Search Results for author: Lingxiao Wang

Found 51 papers, 10 papers with code

Breaking the Curse of Many Agents: Provable Mean Embedding $Q$-Iteration for Mean-Field Reinforcement Learning

no code implementations ICML 2020 Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

We highlight that MF-FQI algorithm enjoys a ``blessing of many agents'' property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.

Multi-agent Reinforcement Learning reinforcement-learning +1

Federated Online and Bandit Convex Optimization

no code implementations29 Nov 2023 Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nati Sebro

Furthermore, we delve into the more challenging setting of federated online optimization with bandit (zeroth-order) feedback, where the machines can only access values of the cost functions at the queried points.

On the Effect of Defections in Federated Learning and How to Prevent Them

no code implementations28 Nov 2023 Minbiao Han, Kumar Kshitij Patel, Han Shao, Lingxiao Wang

Federated learning is a machine learning protocol that enables a large population of agents to collaborate over multiple rounds to produce a single consensus model.

Federated Learning

Generative Diffusion Models for Lattice Field Theory

no code implementations6 Nov 2023 Lingxiao Wang, Gert Aarts, Kai Zhou

This study delves into the connection between machine learning and lattice field theory by linking generative diffusion models (DMs) with stochastic quantization, from a stochastic differential equation perspective.

Quantization

Diffusion Models as Stochastic Quantization in Lattice Field Theory

no code implementations29 Sep 2023 Lingxiao Wang, Gert Aarts, Kai Zhou

In this work, we establish a direct connection between generative diffusion models (DMs) and stochastic quantization (SQ).

Quantization

Privileged Knowledge Distillation for Sim-to-Real Policy Generalization

1 code implementation29 May 2023 Haoran He, Chenjia Bai, Hang Lai, Lingxiao Wang, Weinan Zhang

In this paper, we propose a novel single-stage privileged knowledge distillation method called the Historical Information Bottleneck (HIB) to narrow the sim-to-real gap.

Knowledge Distillation Reinforcement Learning (RL)

Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks

no code implementations17 Feb 2023 Shuai Han, Lukas Stelz, Horst Stoecker, Lingxiao Wang, Kai Zhou

A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases.

Exploration in Model-based Reinforcement Learning with Randomized Reward

no code implementations9 Jan 2023 Lingxiao Wang, Ping Li

We further extend our theory to generalized function approximation and identified conditions for reward randomization to attain provably efficient exploration.

Efficient Exploration Model-based Reinforcement Learning +2

An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models

no code implementations30 Dec 2022 Yufeng Zhang, Boyi Liu, Qi Cai, Lingxiao Wang, Zhaoran Wang

In particular, such a representation instantiates the posterior distribution of the latent variable given input tokens, which plays a central role in predicting output labels and solving downstream tasks.

Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning

1 code implementation29 Jul 2022 Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang

Moreover, under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.

Contrastive Learning reinforcement-learning +3

Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency

no code implementations26 May 2022 Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

For a class of POMDPs with a low-rank structure in the transition kernel, ETC attains an $O(1/\epsilon^2)$ sample complexity that scales polynomially with the horizon and the intrinsic dimension (that is, the rank).

reinforcement-learning Reinforcement Learning (RL) +1

Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

1 code implementation ICLR 2022 Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, Zhaoran Wang

We show that such OOD sampling and pessimistic bootstrapping yields provable uncertainty quantifier in linear MDPs, thus providing the theoretical underpinning for PBRL.

D4RL Offline RL +3

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations14 Feb 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback

1 code implementation28 Dec 2021 Boxin Zhao, Lingxiao Wang, Mladen Kolar, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Chaochao Chen

As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models.

Federated Learning Stochastic Optimization

Automatic differentiation approach for reconstructing spectral functions with neural networks

no code implementations12 Dec 2021 Lingxiao Wang, Shuzhe Shi, Kai Zhou

Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in physics.

Reconstructing spectral functions via automatic differentiation

1 code implementation29 Nov 2021 Lingxiao Wang, Shuzhe Shi, Kai Zhou

Exploiting the neural networks' regularization as a non-local smoothness regulator of the spectral function, we represent spectral functions by neural networks and use the propagator's reconstruction error to optimize the network parameters unsupervisedly.

Spectral Reconstruction

False Correlation Reduction for Offline Reinforcement Learning

1 code implementation24 Oct 2021 Zhihong Deng, Zuyue Fu, Lingxiao Wang, Zhuoran Yang, Chenjia Bai, Tianyi Zhou, Zhaoran Wang, Jing Jiang

Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems.

D4RL Decision Making +3

Dynamic Bottleneck for Robust Self-Supervised Exploration

1 code implementation NeurIPS 2021 Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang

Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards.

Adaptive Differentially Private Empirical Risk Minimization

no code implementations14 Oct 2021 Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, Rebecca Willett

We propose an adaptive (stochastic) gradient perturbation method for differentially private empirical risk minimization.

A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning

no code implementations29 Sep 2021 Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha

To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture.

Inductive Bias Multi-agent Reinforcement Learning +2

Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach

no code implementations18 May 2021 Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha

To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.

Inductive Bias Multi-agent Reinforcement Learning

Principled Exploration via Optimistic Bootstrapping and Backward Induction

1 code implementation13 May 2021 Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang

In this paper, we propose a principled exploration method for DRL through Optimistic Bootstrapping and Backward Induction (OB2I).

Efficient Exploration Reinforcement Learning (RL)

Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning

no code implementations1 Jan 2021 Chenjia Bai, Lingxiao Wang, Peng Liu, Zhaoran Wang, Jianye Hao, Yingnan Zhao

However, such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning (DRL).

Atari Games Efficient Exploration +3

Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk

no code implementations30 Nov 2020 Lingxiao Wang, Tian Xu, Till Hannes Stoecker, Horst Stoecker, Yin Jiang, Kai Zhou

As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level.

BIG-bench Machine Learning

Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning

no code implementations17 Oct 2020 Chenjia Bai, Peng Liu, Kaiyu Liu, Lingxiao Wang, Yingnan Zhao, Lei Han

Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded.

Efficient Exploration reinforcement-learning +2

On the Global Optimality of Model-Agnostic Meta-Learning

no code implementations ICML 2020 Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

Model-agnostic meta-learning (MAML) formulates meta-learning as a bilevel optimization problem, where the inner level solves each subtask based on a shared prior, while the outer level searches for the optimal shared prior by optimizing its aggregated performance over all the subtasks.

Bilevel Optimization Meta-Learning

Provably Efficient Causal Reinforcement Learning with Confounded Observational Data

no code implementations NeurIPS 2021 Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

Empowered by expressive function approximators such as neural networks, deep reinforcement learning (DRL) achieves tremendous empirical successes.

Autonomous Driving reinforcement-learning +1

Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning

no code implementations21 Jun 2020 Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

We highlight that MF-FQI algorithm enjoys a "blessing of many agents" property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.

Multi-agent Reinforcement Learning reinforcement-learning +1

Revisiting Membership Inference Under Realistic Assumptions

1 code implementation21 May 2020 Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans

Since previous inference attacks fail in imbalanced prior setting, we develop a new inference attack based on the intuition that inputs corresponding to training set members will be near a local minimum in the loss function, and show that an attack that combines this with thresholds on the per-instance loss can achieve high PPV even in settings where other attacks appear to be ineffective.

Inference Attack

Improving Neural Language Generation with Spectrum Control

no code implementations ICLR 2020 Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu

Recent Transformer-based models such as Transformer-XL and BERT have achieved huge success on various natural language processing tasks.

Language Modelling Machine Translation +2

Statistical-Computational Tradeoff in Single Index Models

no code implementations NeurIPS 2019 Lingxiao Wang, Zhuoran Yang, Zhaoran Wang

Using the statistical query model to characterize the computational cost of an algorithm, we show that when $\cov(Y, X^\top\beta^*)=0$ and $\cov(Y,(X^\top\beta^*)^2)>0$, no computationally tractable algorithms can achieve the information-theoretic limit of the minimax risk.

Efficient Privacy-Preserving Stochastic Nonconvex Optimization

no code implementations30 Oct 2019 Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu

While many solutions for privacy-preserving convex empirical risk minimization (ERM) have been developed, privacy-preserving nonconvex ERM remains a challenge.

Privacy Preserving

A Knowledge Transfer Framework for Differentially Private Sparse Learning

no code implementations13 Sep 2019 Lingxiao Wang, Quanquan Gu

We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example.

regression Sparse Learning +1

Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

no code implementations ICLR 2020 Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

In detail, we prove that neural natural policy gradient converges to a globally optimal policy at a sublinear rate.

Policy Gradient Methods

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization

1 code implementation NeurIPS 2018 Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu

We explore two popular methods of differential privacy, output perturbation and gradient perturbation, and advance the state-of-the-art for both methods in the distributed learning setting.

Privacy Preserving

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization

no code implementations ICML 2018 Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu

We propose a nonconvex estimator for the covariate adjusted precision matrix estimation problem in the high dimensional regime, under sparsity constraints.

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery

no code implementations ICML 2018 Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu

We propose a primal-dual based framework for analyzing the global optimality of nonconvex low-rank matrix recovery.

Matrix Completion

Learning One-hidden-layer ReLU Networks via Gradient Descent

no code implementations20 Jun 2018 Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu

We study the problem of learning one-hidden-layer neural networks with Rectified Linear Unit (ReLU) activation function, where the inputs are sampled from standard Gaussian distribution and the outputs are generated from a noisy teacher network.

DeepDeblur: Fast one-step blurry face images restoration

no code implementations27 Nov 2017 Lingxiao Wang, Ya-Li Li, Shengjin Wang

Comprehensive experiments demonstrate that our proposed method can handle various blur kenels and achieve state-of-the-art results for small size blurry face images restoration.

Deblurring Face Recognition

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm

no code implementations ICML 2017 Rongda Zhu, Lingxiao Wang, ChengXiang Zhai, Quanquan Gu

We apply our generic algorithm to two illustrative latent variable models: Gaussian mixture model and mixture of linear regression, and demonstrate the advantages of our algorithm by both theoretical analysis and numerical experiments.

Vocal Bursts Intensity Prediction

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption

no code implementations ICML 2017 Lingxiao Wang, Quanquan Gu

In particular, we show that provided that the number of corrupted samples $n_2$ for each variable satisfies $n_2 \lesssim \sqrt{n}/\sqrt{\log d}$, where $n$ is the sample size and $d$ is the number of variables, the proposed robust precision matrix estimator attains the same statistical rate as the standard estimator for Gaussian graphical models.

Model Selection Two-sample testing

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery

no code implementations ICML 2017 Lingxiao Wang, Xiao Zhang, Quanquan Gu

We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.

Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption

no code implementations20 Apr 2017 Jinghui Chen, Lingxiao Wang, Xiao Zhang, Quanquan Gu

We consider the robust phase retrieval problem of recovering the unknown signal from the magnitude-only measurements, where the measurements can be contaminated by both sparse arbitrary corruption and bounded random noise.

Retrieval

A Unified Framework for Low-Rank plus Sparse Matrix Recovery

no code implementations21 Feb 2017 Xiao Zhang, Lingxiao Wang, Quanquan Gu

We propose a unified framework to solve general low-rank plus sparse matrix recovery problems based on matrix factorization, which covers a broad family of objective functions satisfying the restricted strong convexity and smoothness conditions.

A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery

no code implementations9 Jan 2017 Lingxiao Wang, Xiao Zhang, Quanquan Gu

We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery.

Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements

no code implementations2 Jan 2017 Xiao Zhang, Lingxiao Wang, Quanquan Gu

And in the noiseless setting, our algorithm is guaranteed to linearly converge to the unknown low-rank matrix and achieves exact recovery with optimal sample complexity.

An Aligned French-Chinese corpus of 10K segments from university educational material

no code implementations WS 2016 Ruslan Kalitvianski, Lingxiao Wang, Val{\'e}rie Bellynck, Christian Boitet

This paper describes a corpus of nearly 10K French-Chinese aligned segments, produced by post-editing machine translated computer science courseware.

Machine Translation Translation

A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation

no code implementations17 Oct 2016 Lingxiao Wang, Xiao Zhang, Quanquan Gu

In the general case with noisy observations, we show that our algorithm is guaranteed to linearly converge to the unknown low-rank matrix up to minimax optimal statistical error, provided an appropriate initial estimator.

Matrix Completion

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