Bingcong Li | DeepAI

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∙ 08/13/2023

Conic Descent Redux for Memory-Efficient Optimization

Conic programming has well-documented merits in a gamut of signal proces...

0 Bingcong Li, et al. ∙

∙ 03/31/2023

Scalable Bayesian Meta-Learning through Generalized Implicit Gradients

Meta-learning owns unique effectiveness and swiftness in tackling emergi...

0 Yilang Zhang, et al. ∙

∙ 05/27/2022

Surrogate modeling for Bayesian optimization beyond a single Gaussian process

Bayesian optimization (BO) has well-documented merits for optimizing bla...

0 Qin Lu, et al. ∙

∙ 10/20/2021

Distributionally Robust Semi-Supervised Learning Over Graphs

Semi-supervised learning (SSL) over graph-structured data emerges in man...

4 Alireza Sadeghi, et al. ∙

∙ 10/08/2021

Heavy Ball Momentum for Conditional Gradient

Conditional gradient, aka Frank Wolfe (FW) algorithms, have well-documen...

6 Bingcong Li, et al. ∙

∙ 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...

0 Yelin He, et al. ∙

∙ 12/10/2020

Adversarial Linear Contextual Bandits with Graph-Structured Side Observations

This paper studies the adversarial graphical contextual bandits, a varia...

6 Lingda Wang, et al. ∙

∙ 12/09/2020

Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem

Aiming at convex optimization under structural constraints, this work in...

6 Bingcong Li, et al. ∙

∙ 11/06/2020

Confusable Learning for Large-class Few-Shot Classification

Few-shot image classification is challenging due to the lack of ample sa...

0 Bingcong Li, et al. ∙

∙ 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...

0 Bingcong Li, et al. ∙

∙ 06/19/2020

How Does Momentum Help Frank Wolfe?

We unveil the connections between Frank Wolfe (FW) type algorithms and t...

0 Bingcong Li, et al. ∙

∙ 10/15/2019

Adaptive Step Sizes in Variance Reduction via Regularization

The main goal of this work is equipping convex and nonconvex problems wi...

30 Bingcong Li, et al. ∙

∙ 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) ...

0 Lingda Wang, et al. ∙

∙ 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...

13 Gang Wang, et al. ∙

∙ 08/25/2019

Almost Tune-Free Variance Reduction

The variance reduction class of algorithms including the representative ...

2 Bingcong Li, et al. ∙

∙ 06/05/2019

On the Convergence of SARAH and Beyond

The main theme of this work is a unifying algorithm, abbreviated as L2S,...

1 Bingcong Li, et al. ∙

∙ 05/24/2019

Tight Linear Convergence Rate of ADMM for Decentralized Optimization

The present paper considers leveraging network topology information to i...

0 Meng Ma, et al. ∙

∙ 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...

0 Miao Xu, et al. ∙

∙ 07/09/2018

Delayed Bandit Online Learning with Unknown Delays

This paper studies bandit learning problems with delayed feedback, which...

2 Bingcong Li, et al. ∙

∙ 05/09/2018

Secure Mobile Edge Computing in IoT via Collaborative Online Learning

To accommodate heterogeneous tasks in Internet of Things (IoT), a new co...

0 Bingcong Li, et al. ∙

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