conferences
JMLR (9)
NIPS (9)
ICML (8)
IJCAI (2)
AISTATS (1)
Top co-authors
Keywords
convex optimization
(8)
stochastic optimization
(7)
iteration complexity
(4)
convergence rate
(4)
subgradient method
(4)
first-order method
(3)
markov decision process
(3)
knowledge gradient
(3)
stochastic variance reduced gradient
(3)
budget allocation
(3)
min-max optimization
(2)
deep neural network
(2)
black-box model
(2)
linear convergence
(2)
bayesian optimization
(2)
proximal gradient method
(2)
strongly convex
(2)
stochastic convex optimization
(2)
distributed optimization
(2)
empirical risk minimization
(2)
Papers
An Adaptive Parameter-free and Projection-free Restarting Level Set Method for Constrained Convex Optimization Under the Error Bound Condition
JMLR 2025
Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization
NIPS 2023
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
AISTATS 2023
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
NIPS 2022
Large-scale Optimization of Partial AUC in a Range of False Positive Rates
NIPS 2022
Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model
JMLR 2021
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
JMLR 2021
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
ICML 2020
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
JMLR 2020
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
NIPS 2020
Bayesian Decision Process for Budget-efficient Crowdsourced Clustering
IJCAI 2020
Transparency Promotion with Model-Agnostic Linear Competitors
ICML 2020
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
JMLR 2019
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
ICML 2019
Level-Set Methods for Finite-Sum Constrained Convex Optimization
ICML 2018
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
IJCAI 2018
RSG: Beating Subgradient Method without Smoothness and Strong Convexity
JMLR 2018
Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence
ICML 2017
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
JMLR 2017
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization
NIPS 2017
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter
NIPS 2017
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
ICML 2017
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$
NIPS 2016
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
JMLR 2016
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
JMLR 2015
An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization
ICML 2014
An Accelerated Proximal Coordinate Gradient Method
NIPS 2014
Optimistic Knowledge Gradient Policy for Optimal Budget Allocation in Crowdsourcing
ICML 2013
Optimal Regularized Dual Averaging Methods for Stochastic Optimization
NIPS 2012