A new method selects models for ensemble learning to maximize mutual information, outperforming existing approaches.
problem Selecting models for ensemble learning to improve performance and reduce correlation issues.
method Formulate budgeted ensemble selection as maximizing mutual information, use Gaussian-copula to model correlated errors, propose a greedy mutual-information selection algorithm.
result Our method consistently outperforms strong baselines across multiple datasets.
A single slow-growing tree matches Random Forest's performance.
problem Matching Random Forest's performance with a single tree.
method SGT uses a learning rate to tame CART's greedy algorithm, improving on greedy ML algorithms.
result SGT and tree ensembles like Booging, BT, and RF improve performance.
New insights into how randomization affects greedy model selection.
problem Understanding the impact of feature subsampling on greedy model selection.
method Investigated greedy forward selection with feature subsampling, proving effects on bias and variance.
result Ensembling with feature subsampling reduces both bias and variance, unlike convex base learners.
Study develops a predictive model to reduce hospital readmissions.
problem Inaccurate readmission risk prediction models in clinical settings.
method Used Genetic Algorithm and Greedy Ensemble to optimize a readmission risk prediction model.
result Developed a useful risk prediction model for reducing unplanned readmissions.
This work automates decision tree construction from neural networks.
problem Creating optimal decision tree architectures from neural networks.
method Automatic induction of non-greedy decision trees using weights transfer from neural networks.
result Improved model performance over fixed hyperparameters.
Evolutionary algorithms improve decision tree ensembles.
problem Improving predictive performance of decision trees.
method Real-valued vector representation of decision trees, evolutionary algorithms (Differential evolution, Evolution strategies).
result Proposed methods outperform classical decision tree induction algorithms.
Greedy convex combination of models outperforms baselines.
problem Overfitting and underfitting in convex combinations of models.
method Greedy learning of convex combinations with early stopping.
result Greedy approach is competitive or better than boosting and random forests.
A new method combines classical and machine learning PDE solvers efficiently.
problem Combining classical and machine learning PDE solvers to reduce computational cost and improve accuracy.
method Proposes an approximate greedy router to select solvers at each iteration, mimicking a greedy approach.
result Consistently reduces final error and AUC of the error trajectory compared to single-solver baselines and hybrid approaches.
Greedy training of recursive partitioning estimators faces a computational barrier when the true function doesn't satisfy a specific property.
problem Computational inefficiency of greedy training for recursive partitioning estimators.
method Analysis of greedy training for sparse regression functions over binary features.
result Greedy training requires exponential samples when the true function doesn't satisfy a specific property (MSP), but only logarithmic samples when it does.
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In part…
This paper introduces TNTK to study infinite soft tree ensembles.
problem Understanding the behavior of infinite soft tree ensembles.
method Introduced Tree Neural Tangent Kernel (TNTK) to analyze infinite soft tree ensembles.
result Identified several non-trivial properties of infinite soft tree ensembles.
Bayesian approach improves ε-greedy exploration in RL.
problem Improving ε-greedy exploration in model-free RL. method Introducing a Bayesian model update for ε based on BMC. result Proposed ε- exttt{BMC} algorithm efficiently balances exploration and exploitation. Converts GBDT trees to neural networks for online updates.
problem Performance loss in converting GBDT trees to neural networks.
method Converts existing GBDT implementations to neural network architectures, allowing online updates of decision splits.
result Learning bounds for neural network architecture with updated splits.
Given a classifier ensemble and a set of examples to be classified, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble are evaluated. This can reduce both mean latency and CPU while maintaining the high accuracy of the original ensemble. To achieve such gai…
Unions of subspaces provide a powerful generalization to linear subspace models for collections of high-dimensional data. To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace s…
Timber targets decision trees, outperforming existing attacks.
problem Poisoning decision trees to manipulate model predictions.
method Greedy attack strategy using sub-tree retraining for efficiency.
result Timber outperforms existing attacks in effectiveness and efficiency.
In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure which ensures that addition of new experts to the ensemble does not decrease the likelihood of the data. These learning rules lead to a generic …
In critical applications of anomaly detection including computer security and fraud prevention, the anomaly detector must be configurable by the analyst to minimize the effort on false positives. One important way to configure the anomaly detector is by providing true labels for a few instances. We study the problem of…
Paper improves anomaly detection using tree-based ensembles with active learning.
problem Configuring anomaly detectors with true labels to minimize false positives.
method Develops batch and streaming active learning algorithms for tree-based ensembles.
result Significantly more anomalies discovered with active learning compared to baselines.
Aims to optimize influence spread in social networks using bandit algorithms.
problem Maximizing influence spread in unknown social networks.
method Combines Thompson Sampling and Epsilon Greedy algorithms with automatic ensemble learning.
result Demonstrates effectiveness of automatic ensemble learning for combinatorial bandit problems.
Greedy algorithm achieves sublinear regret for various distributions.
problem Efficient performance of greedy algorithms in linear contextual bandit problems.
method Introduced Local Anti-Concentration (LAC) condition to ensure sublinear regret.
result Greedy algorithm achieves O(polylogT) cumulative expected regret. Greedy algorithm optimizes consumption habits with power utility.
problem Optimizing lifetime consumption with habit formation under power utility.
method Developed a greedy algorithm using Monte Carlo simulation.
result Greedy solution is a good approximation to the optimal solution.
Greedy policy achieves good results for adaptive submodular problems.
problem Sequential decision making with adaptive stochastic optimization.
method Adaptive submodularity ratio to analyze greedy policy performance.
result Greedy policy achieves approximation guarantees for a broader class of problems.
Greedy AutoAugment improves accuracy with less computation.
problem Finding effective data augmentation policies to cover the search space.
method Greedy approach to reduce the number of trials from exponential to linear growth.
result Greedy AutoAugment increases accuracy by 360 times with fewer resources.
We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of k-sparse signals b…
In statistical dialogue management, the dialogue manager learns a policy that maps a belief state to an action for the system to perform. Efficient exploration is key to successful policy optimisation. Current deep reinforcement learning methods are very promising but rely on epsilon-greedy exploration, thus subjecting…
New distributions allow greedy arm selection in sparse bandit problems.
problem Sparse contextual bandit problem with sparse parameters and feature distributions.
method Introduced new distribution classes and demonstrated that mixtures of these distributions are also greedy-applicable.
result Greedy algorithm applicable to a wider range of arm feature distributions, including those with origin-asymmetric support.
We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints. We first show that a Bayesian approach can achieve variable-selection consistency under relatively mild conditions on the design matrix. We then demonstrate that t…
Simpler ε-greedy with longer action durations improves exploration.
problem Limited exploration capability of ε-greedy in complex domains.
method Temporally extended ε-greedy with repeated actions for random durations.
result Temporally extended ε-greedy outperforms sophisticated methods on various domains.
This paper is a follow up to the previous author's paper on convex optimization. In that paper we began the process of adjusting greedy-type algorithms from nonlinear approximation for finding sparse solutions of convex optimization problems. We modified there three the most popular in nonlinear approximation in Banach…
Introduces greedy feature selection for classifier-dependent feature ranking.
problem Feature selection for classification tasks.
method Greedy feature selection, identifying the most important feature at each step based on the selected classifier.
result Theoretical and numerical benefits of greedy feature selection.
Greedy approach constructs efficient neural networks with ReLU.
problem Constructing efficient neural networks with limited resources.
method Greedy algorithm with integral representation of the network using ridgelet transform.
result Proposed approach constructs efficient shallow neural networks.
Greedy selection works well in a toy model of independent increments.
problem Iterative selection of maximum-value processes from i.i.d. stochastic processes.
method Fixed greedy selection at each stage.
result Optimal strategy is greedy selection under independent increments.
Greedy policy maximizes information in unknown linear systems.
problem Exploration in unknown linear dynamical systems.
method Online greedy policy maximizing information.
result Competitive performance compared to gradient-based methods.
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness is set at maximum, KES co…
Multiple-step lookahead policies have demonstrated high empirical competence in Reinforcement Learning, via the use of Monte Carlo Tree Search or Model Predictive Control. In a recent work \cite{efroni2018beyond}, multiple-step greedy policies and their use in vanilla Policy Iteration algorithms were proposed and analy…
Paper analyzes Greedy-GQ for reinforcement learning with Markovian noise.
problem Analyzing Greedy-GQ for reinforcement learning with Markovian noise.
method Develops finite-sample analysis for Greedy-GQ with linear function approximation under Markovian noise.
result Provides theoretical justification for choosing stepsizes for faster convergence.
New algorithm speeds up learning of graphical models.
problem Learning graphical models with sparse structure efficiently.
method Vertex-greedy score-based algorithm for learning DAGs.
result Polynomial runtime for learning DAG models.
Paper improves greedy algorithm for non-submodular matroid constraints.
problem Maximizing non-submodular functions subject to matroid constraints.
method Developed and analyzed a greedy algorithm with approximation guarantees.
result Greedy algorithm offers approximation factors for matroid constraints.
Improved greedy 2-coordinate updates for optimization problems with constraints.
problem Minimizing smooth functions subject to constraints.
method Exploiting a connection to steepest descent in the 1-norm, we give faster convergence rates and efficient computation.
result Greedy selection converges faster than random selection and can be computed in O(nlogn) time. The contextual bandit literature has traditionally focused on algorithms that address the exploration-exploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical setti…
Greedy algorithm nearly outperforms exploration in contextual bandits.
problem Balancing exploration and exploitation in online learning.
method Smoothed analysis of the greedy algorithm in linear contextual bandits.
result Greedy algorithm nearly matches Bayesian regret rate under diversity conditions, with regret at most O(T1/3). New greedy algorithms improve Bayesian optimisation performance.
problem Optimizing continuous functions with exploration vs exploitation trade-offs.
method Introduced two novel ε-greedy acquisition functions and compared them with conventional methods.
result ε-greedy algorithms generally outperform conventional methods, especially in higher dimensions.
In this paper we consider the task of estimating the non-zero pattern of the sparse inverse covariance matrix of a zero-mean Gaussian random vector from a set of iid samples. Note that this is also equivalent to recovering the underlying graph structure of a sparse Gaussian Markov Random Field (GMRF). We present two no…
Greedy method adds neurons one by one for better function approximation.
problem Function approximation in neural networks.
method Growing deep neural network by adding one neuron at a time with non-linear activation.
result Accurate approximants for model problems in function approximation.
Breathing k-means outperforms greedy k-means++ in clustering.
problem Improving k-means clustering solutions.
method Dynamic adjustment of centroids through breathing technique.
result Breathing k-means outperforms other k-means techniques, especially greedy k-means++.
The paper gives a systematic study of the approximate versions of three greedy-type algorithms that are widely used in convex optimization. By approximate version we mean the one where some of evaluations are made with an error. Importance of such versions of greedy-type algorithms in convex optimization and in approxi…
Greedy algorithm performs well in online matching despite non-i.i.d. connections.
problem Online matching in sparse random graphs with fixed degree distributions.
method Approximating stochastic processes with partial differential equations.
result GREEDY algorithm can outperform RANKING in certain configurations.