Search Results for author: Yuejie Chi

Found 77 papers, 18 papers with code

Learning Discrete Concepts in Latent Hierarchical Models

no code implementations1 Jun 2024 Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang

Learning concepts from natural high-dimensional data (e. g., images) holds potential in building human-aligned and interpretable machine learning models.

Interpretable Machine Learning

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

no code implementations29 May 2024 Shicong Cen, Jincheng Mei, Katayoon Goshvadi, Hanjun Dai, Tong Yang, Sherry Yang, Dale Schuurmans, Yuejie Chi, Bo Dai

A key bottleneck is understanding how to incorporate uncertainty estimation in the reward function learned from the preference data for RLHF, regardless of how the preference data is collected.

reinforcement-learning Reinforcement Learning (RL) +1

Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty

no code implementations29 Apr 2024 Laixi Shi, Eric Mazumdar, Yuejie Chi, Adam Wierman

To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

Prompt-prompted Mixture of Experts for Efficient LLM Generation

1 code implementation1 Apr 2024 Harry Dong, Beidi Chen, Yuejie Chi

With the development of transformer-based large language models (LLMs), they have been applied to many fields due to their remarkable utility, but this comes at a considerable computational cost at deployment.

Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction

1 code implementation25 Mar 2024 Xingyu Xu, Yuejie Chi

This work develops an algorithmic framework for employing score-based diffusion models as an expressive data prior in general nonlinear inverse problems.

Denoising Image Reconstruction

Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes

no code implementations19 Mar 2024 He Wang, Laixi Shi, Yuejie Chi

In offline reinforcement learning (RL), the absence of active exploration calls for attention on the model robustness to tackle the sim-to-real gap, where the discrepancy between the simulated and deployed environments can significantly undermine the performance of the learned policy.

Reinforcement Learning (RL)

Accelerating Convergence of Score-Based Diffusion Models, Provably

no code implementations6 Mar 2024 Gen Li, Yu Huang, Timofey Efimov, Yuting Wei, Yuejie Chi, Yuxin Chen

Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampling phase.

How Transformers Learn Diverse Attention Correlations in Masked Vision Pretraining

no code implementations4 Mar 2024 Yu Huang, Zixin Wen, Yuejie Chi, Yingbin Liang

Masked reconstruction, which predicts randomly masked patches from unmasked ones, has emerged as an important approach in self-supervised pretraining.

Position

Counterfactual Generation with Identifiability Guarantees

1 code implementation NeurIPS 2023 Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric Xing, Yulan He, Kun Zhang

In this work, we tackle the domain-varying dependence between the content and the style variables inherent in the counterfactual generation task.

counterfactual Style Transfer +1

Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices

no code implementations8 Feb 2024 Jiin Woo, Laixi Shi, Gauri Joshi, Yuejie Chi

Our sample complexity analysis reveals that, with appropriately chosen parameters and synchronization schedules, FedLCB-Q achieves linear speedup in terms of the number of agents without requiring high-quality datasets at individual agents, as long as the local datasets collectively cover the state-action space visited by the optimal policy, highlighting the power of collaboration in the federated setting.

Federated Learning Offline RL +3

Beyond Expectations: Learning with Stochastic Dominance Made Practical

no code implementations5 Feb 2024 Shicong Cen, Jincheng Mei, Hanjun Dai, Dale Schuurmans, Yuejie Chi, Bo Dai

Stochastic dominance models risk-averse preferences for decision making with uncertain outcomes, which naturally captures the intrinsic structure of the underlying uncertainty, in contrast to simply resorting to the expectations.

Decision Making Portfolio Optimization

Communication-Efficient Federated Optimization over Semi-Decentralized Networks

no code implementations30 Nov 2023 He Wang, Yuejie Chi

In large-scale federated and decentralized learning, communication efficiency is one of the most challenging bottlenecks.

Federated Natural Policy Gradient Methods for Multi-task Reinforcement Learning

no code implementations1 Nov 2023 Tong Yang, Shicong Cen, Yuting Wei, Yuxin Chen, Yuejie Chi

Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories.

Decision Making Policy Gradient Methods +2

Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression

no code implementations29 Oct 2023 Sijin Chen, Zhize Li, Yuejie Chi

To our knowledge, Power-EF is the first distributed and compressed SGD algorithm that provably escapes saddle points in heterogeneous FL without any data homogeneity assumptions.

Federated Learning

Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization

no code implementations9 Oct 2023 Cong Ma, Xingyu Xu, Tian Tong, Yuejie Chi

Many problems encountered in science and engineering can be formulated as estimating a low-rank object (e. g., matrices and tensors) from incomplete, and possibly corrupted, linear measurements.

Object

Global Convergence of Policy Gradient Methods in Reinforcement Learning, Games and Control

no code implementations8 Oct 2023 Shicong Cen, Yuejie Chi

Policy gradient methods, where one searches for the policy of interest by maximizing the value functions using first-order information, become increasingly popular for sequential decision making in reinforcement learning, games, and control.

Decision Making Policy Gradient Methods +1

A Lightweight Transformer for Faster and Robust EBSD Data Collection

1 code implementation18 Aug 2023 Harry Dong, Sean Donegan, Megna Shah, Yuejie Chi

Three dimensional electron back-scattered diffraction (EBSD) microscopy is a critical tool in many applications in materials science, yet its data quality can fluctuate greatly during the arduous collection process, particularly via serial-sectioning.

Offline Reinforcement Learning with On-Policy Q-Function Regularization

no code implementations25 Jul 2023 Laixi Shi, Robert Dadashi, Yuejie Chi, Pablo Samuel Castro, Matthieu Geist

In this work, we propose to regularize towards the Q-function of the behavior policy instead of the behavior policy itself, under the premise that the Q-function can be estimated more reliably and easily by a SARSA-style estimate and handles the extrapolation error more straightforwardly.

D4RL reinforcement-learning +1

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

no code implementations15 Jun 2023 Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi

Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling.

Denoising

High-probability sample complexities for policy evaluation with linear function approximation

no code implementations30 May 2023 Gen Li, Weichen Wu, Yuejie Chi, Cong Ma, Alessandro Rinaldo, Yuting Wei

This paper is concerned with the problem of policy evaluation with linear function approximation in discounted infinite horizon Markov decision processes.

The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model

no code implementations NeurIPS 2023 Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Matthieu Geist, Yuejie Chi

Assuming access to a generative model that draws samples based on the nominal MDP, we characterize the sample complexity of RMDPs when the uncertainty set is specified via either the total variation (TV) distance or $\chi^2$ divergence.

Reinforcement Learning (RL)

The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond

no code implementations18 May 2023 Jiin Woo, Gauri Joshi, Yuejie Chi

When the data used for reinforcement learning (RL) are collected by multiple agents in a distributed manner, federated versions of RL algorithms allow collaborative learning without the need for agents to share their local data.

Q-Learning Reinforcement Learning (RL)

Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression

no code implementations17 May 2023 Boyue Li, Yuejie Chi

Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design.

The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing

no code implementations2 Feb 2023 Xingyu Xu, Yandi Shen, Yuejie Chi, Cong Ma

We propose $\textsf{ScaledGD($\lambda$)}$, a preconditioned gradient descent method to tackle the low-rank matrix sensing problem when the true rank is unknown, and when the matrix is possibly ill-conditioned.

Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods

no code implementations30 Jan 2023 Gen Li, Yanxi Chen, Yuejie Chi, H. Vincent Poor, Yuxin Chen

Efficient computation of the optimal transport distance between two distributions serves as an algorithm subroutine that empowers various applications.

Deep Unfolded Tensor Robust PCA with Self-supervised Learning

1 code implementation21 Dec 2022 Harry Dong, Megna Shah, Sean Donegan, Yuejie Chi

Tensor robust principal component analysis (RPCA), which seeks to separate a low-rank tensor from its sparse corruptions, has been crucial in data science and machine learning where tensor structures are becoming more prevalent.

Bayesian Optimization Self-Supervised Learning

Asynchronous Gradient Play in Zero-Sum Multi-agent Games

no code implementations16 Nov 2022 Ruicheng Ao, Shicong Cen, Yuejie Chi

Moving beyond, we demonstrate entropy-regularized OMWU -- by adopting two-timescale learning rates in a delay-aware manner -- enjoys faster last-iterate convergence under fixed delays, and continues to converge provably even when the delays are arbitrarily bounded in an average-iterate manner.

Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games

no code implementations3 Oct 2022 Shicong Cen, Yuejie Chi, Simon S. Du, Lin Xiao

Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to interact in a shared dynamic environment -- permeates across a wide range of critical applications.

Multi-agent Reinforcement Learning

Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model

no code implementations22 Aug 2022 Gen Li, Yuejie Chi, Yuting Wei, Yuxin Chen

This paper studies multi-agent reinforcement learning in Markov games, with the goal of learning Nash equilibria or coarse correlated equilibria (CCE) sample-optimally.

Multi-agent Reinforcement Learning

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity

no code implementations11 Aug 2022 Laixi Shi, Yuejie Chi

This paper concerns the central issues of model robustness and sample efficiency in offline reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration.

Decision Making Offline RL +2

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

1 code implementation20 Jun 2022 Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi

We then propose a unified framework SoteriaFL for private federated learning, which accommodates a general family of local gradient estimators including popular stochastic variance-reduced gradient methods and the state-of-the-art shifted compression scheme.

Federated Learning Privacy Preserving

Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent

1 code implementation18 Jun 2022 Harry Dong, Tian Tong, Cong Ma, Yuejie Chi

An increasing number of data science and machine learning problems rely on computation with tensors, which better capture the multi-way relationships and interactions of data than matrices.

Independent Natural Policy Gradient Methods for Potential Games: Finite-time Global Convergence with Entropy Regularization

no code implementations12 Apr 2022 Shicong Cen, Fan Chen, Yuejie Chi

We show that the proposed method converges to the quantal response equilibrium (QRE) -- the equilibrium to the entropy-regularized game -- at a sublinear rate, which is independent of the size of the action space and grows at most sublinearly with the number of agents.

Autonomous Vehicles Policy Gradient Methods

Settling the Sample Complexity of Model-Based Offline Reinforcement Learning

no code implementations11 Apr 2022 Gen Li, Laixi Shi, Yuxin Chen, Yuejie Chi, Yuting Wei

We demonstrate that the model-based (or "plug-in") approach achieves minimax-optimal sample complexity without burn-in cost for tabular Markov decision processes (MDPs).

Offline RL reinforcement-learning +1

Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity

no code implementations28 Feb 2022 Laixi Shi, Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi

Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment.

Offline RL Q-Learning +2

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

1 code implementation31 Jan 2022 Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtárik, Yuejie Chi

Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments.

Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning

no code implementations NeurIPS 2021 Gen Li, Laixi Shi, Yuxin Chen, Yuejie Chi

Achieving sample efficiency in online episodic reinforcement learning (RL) requires optimally balancing exploration and exploitation.

Q-Learning reinforcement-learning +1

DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization

1 code implementation4 Oct 2021 Boyue Li, Zhize Li, Yuejie Chi

Emerging applications in multi-agent environments such as internet-of-things, networked sensing, autonomous systems and federated learning, call for decentralized algorithms for finite-sum optimizations that are resource-efficient in terms of both computation and communication.

Federated Learning

Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization

no code implementations NeurIPS 2021 Shicong Cen, Yuting Wei, Yuejie Chi

Motivated by the algorithmic role of entropy regularization in single-agent reinforcement learning and game theory, we develop provably efficient extragradient methods to find the quantal response equilibrium (QRE) -- which are solutions to zero-sum two-player matrix games with entropy regularization -- at a linear rate.

Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence

no code implementations24 May 2021 Wenhao Zhan, Shicong Cen, Baihe Huang, Yuxin Chen, Jason D. Lee, Yuejie Chi

These can often be accounted for via regularized RL, which augments the target value function with a structure-promoting regularizer.

Reinforcement Learning (RL)

Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting

no code implementations NeurIPS 2021 Gen Li, Yuxin Chen, Yuejie Chi, Yuantao Gu, Yuting Wei

The current paper pertains to a scenario with value-based linear representation, which postulates the linear realizability of the optimal Q-function (also called the "linear $Q^{\star}$ problem").

reinforcement-learning Reinforcement Learning (RL)

Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements

1 code implementation29 Apr 2021 Tian Tong, Cong Ma, Ashley Prater-Bennette, Erin Tripp, Yuejie Chi

Tensors, which provide a powerful and flexible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across various fields in science and engineering.

Attribute

A Large Collection of Real-world Pediatric Sleep Studies

1 code implementation26 Feb 2021 Harlin Lee, Boyue Li, Shelly DeForte, Mark Splaingard, Yungui Huang, Yuejie Chi, Simon Lin Linwood

Despite being crucial to health and quality of life, sleep -- especially pediatric sleep -- is not yet well understood.

Softmax Policy Gradient Methods Can Take Exponential Time to Converge

no code implementations22 Feb 2021 Gen Li, Yuting Wei, Yuejie Chi, Yuxin Chen

The softmax policy gradient (PG) method, which performs gradient ascent under softmax policy parameterization, is arguably one of the de facto implementations of policy optimization in modern reinforcement learning.

Policy Gradient Methods

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis

no code implementations12 Feb 2021 Gen Li, Changxiao Cai, Yuxin Chen, Yuting Wei, Yuejie Chi

This paper addresses these questions for the synchronous setting: (1) when $|\mathcal{A}|=1$ (so that Q-learning reduces to TD learning), we prove that the sample complexity of TD learning is minimax optimal and scales as $\frac{|\mathcal{S}|}{(1-\gamma)^3\varepsilon^2}$ (up to log factor); (2) when $|\mathcal{A}|\geq 2$, we settle the sample complexity of Q-learning to be on the order of $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^4\varepsilon^2}$ (up to log factor).

Natural Questions Q-Learning

Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing

no code implementations13 Jan 2021 Cong Ma, Yuanxin Li, Yuejie Chi

Low-rank matrix estimation plays a central role in various applications across science and engineering.

Spectral Methods for Data Science: A Statistical Perspective

no code implementations15 Dec 2020 Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma

While the studies of spectral methods can be traced back to classical matrix perturbation theory and methods of moments, the past decade has witnessed tremendous theoretical advances in demystifying their efficacy through the lens of statistical modeling, with the aid of non-asymptotic random matrix theory.

Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number

2 code implementations26 Oct 2020 Tian Tong, Cong Ma, Yuejie Chi

Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations.

Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization

no code implementations13 Jul 2020 Shicong Cen, Chen Cheng, Yuxin Chen, Yuting Wei, Yuejie Chi

This class of methods is often applied in conjunction with entropy regularization -- an algorithmic scheme that encourages exploration -- and is closely related to soft policy iteration and trust region policy optimization.

Policy Gradient Methods

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction

no code implementations NeurIPS 2020 Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen

Focusing on a $\gamma$-discounted MDP with state space $\mathcal{S}$ and action space $\mathcal{A}$, we demonstrate that the $\ell_{\infty}$-based sample complexity of classical asynchronous Q-learning --- namely, the number of samples needed to yield an entrywise $\varepsilon$-accurate estimate of the Q-function --- is at most on the order of $\frac{1}{\mu_{\min}(1-\gamma)^5\varepsilon^2}+ \frac{t_{mix}}{\mu_{\min}(1-\gamma)}$ up to some logarithmic factor, provided that a proper constant learning rate is adopted.

Q-Learning

Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent

2 code implementations18 May 2020 Tian Tong, Cong Ma, Yuejie Chi

Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science.

Matrix Completion

Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently

no code implementations25 Nov 2019 Laixi Shi, Yuejie Chi

Multi-channel sparse blind deconvolution, or convolutional sparse coding, refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse.

Subspace Estimation from Unbalanced and Incomplete Data Matrices: $\ell_{2,\infty}$ Statistical Guarantees

no code implementations9 Oct 2019 Changxiao Cai, Gen Li, Yuejie Chi, H. Vincent Poor, Yuxin Chen

This paper is concerned with estimating the column space of an unknown low-rank matrix $\boldsymbol{A}^{\star}\in\mathbb{R}^{d_{1}\times d_{2}}$, given noisy and partial observations of its entries.

Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction

1 code implementation12 Sep 2019 Boyue Li, Shicong Cen, Yuxin Chen, Yuejie Chi

There is growing interest in large-scale machine learning and optimization over decentralized networks, e. g. in the context of multi-agent learning and federated learning.

Distributed Optimization Federated Learning

Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data

no code implementations29 May 2019 Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, Tie-Yan Liu

Our theory captures how the convergence of distributed algorithms behaves as the number of machines and the size of local data vary.

Vector-Valued Graph Trend Filtering with Non-Convex Penalties

1 code implementation29 May 2019 Rohan Varma, Harlin Lee, Jelena Kovačević, Yuejie Chi

This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued.

Denoising Event Detection +1

Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

no code implementations20 Feb 2019 Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma, Yuling Yan

This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently.

Low-Rank Matrix Completion

Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

no code implementations25 Sep 2018 Yuejie Chi, Yue M. Lu, Yuxin Chen

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization.

Matrix Completion Retrieval

Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion

no code implementations ICML 2018 Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen

Focusing on two statistical estimation problems, i. e. solving random quadratic systems of equations and low-rank matrix completion, we establish that gradient descent achieves near-optimal statistical and computational guarantees without explicit regularization.

Low-Rank Matrix Completion Retrieval

Streaming PCA and Subspace Tracking: The Missing Data Case

no code implementations12 Jun 2018 Laura Balzano, Yuejie Chi, Yue M. Lu

This survey article reviews a variety of classical and recent algorithms for solving this problem with low computational and memory complexities, particularly those applicable in the big data regime with missing data.

Decision Making

Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval

no code implementations21 Mar 2018 Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma

This paper considers the problem of solving systems of quadratic equations, namely, recovering an object of interest $\mathbf{x}^{\natural}\in\mathbb{R}^{n}$ from $m$ quadratic equations/samples $y_{i}=(\mathbf{a}_{i}^{\top}\mathbf{x}^{\natural})^{2}$, $1\leq i\leq m$.

Retrieval

Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation

no code implementations23 Feb 2018 Yudong Chen, Yuejie Chi

Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative filtering, video surveillance, medical imaging, to dimensionality reduction and adaptive filtering.

Collaborative Filtering Dimensionality Reduction

Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy

no code implementations ICLR 2019 Haoyu Fu, Yuejie Chi, Yingbin Liang

We prove that with Gaussian inputs, the empirical risk based on cross entropy exhibits strong convexity and smoothness {\em uniformly} in a local neighborhood of the ground truth, as soon as the sample complexity is sufficiently large.

Nonconvex Matrix Factorization from Rank-One Measurements

no code implementations17 Feb 2018 Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi

We consider the problem of recovering low-rank matrices from random rank-one measurements, which spans numerous applications including covariance sketching, phase retrieval, quantum state tomography, and learning shallow polynomial neural networks, among others.

Quantum State Tomography Retrieval

Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent

no code implementations23 Sep 2017 Yuanxin Li, Yuejie Chi, Huishuai Zhang, Yingbin Liang

Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent manner when initialized properly.

Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations

1 code implementation25 May 2016 Huishuai Zhang, Yi Zhou, Yingbin Liang, Yuejie Chi

We further develop the incremental (stochastic) reshaped Wirtinger flow (IRWF) and show that IRWF converges linearly to the true signal.

Retrieval

Median-Truncated Nonconvex Approach for Phase Retrieval with Outliers

no code implementations11 Mar 2016 Huishuai Zhang, Yuejie Chi, Yingbin Liang

This paper investigates the phase retrieval problem, which aims to recover a signal from the magnitudes of its linear measurements.

Retrieval

Subspace Learning From Bits

no code implementations23 Jul 2014 Yuejie Chi, Haoyu Fu

Networked sensing, where the goal is to perform complex inference using a large number of inexpensive and decentralized sensors, has become an increasingly attractive research topic due to its applications in wireless sensor networks and internet-of-things.

Exact and Stable Covariance Estimation from Quadratic Sampling via Convex Programming

no code implementations2 Oct 2013 Yuxin Chen, Yuejie Chi, Andrea Goldsmith

Our method admits universally accurate covariance estimation in the absence of noise, as soon as the number of measurements exceeds the information theoretic limits.

Retrieval

Robust Spectral Compressed Sensing via Structured Matrix Completion

no code implementations30 Apr 2013 Yuxin Chen, Yuejie Chi

The paper explores the problem of \emph{spectral compressed sensing}, which aims to recover a spectrally sparse signal from a small random subset of its $n$ time domain samples.

Matrix Completion Super-Resolution

Spectral Compressed Sensing via Structured Matrix Completion

no code implementations16 Apr 2013 Yuxin Chen, Yuejie Chi

The paper studies the problem of recovering a spectrally sparse object from a small number of time domain samples.

Matrix Completion Super-Resolution

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