# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Published:

## Deep Reinforcement Learning: Policy Gradient and Actor-Critic

Published:

In this post, we review the basic policy gradient algorithm for deep reinforcement learning and the actor-critic algorithm. Most of the contents are derived from CS 285 at UC Berkeley. Read more

## Theory of Optimization: More on Mirror Descent

Published:

In this post, we will continue on our discuss of mirror descent. We will present a variant of mirror descent: the lazy mirror descent, also known as Nesterov’s dual averaging. Read more

## Theory of Optimization: Frank-Wolfe Algorithm

Published:

In this post, we describe a new geometry dependent algorithm that relies on different set of assumptions. The algorithm is called conditional gradient descent, aka Frank-Wolfe. Read more

## Theory of Optimization: Mirror Descent

Published:

In this post, we will introduce the Mirror Descent algorithm that solves the convex optimization algorithm. Read more

## Theory of Optimization: Projected (Sub)Gradient Descent

Published:

In this post, we will continue our analysis for gradient descent. Different from the previous post, we will not assume that the function is smooth. We will only assume that the function is convex and has some Lipschitz constant. Read more

## Theory of Optimization: Gradient Descent

Published:

In this post, we will review the most basic and the most intuitive optimization method – the gradient decent method – in optimization. Read more

## Theory of Optimization: Preliminaries and Basic Properties

Published:

Recently, I find an interesting course taught by Prof. Yin Tat Lee at UW. The course is called `Theory of Optimization and Continuous Algorithms’, and the lecture notes are available under the homepage of this courseuw-cse535-winter19. As a great fan of optimization theory and algorithm design, I think I will follow this course and write a bunch of blogs to record my study of this course. Most of the materials in this series of blogs will follow the lecture notes of the course, and and interesting optimization book Convex Optimization: Algorithms and Complexity by Sebastien Bubeck. Since this is the first blog about this course, I will present the preliminaries of the optimization theory, and some basic knowledge about convex optimization, including some basic properties of convex functions. Read more

## Portfolio item number 1

Short description of portfolio item number 1

## Portfolio item number 2

Short description of portfolio item number 2

## An FPTAS for Stochastic Unbounded Min-Knapsack Problem

Published in International Frontiers of Algorithmics Workshop, 2019

## Stochastic One-Sided Full-Information Bandit

Published in The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019

## Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations

Published in AAAI Conference on Artificial Intelligence, 2019

## Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting

Published in AAAI Conference on Artificial Intelligence, 2019

## Mildly Overparametrized Neural Nets can Memorize Training Data Efficiently

Published in , 2019

## Combinatorial Pure Exploration of Dueling Bandit

Published in International Conference on Machine Learning, 2020

## Combinatorial Semi-Bandit in the Non-Stationary Environment

Published in The Conference on Uncertainty in Artificial Intelligence, 2021

## FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning

Published in , 2021

## BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression

Published in Conference on Neural Information Processing Systems, 2022

## SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

Published in Conference on Neural Information Processing Systems, 2022

## Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering

Published in Conference on Neural Information Processing Systems, 2022

## Task-Specific Skill Localization in Fine-tuned Language Models

Published in International Conference on Machine Learning, 2023

## Faster Rates for Compressed Federated Learning with Client-Variance Reduction

Published in SIAM Journal on Mathematics of Data Science, 2023

## Do Transformers Parse while Predicting the Masked Word?

Published in Conference on Empirical Methods in Natural Language Processing, 2023

## Oral presentation at ECML/PKDD 2019

Published:

In this talk, I presented my work with Prof. Wei Chen @MSRA on our paper Stochastic One-Sided Full-Information Bandit. The paper can be downloaded here. Read more

## Oral presentation at AAAI 2020

Published:

In this talk, I presented my work with Prof. Wei Chen @MSRA on our paper Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting. Because of the virus in China, I cannot go the the AAAI main conference, and I will give my oral presentation remotely. The paper can be downloaded here. The PPT is available at here. Read more

## Presentation at FLOW: Federated Learning One World Seminar

Published:

In this talk, I presented my work with Zhize Li @KAUST and Peter Richtárik on our paper FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning. Read more

## Video presentation at NeurIPS 2022

Published:

In this talk, I use 5 minutes to present our paper BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. You can visit my talk online here. Read more

## Video presentation at NeurIPS 2022

Published:

In this talk, I use 5 minutes to present our paper Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering. You can visit my talk online here Read more

## Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

## Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.