Sparse representations improve reinforcement learning control policies.
problem Sparse representations are underused in reinforcement learning control.
method Incremental learning with sparse representations from neural networks, using distributional regularizers.
result Sparse representations avoid catastrophic interference and provide stable values for reinforcement learning.
Paper proposes a sparse synthetic control method to select important predictors.
problem Choosing and weighting predictors affects synthetic control estimator performance.
method Sparse synthetic control procedure that penalizes predictors, derived in a linear factor model.
result Sparse synthetic control achieves lower bias and better post-treatment performance.
Efficiently designs distributed controllers for sparse systems with sub-linear sample complexity.
problem Designing robust distributed controllers for unknown-but-sparse linear systems.
method Combining distributed controller synthesis and structured linear inverse problems for system identification.
result Near-optimal distributed controllers can be learned with sub-linear sample complexity and near-linear time complexity.
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.
Improved FDR control for sparse financial index tracking.
problem Maintaining FDR control in high-dimensional financial data with strong variable dependencies.
method Expanding T-Rex framework to handle overlapping groups of correlated variables with nearest neighbors penalization.
result Accurately tracks the S&P 500 index using only a small number of stocks.
Sparse codes improve optimal control tasks with correlated inputs.
problem Optimal control tasks with correlated feature inputs.
method Used a sparse code to represent natural images in an optimal control task solved with neuro-dynamic programming.
result An over-complete sparse code increases memory capacity and learning speed beyond a complete code.
Sparse PCA selects variables with FDR control for improved performance.
problem Sparse PCA selects irrelevant variables when maximizing explained variance.
method Proposes FDR-controlled selection using T-Rex selector.
result Significant performance improvement over traditional sparse PCA.
SAC-X enables learning complex behaviors from sparse rewards.
problem Learning complex behaviors from sparse reward signals.
method Scheduled Auxiliary Control (SAC-X) with auxiliary tasks.
result SAC-X enables efficient exploration and complex behavior learning.
A method for control of complex systems using sparse data and reinforcement learning.
problem Control of complex systems with limited and streaming data.
method Discrete embedding space, Markov process model, reinforcement learning.
result The method performs well on experimental systems.
Big T-Rex solves FDR-controlled sparse regression on laptops with millions of variables.
problem Scalable FDR-controlled variable selection for high-dimensional data.
method Early terminated random experiments with memory-mapping and permutation-based dummy generation.
result Solves FDR-controlled Lasso problems with 5 million variables on a laptop in 30 minutes.
New method controls gradient error for sparse MRFs.
problem Efficient learning for sparse discrete MRFs with NP-hard inference.
method Stochastic proximal gradient (SPG) with controlled gradient approximation error.
result Novel bounds control gradient approximation quality.
VASE uses Bayesian neural networks to improve exploration in sparse reward environments.
problem Exploration in environments with continuous control and sparse rewards.
method VASE uses a Bayesian neural network model of the environment dynamics and variational inference to alternately update the model's accuracy and policy.
result VASE outperforms other surprise-based exploration techniques in continuous control sparse reward environments.
New method improves signal reconstruction with nonconvex penalties and parameter control.
problem Reconstructing sparse signals with nonconvex penalties and nonconvexity control.
method Introduces nonconvex penalties (SCAD, MCP) with nonconvexity parameters and controls them to guide AMP trajectory.
result Achieves perfect reconstruction for relatively dense signals with small nonconvexity parameters.
Methodology for learning sparse models using all multiplicative interactions efficiently.
problem Learning high-order feature interactions with fine control.
method Fine Control Kernel framework, combining Fenchel Duality and Apriori algorithm.
result Efficiently solves large sparse learning problems with sparse feature screening rules.
ADAPT method controls balance between sparse generator and discriminator in GANs.
problem High computational complexity in GANs training.
method Balanced Dynamic Sparse Training (ADAPT) method.
result ADAPT method achieves a good trade-off between performance and computational cost.
A simple algorithm finds sparse correlations between two sets of variables.
problem Sparse CCA under standardized and uncorrelated variables.
method Combinatorial algorithm operating on low rank data approximation.
result Global approximation guarantees with precise sparsity control.
Unified framework for sparse alternatives to softmax with control over sparsity.
problem Lack of understanding and explicit control over sparsity in probability mapping functions.
method Unified framework encompassing softmax, sum-normalization, spherical softmax, and sparsemax. Two novel sparse formulations (sparsegen-lin and sparsehourglass) and convex loss functions developed.
result Improved performance in multilabel classification and seq2seq tasks like neural machine translation and abstractive summarization.
Sparse representations improve reinforcement learning performance.
problem TD Learning struggles with large state spaces and simple control tasks.
method Learned sparse representations to reduce state space and support generalization.
result Sparse representations enhance reinforcement learning performance on challenging tasks.
BP fails to find sparsest solution for structured matrices.
problem Finding sparsest solution to linear equations with structured matrices.
method Introduced class of structured matrices for BP failure.
result Determines columns corresponding to unrecoverable non-zero entries.
ETGL-DDPG improves DDPG for sparse reward control with new exploration and replay techniques.
problem Sparse reward continuous control in reinforcement learning.
method Introduces εt-greedy search and GDRB framework for efficient exploration and reward use. result ETGL-DDPG outperforms DDPG and other methods on sparse-reward continuous benchmarks.
Proposes ARSK for robust and sparse clustering.
problem Outliers and high-dimensional noisy variables in K-means clustering.
method Introduces redundant error component and group sparse penalty for robustness, and weights and sparsity control penalty for noisy variables.
result Superior performance in identifying clusters without outliers and informative variables.
The paper proposes a control strategy for systems with sparse parameters using compressed sensing.
problem Control of linear systems with unknown sparse parameters under disturbances.
method Sparse estimation using Recursive Least Squares, improved with Basis Pursuit Denoising, and reformulated probabilistic constraints.
result The proposed algorithm outperforms existing methods in control design for systems with sparse impulse response parameters.
msPCA solves sparse PCA for multiple components efficiently.
problem Sparse principal component analysis with multiple components.
method Alternating maximization algorithm for sparse loading vectors, with orthogonality or zero correlation constraints.
result Achieves high variance explained with sparse components and controlled feasibility violations.
New method learns dynamics from sparse data using geometric constraints.
problem Learning dynamics from sparse, undersampled data.
method Reformulates inference as a stochastic control problem, using geometry-driven path augmentation.
result Accurately recovers stochastic dynamics from extremely undersampled data.
Tensor decomposition improves robot control by modeling inverse dynamics.
problem Accurate modeling of inverse dynamics for robot control.
method Tensor decomposition of sparse tensors to approximate non-linear functions.
result Superior performance compared to state-of-the-art methods.
New DRGP models improve prediction accuracy for sequential data.
problem Modeling sequential data for applications like autonomous driving.
method Introduces Deep recurrent Gaussian process (DRGP) models based on Sparse Spectrum Gaussian process (SSGP) and variational Sparse Spectrum Gaussian process (VSSGP).
result Improves prediction accuracy compared to current state of the art methods.
New method uses temperature to control sparse MoE convergence rates.
problem Sparse MoE convergence rates are slow due to temperature interactions.
method Proposes a novel activation gate to improve convergence rates.
result Improved convergence rates to polynomial rates via novel gate.
SAIL learns from sub-optimal demonstrations to improve sample efficiency in sparse reward tasks.
problem Reducing sample complexity in sparse-rewarded tasks.
method Self-Adaptive Imitation Learning (SAIL) that exploits sub-optimal demonstrations and efficient exploration.
result Significantly improved sample efficiency and better final performance across various tasks.
Guiding reinforcement learning with suboptimal controllers speeds up training.
problem Sparse rewards in reinforcement learning make exploration inefficient.
method Use a suboptimal controller to guide exploration, applying a Q-filter loss conditionally.
result The approach leads to faster policy refinement and better performance.
A new method detects sparse changes in high-dimensional data streams using tailored PCA projections.
problem Detecting sparse changes in high-dimensional data streams.
method Tailored PCA projections for online change detection.
result High efficiency in detecting even very sparse changes in mean, variance, and correlation.
A new Boosting algorithm with differential inclusion approach for sparse parameter control.
problem Sparse parameter control in machine learning models.
method Iterative regularization path with differential inclusions and variable splitting.
result Split LBI outperforms generalized Lasso in theory and experiments.
DiSMEC learns sparse classifiers for large label spaces without low-rank assumptions.
problem Learning multi-label classification with power-law distributed labels.
method Large-scale distributed framework with explicit capacity control for one-versus-rest linear classifiers.
result DiSMEC achieves significant improvements in prediction accuracy compared to state-of-the-art methods.
New method speeds up sparse graph neural networks training on dense hardware.
problem Training sparse graph neural networks is slow on custom hardware.
method Inspired by sparse matrix optimization, developed techniques for dense hardware.
result Sparse graph neural networks trained in 13 minutes on 512-core TPUv2 Pod.
Sparse activations in neural models correlate with frequent words, suggesting sparsity is natural.
problem Interpretability and resource efficiency in neural language models.
method Used the Taxi-Euclidean norm to measure sparsity and analyzed gradients and activations of frequent words.
result Frequent input words are associated with sparse activations, while frequent target words are associated with dispersed activations.
Paper proposes a new method for efficient exploration in reinforcement learning.
problem Sparse reward reinforcement learning challenges in exploration.
method Learn separate intrinsic and extrinsic task policies, schedule between them, and use successor feature control (SFC).
result Substantially improved exploration efficiency with SFC and hierarchical usage of intrinsic drives.
Paper proposes a method to monitor industrial processes under closed-loop control.
problem Difficulty distinguishing between real process faults and normal operating conditions changes.
method Develops a distributed monitoring system by capturing static and dynamic characteristics of large-scale closed-loop industrial processes.
result The method effectively distinguishes between real process faults and normal operating conditions changes.
EASIER-net uses sparse networks to improve prediction accuracy for high-dimensional data.
problem Limited use of neural networks in high-dimensional data with small samples.
method Ensemble by Averaging Sparse-Input Hierarchical networks (EASIER-net) with small modifications to neural network architecture and training procedure.
result EASIER-net achieves higher prediction accuracy than off-the-shelf methods on average.
New method for sparse data using L1-NMF with improved sparsity control.
problem Sparse data with false zeros and heavy-tailed noise.
method Component-wise L1-NMF with weighted penalization and coordinate descent.
result Effective in handling sparse data with false zeros.
Paper explains failures of DDPG in simple deterministic environments.
problem DDPG's failures in trivial environments with sparse rewards.
method Formal explanation through a simple control problem and general analysis.
result DDPG can get stuck in poor solutions in deterministic environments.
Improved CEM for fast real-time planning in high-dimensional control tasks.
problem Sampling inefficiency of CEM in real-time planning.
method Novel additions to CEM including temporally-correlated actions and memory.
result 2.7-22x less samples and 1.2-10x performance increase.
Improved SGD for sparse data with faster convergence and better stability.
problem Slow convergence and high variance in sparse online learning for high-dimensional sparse data.
method Stabilized truncated stochastic gradient descent with adaptive shrinkage and annealing strategy.
result Our algorithm achieves better prediction accuracy, sparsity, and stability compared to the original method.
New method discovers discrepancies between simplified models and experimental data.
problem Model discrepancies in nonlinear systems lead to significant deviations from true behavior.
method Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to discover sparse model terms.
result Improvement in performance with a discrepancy model in simulations.
Metalearning optimizes autoencoder dimensions for efficient data representation.
problem Selecting optimal dimension for autoencoder output to balance accuracy and complexity.
method Metalearning approach using actor-critic algorithm to dynamically adjust dimension.
result Automatic selection of minimum number of bases for optimal reconstruction.
New method improves sparse signal reconstruction using 1RSB-AMP.
problem Sparse signal reconstruction with improved accuracy.
method Developed 1RSB-AMP and 1RSB-SE for SCAD penalty minimization.
result 1RSB-AMP achieves improved reconstruction compared to RS-AMP.
Alpha-norm regularization simplifies marketing demand forecasting.
problem Ultra high-dimensional problems in demand estimation and forecasting.
method Nonconvex alpha-norm objective with coordinate descent and proximal operators.
result Alpha-norm regularization provides accurate out-of-sample estimates for promotion effects.
The paper introduces a method for multi-agent reinforcement learning to coordinate exploration.
problem Sparse rewards in multi-agent settings lead to independent exploration.
method Designing intrinsic rewards that encourage coordination and developing a hierarchical policy.
result The approach accelerates and improves exploration in cooperative multi-agent settings.
We consider the problem of recovering block-sparse signals whose structures are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. However, the knowledge of the block structure is usually unavailable in practice. In this paper, we d…
Adaptive sparseness enhances robust regression using MCC and ARD.
problem Developing a robust regression method with adaptive sparseness.
method Integrating MCC with ARD in a Bayesian framework using variational Bayesian inference.
result MCC-ARD regression outperforms existing methods in prediction and feature selection.