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Found 24 papers, 5 papers with code
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers
Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data across various sectors.
Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse Problems
The proposed algorithm, termed ProjDiff, effectively harnesses the prior information and the denoising capability of a pre-trained diffusion model within the optimization framework.
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector
Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks.
Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection
Traditional Time-series Anomaly Detection (TAD) methods often struggle with the composite nature of complex time-series data and a diverse array of anomalies.
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distributed optimization.
SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
To bridge this gap, this paper introduces a novel series-aware framework, explicitly designed to emphasize the significance of such dependencies.
Unlocking the Potential of Deep Learning in Peak-Hour Series Forecasting
Unlocking the potential of deep learning in Peak-Hour Series Forecasting (PHSF) remains a critical yet underexplored task in various domains.
Provable Identifiability of ReLU Neural Networks via Lasso Regularization
In this paper, the territory of LASSO is extended to the neural network model, a fashionable and powerful nonlinear regression model.
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
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").
THE EFFICACY OF L1 REGULARIZATION IN NEURAL NETWORKS
A crucial problem in neural networks is to select the most appropriate number of hidden neurons and obtain tight statistical risk bounds.
The Efficacy of $L_1$ Regularization in Two-Layer Neural Networks
A crucial problem in neural networks is to select the most appropriate number of hidden neurons and obtain tight statistical risk bounds.
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
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.
Grid-less Variational Direction of Arrival Estimation in Heteroscedastic Noise Environment
This paper studies DOA in heteroscedastic noise (HN) environment, where the variance of noise is varied across the snapshots and the antennas.
Theory of Spectral Method for Union of Subspaces-Based Random Geometry Graph
Spectral Method is a commonly used scheme to cluster data points lying close to Union of Subspaces by first constructing a Random Geometry Graph, called Subspace Clustering.
Compressed Subspace Learning Based on Canonical Angle Preserving Property
In this paper, we prove that random projection with the so-called Johnson-Lindenstrauss (JL) property approximately preserves canonical angles between subspaces with overwhelming probability.
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces
Subspace Restricted Isometry Property, a newly-proposed concept, has proved to be a useful tool in analyzing the effect of dimensionality reduction algorithms on subspaces.
Rigorous Restricted Isometry Property of Low-Dimensional Subspaces
As an analogy to JL Lemma and RIP for sparse vectors, this work allows the use of random projections to reduce the ambient dimension with the theoretical guarantee that the distance between subspaces after compression is well preserved.
Linear Convergence of An Iterative Phase Retrieval Algorithm with Data Reuse
In this work, we study for the first time, without the independence assumption, the convergence behavior of the randomized Kaczmarz method for phase retrieval.
Active Orthogonal Matching Pursuit for Sparse Subspace Clustering
In this letter, we propose a novel Active OMP-SSC, which improves clustering accuracy of OMP-SSC by adaptively updating data points and randomly dropping data points in the OMP process, while still enjoying the low computational complexity of greedy pursuit algorithms.
Nonconvex Sparse Logistic Regression with Weakly Convex Regularization
In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem.
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization
This paper addresses image classification through learning a compact and discriminative dictionary efficiently.
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces
Dimension reduction plays an essential role when decreasing the complexity of solving large-scale problems.
Disciplined Multi-Convex Programming
A multi-convex optimization problem is one in which the variables can be partitioned into sets over which the problem is convex when the other variables are fixed.
Optimization and Control
Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints
Consider convex optimization problems subject to a large number of constraints.
