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Found 22 papers, 6 papers with code
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
Up to a logarithmic dependence on the size of the state space, Lin-Confident-FTRL learns $\epsilon$-CCE with a provable optimal accuracy bound $O(\epsilon^{-2})$ and gets rids of the linear dependency on the action space, while scaling polynomially with relevant problem parameters (such as the number of agents and time horizon).
Online Tensor Learning: Computational and Statistical Trade-offs, Adaptivity and Optimal Regret
Under the fixed step size regime, a fascinating trilemma concerning the convergence rate, statistical error rate, and regret is observed.
Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression
The algorithm is not only computationally efficient with linear convergence but also statistically optimal, be the noise Gaussian or heavy-tailed with a finite 1 + epsilon moment.
Computationally Efficient and Statistically Optimal Robust Low-rank Matrix and Tensor Estimation
Lastly, RsGrad is applicable for low-rank tensor estimation under heavy-tailed noise where a statistically optimal rate is attainable with the same phenomenon of dual-phase convergence, and a novel shrinkage-based second-order moment method is guaranteed to deliver a warm initialization.
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
In this paper, we provide, to our best knowledge, the first theoretical guarantees of the convergence of RGrad algorithm for TT-format tensor completion, under a nearly optimal sample size condition.
Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal Recovery
We study the robust recovery problem for the spectrally sparse signal under the fully observed setting, which is about recovering $\boldsymbol{x}$ and a sparse corruption vector $\boldsymbol{s}$ from their sum $\boldsymbol{z}=\boldsymbol{x}+\boldsymbol{s}$.
A stochastic alternating minimizing method for sparse phase retrieval
Sparse phase retrieval plays an important role in many fields of applied science and thus attracts lots of attention.
Optimal low rank tensor recovery
We investigate the sample size requirement for exact recovery of a high order tensor of low rank from a subset of its entries.
Fast Single Image Reflection Suppression via Convex Optimization
Removing undesired reflections from images taken through the glass is of great importance in computer vision.
Enhanced Expressive Power and Fast Training of Neural Networks by Random Projections
The key in our proof is that random projections embed stably the set of sparse vectors or a low-dimensional smooth manifold into a low-dimensional subspace.
Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification
We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising.
Computation Image and Video Processing
Accelerated Alternating Projections for Robust Principal Component Analysis
We study robust PCA for the fully observed setting, which is about separating a low rank matrix $\boldsymbol{L}$ and a sparse matrix $\boldsymbol{S}$ from their sum $\boldsymbol{D}=\boldsymbol{L}+\boldsymbol{S}$.
Separation-Free Super-Resolution from Compressed Measurements is Possible: an Orthonormal Atomic Norm Minimization Approach
However, it is known that in order for TV minimization and atomic norm minimization to recover the missing data or the frequencies, the underlying $R$ frequencies are required to be well-separated, even when the measurements are noiseless.
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.
Precise Phase Transition of Total Variation Minimization
Characterizing the phase transitions of convex optimizations in recovering structured signals or data is of central importance in compressed sensing, machine learning and statistics.
Projected Wirtinger Gradient Descent for Low-Rank Hankel Matrix Completion in Spectral Compressed Sensing
This paper considers reconstructing a spectrally sparse signal from a small number of randomly observed time-domain samples.
Projected Iterative Soft-thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
It has been shown that, redundant image representations, e. g. tight frames, can significantly improve the image quality.
Robust recovery of complex exponential signals from random Gaussian projections via low rank Hankel matrix reconstruction
Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of $R$ complex sinusoids.
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods.
Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional ($d\geq 2$) Off-the-Grid Frequencies
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous.
Guarantees of Total Variation Minimization for Signal Recovery
In this paper, we consider using total variation minimization to recover signals whose gradients have a sparse support, from a small number of measurements.
A Singular Value Thresholding Algorithm for Matrix Completion
Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries.
Optimization and Control
