research
∙
08/13/2023
Conic Descent Redux for Memory-Efficient Optimization
Conic programming has well-documented merits in a gamut of signal proces...
share
research
∙
03/31/2023
Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
Meta-learning owns unique effectiveness and swiftness in tackling emergi...
research
∙
05/27/2022
Surrogate modeling for Bayesian optimization beyond a single Gaussian process
Bayesian optimization (BO) has well-documented merits for optimizing bla...
research
∙
10/20/2021
Distributionally Robust Semi-Supervised Learning Over Graphs
Semi-supervised learning (SSL) over graph-structured data emerges in man...
research
∙
10/08/2021
Heavy Ball Momentum for Conditional Gradient
Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documen...
research
∙
05/05/2021
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Table Image Recognition to Latex
This paper presents our solution for the ICDAR 2021 Competition on Scien...
research
∙
12/10/2020
Adversarial Linear Contextual Bandits with Graph-Structured Side Observations
This paper studies the adversarial graphical contextual bandits, a varia...
research
∙
12/09/2020
Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem
Aiming at convex optimization under structural constraints, this work in...
research
∙
11/06/2020
Confusable Learning for Large-class Few-Shot Classification
Few-shot image classification is challenging due to the lack of ample sa...
research
∙
09/23/2020
Hamming OCR: A Locality Sensitive Hashing Neural Network for Scene Text Recognition
Recently, inspired by Transformer, self-attention-based scene text recog...
research
∙
06/19/2020
How Does Momentum Help Frank Wolfe?
We unveil the connections between Frank Wolfe (FW) type algorithms and t...
research
∙
10/15/2019
Adaptive Step Sizes in Variance Reduction via Regularization
The main goal of this work is equipping convex and nonconvex problems wi...
research
∙
09/12/2019
Be Aware of Non-Stationarity: Nearly Optimal Algorithms for Piecewise-Stationary Cascading Bandits
Cascading bandit (CB) is a variant of both the multi-armed bandit (MAB) ...
research
∙
09/10/2019
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation
Motivated by the widespread use of temporal-difference (TD-) and Q-learn...
research
∙
08/25/2019
Almost Tune-Free Variance Reduction
The variance reduction class of algorithms including the representative ...
research
∙
06/05/2019
On the Convergence of SARAH and Beyond
The main theme of this work is a unifying algorithm, abbreviated as L2S,...
research
∙
05/24/2019
Tight Linear Convergence Rate of ADMM for Decentralized Optimization
The present paper considers leveraging network topology information to i...
research
∙
01/29/2019
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative
In the early history of positive-unlabeled (PU) learning, the sample sel...
research
∙
07/09/2018
Delayed Bandit Online Learning with Unknown Delays
This paper studies bandit learning problems with delayed feedback, which...
research
∙
05/09/2018