New method for community detection in sparse directed SBMs with exact recovery guarantees.
problem Exact recovery in sparse directed SBMs, especially with growing communities.
method Two-stage procedure: neighborhood-smoothing followed by K-means clustering. result Exact recovery of all community labels with probability tending to one under mild sparsity and separation conditions.
IDS improves sparse linear bandits by balancing information and regret.
problem Sparse linear bandits in high-dimensional decision-making.
method Information-directed sampling (IDS) with Bayesian regret bounds and empirical Bayesian sparse posterior sampling.
result IDS nearly matches existing lower bounds and significantly reduces regret.
Is it possible to find the sparsest vector (direction) in a generic subspace S⊆Rp with dim(S)=n<p? This problem can be considered a homogeneous variant of the sparse recovery problem, and finds connections to sparse dictionary learning, sparse PCA, and many other …
New matrix reveals cluster info in sparse directed graphs.
problem Analyzing cluster information in directed graphs.
method Proposed complex non-backtracking matrix integrating Hermitian adjacency matrix and non-backtracking matrix properties.
result The complex non-backtracking matrix holds cluster information, especially for sparse directed graphs.
Survey on greedy algorithms for sparse approximation.
problem Sparse approximation using greedy algorithms.
method Greedy algorithms, Lebesgue-type inequalities, trigonometric system, tensor product structure.
result Constructive sparse approximation methods provided for various cases.
This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs".
New method computes affine normal directions efficiently for sparse polynomials.
problem Computing affine normal directions is computationally expensive in high dimensions.
method Reduces third-order tensor contraction to matrix-free formulation using log-determinant gradient.
result Scalable implementations with near-linear scaling in dimension and sparsity.
New method controls FDR for sparse GLMs, identifying positive and negative relationships.
problem Sparse GLMs with high-dimensional data and varying sample size.
method Debiased-Lasso estimator and CLIME method for precision matrix estimation.
result Asymptotically controls directional FDR and FDV for sparse GLMs.
A new pruning method finds sparse minimizers in flat regions of deep neural networks.
problem Finding sparse minimizers in flat regions of deep neural networks.
method Directional pruning using an ℓ1 proximal gradient algorithm. result Empirical results show promising sparsity (92%) and comparable minima to SGD.
Directly learns sparse changes in Markov networks without modeling individual structures.
problem Learning sparse structural changes in Markov networks.
method Directly learns sparse changes without modeling individual dense networks.
result Direct learning method provides insights into system changes.
BiSHop tackles tabular data challenges with sparse Hopfield layers.
problem Non-rotationally invariant data structure and feature sparsity in tabular data.
method Sequential column-wise and row-wise processing through interconnected directional learning modules with generalized sparse modern Hopfield layers.
result BiSHop surpasses current SOTA methods with significantly less hyperparameter tuning.
Improved COD algorithm reduces streaming AMM errors and uses less space.
problem Efficiently approximate matrix multiplication with limited memory.
method Tighter error bound for COD, space optimality, sparse matrix variant.
result Improved COD is space optimal and more efficient for sparse matrices.
Efficiently prices options in stochastic volatility models using sparse grids.
problem Option pricing in stochastic volatility models.
method Sparse grid high-order ADI scheme for second-order in time and fourth-order in space.
result Computational efficiency gains confirmed by numerical experiments.
New method discovers causal relationships in sparse linear data.
problem Discovering cause-effect relationships in sparse linear data.
method Uses structural matrix to reconstruct data and identify causal structures without independence tests.
result Outperforms existing methods in sparse causal structure recovery.
New concept of sparse regular variation for better understanding of extreme events.
problem Characterizing the dependence structure of extreme events in multivariate settings.
method Introducing sparse regular variation based on Euclidean projection onto the simplex.
result Sparse regular variation and regular variation are equivalent under mild assumptions.
Paper improves DOA estimation in sparse arrays using Siamese neural networks.
problem Challenges in DOA estimation with limited snapshots in sparse linear arrays.
method Introduces a Siamese neural network with a sparse augmentation layer for enhanced signal feature embedding.
result Demonstrates improved DOA estimation accuracy in sparse arrays.
Proposes a multivariate regression model for better analysis of multiple datasets.
problem Insufficient performance of single-dataset analysis in integrative studies.
method Sparse estimation for variable and group selection, alternating direction method of multipliers algorithm.
result Demonstrated improved performance through simulations and real data analysis.
The paper improves sparse Gaussian processes by optimizing predictive loss.
problem Optimizing predictive loss in sparse Gaussian processes.
method Direct loss minimization (DLM) for log-loss and square loss, with product sampling (uPS) and biased Monte Carlo (bMC) for non-conjugate cases.
result DLM shows significant performance improvement in both log-loss and square loss cases.
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Markov equivalent class. It is of great interest to describe and understand the space of such classes. However, with curren…
Introduces a new model for directed relationships in Gaussian data.
problem Learning directed relationships in Gaussian data.
method Developed a new directed graphical model (GGIM) from Gaussian data, leveraging stationary Gaussian processes on graphs.
result GGIMs can be framed as a LASSO problem and have a bound on the difference from the l1-norm penalized maximum log-likelihood estimate. Sparse prototypes improve clustering of high-dimensional directional data.
problem Clustering high-dimensional directional data like texts.
method Estimate a von Mises mixture using l1 penalized likelihood and EM algorithm.
result Sparse prototypes enhance interpretability and clustering performance.
A new exploration method for RL using parameter space noise.
problem Improving exploration in deep reinforcement learning.
method Switching isotropic and directional exploration in parameter space with parameter space noise.
result The proposed method achieves competitive results and better performance in sparse reward environments.
DeepFPC uses neural networks to recover sparse signals from quantized measurements.
problem Recovering sparse signals from quantized measurements.
method Unfolding the fixed-point continuation algorithm into a deep neural network.
result DeepFPC outperforms state-of-the-art algorithms in DOA estimation.
We present a novel approach to the formulation and the resolution of sparse Linear Discriminant Analysis (LDA). Our proposal, is based on penalized Optimal Scoring. It has an exact equivalence with penalized LDA, contrary to the multi-class approaches based on the regression of class indicator that have been proposed s…
Sparse RSP routing improves graph exploration and classification.
problem Optimal randomized routing and distance measures on weighted graphs.
method Tsallis divergence regularization for sparse RSP.
result Sparse random walk converges to least-cost graph as temperature decreases.
New method aggregates nodes in sparse graphical models.
problem Estimating edge-sparse graphical models.
method Tree-aggregated graphical lasso (tag-lasso) method.
result Aggregates nodes in a data-driven fashion using a tree.
New sparse CCA method finds interpretable associations in multi-view data.
problem Discovering interpretable associations in high-dimensional multi-view data.
method Inspired by sparse PCA, proposed a convex maximization program equivalent to non-convex sparse CCA formulation, using gradient method to reduce search space.
result Proposed two-step algorithm and new sparse CCA variants (Directed Sparse CCA, Multi-View sCCA) for multi-omic studies.
Sparse CCA improves classical CCA for high-dimensional data.
problem High-dimensional data limits classical CCA's effectiveness.
method Sparse CCA with l1 constraints and ADMM for efficient solution.
result Efficiently solved sparse CCA using ADMM and TFOCS.
A new PCR method using SVD with sparse regularization.
problem Lack of response variable information in traditional PCR.
method One-stage SVD approach with two loss functions and sparse regularization.
result Obtains principal component loadings with response variable information.
This work improves Hindsight Learning for goal-directed tasks in reinforcement learning.
problem Sparse reward problems in reinforcement learning, especially goal-directed tasks.
method Improves Hindsight Experience Replay and Hindsight Policy Gradients for robotic tasks.
result Improved learning in Hindsight Experience Replay and Hindsight Policy Gradients.
BLOCCS improves sparse CCA for better interpretation of multi-omics data.
problem Improving interpretation of multi-omics data.
method Block Sparse Canonical Correlation Analysis (BLOCCS) using a bi-convex objective and gradient descent.
result BLOCCS provides more interpretable solutions with improved orthogonality of sparse directions.
New method learns DAGs from data without acyclicity constraint.
problem Learning DAGs from data without imposing acyclicity.
method Sparse matrix factorization and ℓ1-penalized optimization. result Empirical success in recovering true graphs and almost-DAG graphs.
New algorithm reduces matrix multiplication time for sparse matrices.
problem Efficiently multiply large sparse matrices with limited space.
method Exploits sparsity to reduce QR decompositions and time complexity.
result Time complexity reduced to $\widetilde{O}\left((
nz(X)+
nz(Y))\ell+n\ell^2
ight)$ in expectation.
Proposes a convex method for high-dimensional sparse sliced inverse regression.
problem Difficulty in interpreting results and variability in high-dimensional settings.
method Convex formulation and linearized alternating direction methods of multiplier algorithm.
result Upper bound on the subspace distance between estimated and true subspaces.
Deep Q-learning generates directed acyclic graphs.
problem Generating DAGs with specified structures.
method Deep reinforcement learning, specifically deep Q-learning.
result Demonstrated capability of generating DAGs in sparse reward environments.
A new R package for high-dimensional regression and precision matrix estimation.
problem High-dimensional linear regression and precision matrix estimation challenges.
method flare package implements various regression methods and extensions for sparse precision matrix estimation.
result The flare package is efficient and scalable for large problems.
New pruning method for sparse additive models speeds up causal structure learning.
problem Efficiently prune spurious edges from fully-connected DAG induced by estimated topological order.
method Sparse additive models combined with randomized tree embedding and group-wise sparse regression.
result Significantly faster than existing pruning methods while maintaining comparable accuracy.
We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements. We tackle the issue of varying group size via group-sparse least absolute shrinkage selection operator (LASSO) as well as via latent group LASSO regularizations. We achieve smoothness in the signal via fusion…
Method converts sparse systems to dense ones for statistical mechanics problems.
problem Statistical mechanics on sparse graphs
method Extracts a Feedback Vertex Set, learns variational distribution, estimates free energy.
result More accurate and faster than existing methods for sparse systems.
Direct feedback alignment reduces data movement in neural networks.
problem Efficiency and energy-efficiency in training large neural networks.
method Sparse feedback matrix for local learning, reducing data movement and compute.
result Orders of magnitude improvement in data movement and 2x improvement in multiply-and-accumulate operations.
3BASiL-TM decomposes LLMs into sparse and low-rank matrices for efficient compression.
problem Efficiently compressing large language models without significant performance loss.
method 3-Block ADMM method and transformer-matching refinement step for sparse plus low-rank decomposition.
result 3BASiL-TM reduces perplexity gap by over 30% and speeds up compression by 2.5x.
This paper converts ADMM to proximal gradient for efficient sparse estimation.
problem Sparse estimation problems like fused lasso and convex clustering.
method General method converting ADMM to proximal gradient, assuming Lipschitz continuity of derivative.
result Significant improvement in efficiency for sparse estimation problems.
LSALSA accelerates sparse coding and MCA by learning optimal sparse codes.
problem Efficiently solving sparse coding and MCA problems.
method Deep learning architecture based on SALSA and ADMM.
result LSALSA achieves significant improvements in running time and code quality.
New RL agent learns sparse rewards efficiently.
problem Sparse-reward environments and computational expense.
method Active inference with novel free energy minimization.
result High sample efficiency and online operation.
This paper improves neural network learning by escaping the NTK regime and efficiently learning sparse polynomials.
problem Learning sparse polynomials efficiently using neural networks.
method Spectral analysis of NTK, identifying 'good' directions, and constructing a regularizer.
result Gradient descent on a two-layer neural network can learn sparse polynomials efficiently, improving over the NTK and QuadNTK.
The paper tackles learning varying DAG structures based on contextual features.
problem Learning a single DAG for the entire population from observational data.
method A neural network that maps contextual features to a weighted adjacency matrix of a DAG, with a projection layer to ensure acyclicity.
result The new approach can recover context-specific DAGs where existing methods fail.
Develops a method for identifying structured dynamical systems from data.
problem Identifying structured dynamical systems from undersampled and noisy data.
method Sparse least-squares fitting via ℓ1−ℓ2 optimization with the alternating direction method of multipliers. result The method is stable and successful under certain conditions, as shown by theoretical guarantees and computational results.
Proposes new ℓ0-based methods for low-rank sparse subspace clustering.
problem Clustering high-dimensional data points represented by low-dimensional subspaces.
method Introduces two ℓ0 quasi-norm based regularizations: GMC-LRSSC and S0/ℓ0-LRSSC. Solves resulting nonconvex optimization problems using alternating direction method of multipliers. result Demonstrates effectiveness of proposed methods on synthetic and real-world datasets.