arXiv research

The paper tackles reward-relevance in offline RL with sparse decision dynamics.

Proposes a flexible MGP model for dynamic, sparse correlations.

Proposes pT-Learning for optimal dynamic treatment regimes in mHealth.

Study dynamic batch learning in high-dimensional sparse linear bandits.

New algorithm reduces dynamic regret by adapting to comparator complexity.

MAESTRO improves multimodal learning for dynamic time series with adaptive attention and robustness.

New measure SEV shows non-sparse models can still have low decision sparsity.

UCB-TQL learns from multiple tasks with shared dynamics and adapts to task-specific variations.

LSDAT reduces query efficiency for decision-based adversarial attacks.

Study identifies latent variables and models from spacecraft data.

Sparse oblique decision tree improves security rules for renewable power systems.

Improves content allocation in educational platforms with sparse data.

Dynamic Sparse Training finds efficient sparse networks from scratch.

Predictive Sparse Manifold Transform learns dynamic video sequences.

In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…

Paper improves reinforcement learning in multi-scene tasks.

Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many r…

We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allows for sample-efficient learning on large problems by exploiting a factorization to approximate the value function. Our approach only requir…

Optimal sparse recovery with decision stumps achieves strong feature selection guarantees.

"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. Whereas in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created wh…

In this paper we propose an algorithm that builds sparse decision DAGs (directed acyclic graphs) from a list of base classifiers provided by an external learning method such as AdaBoost. The basic idea is to cast the DAG design task as a Markov decision process. Each instance can decide to use or to skip each base clas…

The study reveals decision trees' limitations in fitting data from additive models, proving a generalization lower bound.

A new method for estimating joint value functions in multi-scene reinforcement learning.

Deep learning predicts dynamics from sparse data.

We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamics in a computationally efficient and parameter tuning-free iterative framework known as SPICE (Sparse Iterative Covariance Estimation). The …

KKR uses Koopman theory to improve forecasting in complex systems.

New pruning methods improve dynamic sparse training performance.

New method learns dynamics from sparse data using geometric constraints.

REALITrees uses a Rashomon ensemble approach for active learning in sparse decision trees.

Probabilistic NDVI forecasting from sparse satellite data.

Novel algorithm reduces feature inclusion in online decision-making.

Paper addresses regret minimization and inference in high-dimensional online decision-making.

New techniques improve the accuracy of identifying nonlinear systems from noisy data.

We consider the problem of modeling multivariate time series with parsimonious dynamical models which can be represented as sparse dynamic Bayesian networks with few latent nodes. This structure translates into a sparse plus low rank model. In this paper, we propose a Gaussian regression approach to identify such a mod…

A new algorithm reduces communication costs for collaborative decision-making across clients.

Parsimonious Dynamic Mode Decomposition selects sparse modes robustly.

LD-EnSF speeds up data assimilation with sparse observations.

SVR-Tree improves classification trees for imbalanced and sparse data.

Framework learns continuous dynamics from sparse trajectories.

Improved SINDy autoencoder for identifying noisy dynamical systems.

New techniques optimize decision trees for interpretable machine learning.

Neural networks have achieved state of the art performance across a wide variety of machine learning tasks, often with large and computation-heavy models. Inducing sparseness as a way to reduce the memory and computation footprint of these models has seen significant research attention in recent years. In this paper, w…

Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the existing algorithms, fewer still provide strong performance guarantees. This paper examines two algorithms f…

A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is proposed to model sequentially observed multivariate data. SGLDS uses the Bernoulli-Poisson link together with a gamma process to generate an infinite dimensional sparse random graph to model state transitions. Depending on the sparsity pattern of…

This research develops an evolutionary approach to discover non-Gaussian stochastic dynamical systems.

The paper analyzes dynamics of momentum in high dimensions with sparse updates.

Model infers latent variables in sparse coding models using Langevin dynamics.

ADAPT method controls balance between sparse generator and discriminator in GANs.