research
∙
07/14/2023
First-order Methods for Affinely Constrained Composite Non-convex Non-smooth Problems: Lower Complexity Bound and Near-optimal Methods
Many recent studies on first-order methods (FOMs) focus on composite non...
share
research
∙
01/30/2023
Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Optimization with Non-Smooth Convex Constraints
In this paper, we consider a general non-convex constrained optimization...
research
∙
12/23/2022
Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints
As machine learning being used increasingly in making high-stakes decisi...
research
∙
11/06/2022
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
While deep reinforcement learning has proven to be successful in solving...
research
∙
07/21/2022
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization
We propose a federated learning method with weighted nodes in which the ...
research
∙
03/03/2022
Large-scale Optimization of Partial AUC in a Range of False Positive Rates
The area under the ROC curve (AUC) is one of the most widely used perfor...
research
∙
01/04/2022
Inexact accelerated proximal gradient method with line search and reduced complexity for affine-constrained and bilinear saddle-point structured convex problems
The goal of this paper is to reduce the total complexity of gradient-bas...
research
∙
02/13/2020
Sharp Analysis of Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Epoch gradient descent method (a.k.a. Epoch-GD) proposed by (Hazan and K...
research
∙
01/09/2020
Self-guided Approximate Linear Programs
Approximate linear programs (ALPs) are well-known models based on value ...
research
∙
09/23/2019
Model-Agnostic Linear Competitors – When Interpretable Models Compete and Collaborate with Black-Box Models
Driven by an increasing need for model interpretability, interpretable m...
research
∙
08/30/2019
Inexact Proximal-Point Penalty Methods for Non-Convex Optimization with Non-Convex Constraints
Non-convex optimization problems arise from various areas in science and...
research
∙
08/07/2019
A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints
Stochastic convex optimization problems with expectation constraints (SO...
research
∙
05/10/2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model
Interpretable machine learning has become a strong competitor for tradit...
research
∙
04/23/2019
Stochastic Primal-Dual Algorithms with Faster Convergence than O(1/√(T)) for Problems without Bilinear Structure
Previous studies on stochastic primal-dual algorithms for solving min-ma...
research
∙
11/28/2018
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Difference of convex (DC) functions cover a broad family of non-convex a...
research
∙
10/24/2018
Solving Weakly-Convex-Weakly-Concave Saddle-Point Problems as Weakly-Monotone Variational Inequality
In this paper, we consider first-order algorithms for solving a class of...
research
∙
10/04/2018
Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning
Min-max saddle-point problems have broad applications in many tasks in m...
research
∙
08/30/2018
A Unified Analysis of Stochastic Momentum Methods for Deep Learning
Stochastic momentum methods have been widely adopted in training deep ne...
research
∙
02/14/2018
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Inverse classification uses an induced classifier as a queryable oracle ...
research
∙
10/13/2017
DSCOVR: Randomized Primal-Dual Block Coordinate Algorithms for Asynchronous Distributed Optimization
Machine learning with big data often involves large optimization models....
research
∙
07/16/2017
Normalized Gradient with Adaptive Stepsize Method for Deep Neural Network Training
In this paper, we propose a generic and simple algorithmic framework for...
research
∙
12/21/2016
Bayesian Decision Process for Cost-Efficient Dynamic Ranking via Crowdsourcing
Rank aggregation based on pairwise comparisons over a set of items has a...
research
∙
10/05/2016
Generalized Inverse Classification
Inverse classification is the process of perturbing an instance in a mea...
research
∙
08/11/2016
A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates
This paper focuses on convex constrained optimization problems, where th...
research
∙
07/13/2016
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than O(1/ε)
In this paper, we develop a novel homoto py smoothing (HOPS) algorithm...
research
∙
07/04/2016
Accelerated Stochastic Subgradient Methods under Local Error Bound Condition
In this paper, we propose two accelerated stochastic subgradient method...
research
∙
05/29/2016
A budget-constrained inverse classification framework for smooth classifiers
Inverse classification is the process of manipulating an instance such t...
research
∙
04/12/2016
Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization
Recently, stochastic momentum methods have been widely adopted in train...
research
∙
12/09/2015
RSG: Beating Subgradient Method without Smoothness and Strong Convexity
In this paper, we study the efficiency of a Restarted Sub Gradient (RS...
research
∙
08/14/2015
Doubly Stochastic Primal-Dual Coordinate Method for Bilinear Saddle-Point Problem
We propose a doubly stochastic primal-dual coordinate optimization algor...
research
∙
03/12/2014
Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling
In crowd labeling, a large amount of unlabeled data instances are outsou...
research
∙
04/19/2013
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
We consider stochastic strongly convex optimization with a complex inequ...
research
∙
02/14/2012