Boosting improves control of complex systems.
problem Improving performance of controllers for dynamical systems.
method Proposes a boosting framework for online control of dynamical systems.
result An efficient boosting algorithm that combines weak controllers into a more accurate one.
Boost-R uses gradient boosted trees for analyzing recurrence data.
problem Analyzing recurrence data with static and dynamic features.
method Gradient boosted additive trees with time-dependent functions.
result Estimates the cumulative intensity function of recurrent event processes.
Infinitesimal boosting converges to a deterministic process in large sample limit.
problem Characterizing the asymptotic behavior of infinitesimal gradient boosting in large sample sizes.
method Proving convergence to a deterministic process using large sample theory and differential equations.
result The test error decreases over time in the population limit.
Boosting CNNs with dynamic feature selection and boosting weights improves accuracy and efficiency.
problem Expensive training of deep CNNs and manual architecture design.
method Dynamic feature selection, importance sampling, and boosting weights integrated into network training.
result Boosted CNN variants consistently outperform conventional CNNs in accuracy and training speed.
This paper reconciles different views on AdaBoost to better understand its dynamics.
problem Understanding the dynamics of AdaBoost and its various interpretations.
method Analyzing and unifying different perspectives on AdaBoost.
result Unified understanding of AdaBoost's dynamics and its various interpretations.
Dynamic CBDT improves treatment effect estimation in clinical data.
problem Estimating heterogeneous treatment effects in observational data with high accuracy and interpretability.
method Dynamic Regularized Causal Boosted Decision Trees (CBDT) integrating variance regularization and calibration.
result Significantly improved estimation accuracy and reliable coverage of true treatment effects.
BoXHED boosts hazard estimation for dynamic health risk scores.
problem Analyzing time-varying health vitals for disease onset prediction.
method Gradient boosting for nonparametric hazard function estimation with time-dependent covariates.
result Novel interaction effects among risk factors identified in cardiovascular disease onset data.
Boosting improves trend detection in financial data.
problem Discovering trends in financial data during crises and recoveries.
method Extends boosting to higher order integrated processes and series with roots near unity.
result Boosting captures downturns and recoveries more accurately.
The paper proposes a method to assess and improve data quality using GBDT training dynamics.
problem Improving data quality in datasets with noisy labels and varying contributions.
method Metrics computed from training dynamics of Gradient Boosting Decision Trees (GBDTs).
result The method achieved the best results compared to other approaches.
Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.
problem Static label ordering in multi-label classification limits model performance.
method Combining dynamic classifier chains with XGBoost for efficient multi-label prediction.
result Dynamic label ordering improves model performance and reduces training costs.
BoXHED2.0 boosts survival analysis for complex data.
problem Survival analysis with time-dependent covariates.
method Tree-boosted hazard estimator, fully nonparametric, scalable.
result Scalable to parametric boosted survival models in speed.
NGBoost boosts probabilistic predictions using natural gradients.
problem Uncertainty estimation in probabilistic predictions.
method Gradient boosting for probabilistic regression with natural gradient correction.
result NGBoost outperforms existing methods for probabilistic prediction.
We offer a novel view of AdaBoost in a statistical setting. We propose a Bayesian model for binary classification in which label noise is modeled hierarchically. Using variational inference to optimize a dynamic evidence lower bound, we derive a new boosting-like algorithm called VIBoost. We show its close connections …
DDBF improves random forest for imbalanced data.
problem Learning from imbalanced data.
method Incorporates hard example mining into random forest, dynamically removing easy examples.
result DDBF outperforms random forest on multiple datasets.
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.
problem Understanding the mathematical principles behind ensemble learning and ant colony behavior.
method Developed a formal mapping between AdaBoost's adaptive reweighting and ant recruitment dynamics.
result Proved that the fundamental theorem of weak learnability has a direct analog in colony decision-making.
This thesis optimizes neuromorphic systems by slowing down their dynamics, improving performance.
problem Timescale mismatch between analog neuromorphic circuits and real-time sensory inputs.
method Proposes and tests solutions to slow down the dynamics of spiking neural networks.
result Spiking neural networks on analog neuromorphic systems can achieve significant performance boosts.
Improved natural gradient boosting with leaf number clipping for faster and better performance.
problem Slower training speed and poor performance on large datasets for natural gradient boosting.
method Leaf number clipping regularization to optimize hyperparameters and improve performance.
result Significant improvement in performance and up to 4.85x speed up on various datasets.
Proposes a new Q-learning method for survival outcomes in clinical trials.
problem Incomplete follow-up data and nonlinear covariate effects in clinical trials.
method Combines Buckley-James boosting with flexible base learners for estimating optimal treatment regimes.
result Improves treatment decision accuracy and stability in longitudinal clinical trials.
LGB+ improves macroeconomic forecasting by combining linear and tree models.
problem Efficiency in small samples for forecasting with mixed linear and nonlinear dynamics.
method LGB+ is a boosting procedure that evaluates both tree and linear candidates at each step, advancing only the winner. It decomposes forecasts into linear and nonlinear contributions.
result LGB+ delivers strong gains for targets with pronounced autoregressive dynamics or mixed signals.
Spectral deconfounding improves machine learning models by reducing hidden confounding effects.
problem Machine learning models can be misled by hidden confounders, leading to unreliable predictions.
method Develops a nonlinear spectral deconfounding framework for gradient boosting that modifies boosting dynamics to slow down in confounding-aligned directions.
result Spectrally deconfounded boosting improves estimation of the target function under hidden confounding and is more scalable.
Boosts causal discovery by dynamically reweighting samples to learn better DAGs.
problem Overfitting to easier-to-fit samples and violating homogeneity assumptions in causal discovery.
method Adaptive sample reweighting via ReScore function to upweight and downweight samples based on fitting quality.
result Consistent and significant boosts in structure learning performance on synthetic and real-world datasets.
We analyze a new Markov chain model for better sampling and optimization.
problem Developing a new Markov chain model for improved sampling and optimization.
method We introduce a new class of Ito chains with arbitrary noise and inexact drift/diffusion coefficients, proving a bound in W2-distance. result Our analysis provides improved or first results for various applications like SGLD, sampling, and boosting.
StealthRank subtly boosts LLM rankings without detectable anomalies.
problem Adversarial manipulation of LLM-driven ranking systems.
method Energy-based optimization with Langevin dynamics for stealthy prompt generation.
result StealthRank outperforms existing methods in covertly boosting rankings.
Proposes a method to boost deep reinforcement learning with sparse rewards.
problem Challenges in learning complex behaviors with long horizons and sparse rewards.
method Predictive coding for reward shaping.
result Achieves better learning by providing reward signals that understand environment dynamics and emphasize useful features.
Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent with structural sparsity control whose underlying dynamics are governed by differential inclusions. In particular, we present an iterative reg…
Enhances neural network dynamics to boost computational capacity.
problem Improving computational capacity of neural networks.
method Introducing Phase Transition Adaptation to drive system dynamics towards edge of stability.
result Consistently achieves enhancement in computational capacity over multiple datasets.
Adaptive XGBoost improves accuracy on evolving data streams by updating the ensemble dynamically.
problem Concept drift in evolving data streams.
method Adapts XGB to update the ensemble with new data, maintaining consistency with current concept.
result Improves classification accuracy on evolving data streams compared to other methods.
AF improves classification models by adaptively weighting trees.
problem Improving classification model performance.
method AF combines OP2T for input-dependent weights and MIO for dynamic refinement.
result AF consistently outperforms RF, XGBoost, and other weighted RF.
Fair MP-Boost improves fairness and interpretability in boosting methods.
problem Improving fairness and interpretability in boosting methods.
method Fair MP-Boost uses adaptive sampling of minipatches to balance accuracy and fairness.
result Fair MP-Boost enhances fairness and accuracy while providing interpretable feature importance.
BooST combines boosting and smooth trees for nonlinear regression.
problem Estimating partial effects in nonlinear models.
method Boosting smooth trees for partial effect estimation.
result BooST provides more interpretable partial effects than tree-based models.
Study of stock market dynamics using statistical physics, showing critical temperature and boosting effects.
problem Understanding price fluctuations and market clearing in stock markets.
method Statistical physics approach with agents modeled as spins, temperature parameter, and Monte Carlo simulations.
result Critical temperature Tc where market becomes disordered, and boosting effect depends on H applied near Tc. Survey of gradient boosting algorithms and their applications.
problem Improving machine learning models through boosting techniques.
method Detailed mathematical frameworks of gradient boosting algorithms.
result Illustration of gradient boosting's role in model optimization and ranking.
Proximal boosting improves gradient boosting for non-differentiable losses.
problem Minimizing non-differentiable losses in prediction models.
method Proximal point algorithm applied to gradient boosting.
result Proximal boosting outperforms gradient boosting in convergence rate and accuracy.
A scalable framework for gradient boosting using TensorFlow.
problem Training gradient boosted trees efficiently on large datasets.
method Distributed training architecture, automatic loss differentiation, layer-by-layer boosting, multi-class handling, regularization.
result Faster prediction and smaller ensembles compared to traditional methods.
A new gradient tree boosting framework reduces variance and accelerates performance.
problem High variance in stochastic gradient boosting.
method Combining gradient tree boosting with importance sampling and a regularizer.
result Achieves a linear convergence rate on logistic loss and 2.5x--18x acceleration on LogitBoost and LambdaMART.
MP-Boost boosts accuracy faster and more interpretable than AdaBoost.
problem Developing a faster, more interpretable boosting method.
method Adaptive selection of minipatches (small subsets of instances and features) at each iteration.
result Achieves comparable accuracy to AdaBoost and gradient boosting but faster and more interpretable.
Research examines how strategic latency manipulation impacts Ethereum's network efficiency and decentralization.
problem Impact of artificial latency on Ethereum's network efficiency and decentralization.
method Comprehensive analysis of MEV-Boost auction system and empirical validation with a pilot.
result Increased profitability for node operators and significant systemic challenges like heightened network inefficiencies and centralization risks.
Residual Networks are shown to be equivalent to boosting feature representation.
problem Improving feature representation in deep learning models.
method Proved ResNet's equivalence to Online Gradient Boosting and proposed decision tree residual modules.
result ResNet can achieve Online Gradient Boosting regret bounds through architectural changes.
This research compares gradient and Newton boosting methods in classification and regression.
problem The distinction between gradient descent and Newton updates in boosting algorithms is not well understood.
method Presented a unified framework for gradient and Newton boosting, and compared them with tree base learners.
result Newton boosting outperforms gradient and hybrid boosting in predictive accuracy on most datasets.
Tree-structured boosting connects gradient boosted stumps and full decision trees.
problem Connecting gradient boosted stumps and full decision trees.
method Introducing tree-structured boosting to create a single decision tree.
result Tree-structured boosting produces models equivalent to CART or gradient boosted stumps at the extremes.
Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three cali…
Calibrated Boosting-Forest improves ranking and probability calibration in classification tasks.
problem Need for superior ranking power and well-calibrated probability estimates in classification tasks.
method Ensemble of gradient boosting machines supporting both continuous and binary labels.
result Calibrated Boosting-Forest achieves significant improvements in ranking and probability calibration compared to state-of-the-art models.
Extends boosting to multiclass online agnostic classification.
problem Online multiclass classification with weak learners.
method Reduces multiclass online agnostic boosting to online convex optimization.
result First boosting algorithm for online agnostic multiclass classification.
Iran's FiT policy boosts REs but risks financial crisis and plant failure.
problem Financial challenges in maintaining renewable energy growth after policy targets.
method System dynamics model considering budget status, social tolerance, and investor trust.
result Adjusting tax on electricity consumption based on budget status prevents financial crises and maintains stable growth.
Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of L2Boosting, which is tailored for regression, in a high-dimensional setting. Moreover, we introduce so-called \textquotedblleft post-Boosting\textquotedblright. This is a post-selection estimator w…
Robust boosting improves regression accuracy in noisy data.
problem Handling outliers in non-parametric regression.
method Two-stage approach: robust residual scale minimization followed by bounded loss optimization.
result Robust boosting outperforms standard methods in outlier-prone data.
Boosting random forests reduces bias and improves predictive performance.
problem Reducing bias in random forest predictions.
method Extract residuals from random forest, fit another random forest to residuals, sum predictions.
result One-step boosted forest has reduced bias and improved predictive performance.
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers. Such an optimized worker assignment method allows us to boost the efficacy of any popular crowdsourcing estimation algorithm. We consider a mutual info…