Improves tree model performance by considering future node splits.
problem Improving tree model performance.
method Next-Depth Lookahead Tree (NDLT) model that evaluates future node splits.
result Enhanced tree model performance.
Paper improves k-NN predictive performance with efficient variable selection.
problem Improving predictive performance of k-NN models. method Efficient forward selection of predictor variables.
result Novel approach approaches outperformance of stepwise selection models.
FIRE PBT improves neural network training by focusing on long-term performance.
problem Greedy decision mechanisms in PBT lead to poor long-term performance.
method FIRE PBT uses a fitness metric to encourage long-term performance over short-term improvements.
result FIRE PBT outperforms PBT on ImageNet and matches hand-tuned learning rates.
New method improves NN performance across various settings.
problem Improving neural network performance across different datasets and architectures.
method Population Gradients (PG) method to calculate non-local gradient estimates.
result Significantly improves final performance across architectures, data-sets, and hyper-parameters.
Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full auto…
TuneUp improves GNN training by focusing on hard-to-learn nodes.
problem Sub-optimal training of GNNs on all nodes equally.
method Two-stage training: base GNN + tail node improvement.
result Significant improvement in tail node prediction performance.
Improved neural active learning algorithms reduce regret and improve performance.
problem Improving performance and reducing regret in neural active learning for non-parametric streaming data.
method Introducing two new regret metrics and leveraging NNs for both exploitation and exploration. The algorithm uses tailored query decision-makers and full feedback.
result Achieved an instance-dependent regret upper bound improving by a multiplicative factor of O(logT) and removing the curse of dimensionality. Improves neural network performance by enriching training dataset.
problem Achieving worst-case performance guarantees in neural networks.
method Adapting training dataset during training to reduce worst-case violations.
result Improved worst-case performance guarantees in neural networks.
A simple approach improves performance on both past and future tasks in lifelong learning.
problem Forgetting in lifelong learning, where performance on past tasks degrades when learning new tasks.
method Representation ensembling to improve performance on both future and past tasks.
result Representation ensembling demonstrates both forward and backward transfer across various datasets.
Improved analysis of UCBVI algorithm with better empirical performance.
problem Improving the UCBVI algorithm's performance and understanding its bounds.
method Refined analysis of UCBVI algorithm with improved bonus terms and regret analysis.
result Improving multiplicative constants in UCBVI bounds enhances empirical performance.
QTRAN++ improves MARL performance in complex environments.
problem Poor empirical performance of QTRAN in complex environments.
method Stabilizing training objective, removing role separation, and introducing a multi-head mixing network.
result QTRAN++ achieves state-of-the-art performance in the Starcraft Multi-Agent Challenge (SMAC).
Gradient sparsification enhances privacy-preserving machine learning models.
problem Improving performance of differentially-private machine learning models under privacy constraints.
method Gradient sparsification combined with compressed sensing and additive Laplace noise.
result Gradient sparsification can improve performance of differentially-private machine learning models for small privacy budgets.
There is growing evidence that converting targets to soft targets in supervised learning can provide considerable gains in performance. Much of this work has considered classification, converting hard zero-one values to soft labels---such as by adding label noise, incorporating label ambiguity or using distillation. In…
Improved pre-trained embeddings through effective entropy maximization.
problem Developing high-quality pre-trained embeddings for future tasks.
method E2MC criterion defined in terms of low-dimensional constraints.
result Significant improvement in downstream performance.
In this study, the effects of eight representation regularization methods are investigated, including two newly developed rank regularizers (RR). The investigation shows that the statistical characteristics of representations such as correlation, sparsity, and rank can be manipulated as intended, during training. Furth…
Deep learning ensembles improve COVID-19 detection from chest X-rays.
problem Detecting COVID-19 from chest X-rays using machine learning.
method Custom CNN and ImageNet models, transfer learning, iterative pruning, ensemble learning.
result 99.01% accuracy in detecting COVID-19 from chest X-rays.
Ensembles of climate models are commonly used to improve climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, we use an ensemble of decadal climate predictions to demonstrate the abilit…
Different technological domains have significantly different rates of performance improvement. Prior theory indicates that such differing rates should influence the relative speed of diffusion of the products embodying the different technologies since improvement in performance during the diffusion process increases th…
Data splitting enhances model performance in overparametrized ridgeless regression.
problem Computational inefficiency in training models with large datasets.
method Data splitting as a regularization technique in overparametrized ridgeless regression.
result Data splitting improves statistical performance and computational complexity.
Improved GANs mitigate forgetting and collapse issues.
problem Catastrophic forgetting and mode collapse in GANs.
method Contrastive learning and mutual information maximization.
result Significantly stabilizes GAN training and improves performance.
SIBRE boosts reinforcement learning convergence by rewarding improvement over past performance.
problem Improving the rate of convergence in reinforcement learning.
method SIBRE is a reward shaping approach that rewards improvement over the agent's own past performance.
result SIBRE converges faster and more stably to the optimal policy compared to baseline RL algorithms.
Proposes a method to improve neural architectures reproducibly.
problem Lack of reproducibility in Neural Architecture Transformer (NAT).
method Differentiable Neural Architecture Transformation (DNAT).
result DNAT outperforms NAT and is applicable to various models and datasets.
RPI combines imitation and reinforcement learning to improve policies efficiently.
problem High sample complexity in reinforcement learning.
method Active interleaving between imitation and reinforcement learning, using oracle queries for exploration.
result RPI outperforms existing methods across various domains.
KT models improved slightly with synthetic student data.
problem Limited access to real student data and lack of diversity in public datasets.
method Simulated student data using three statistical strategies and tested on KT baselines.
result Synthetic data can lead to similar performance as real data.
Bagging, a powerful ensemble method from machine learning, improves the performance of unstable predictors. Although the power of Bagging has been shown mostly in classification problems, we demonstrate the success of employing Bagging in sparse regression over the baseline method (L1 minimization). The framework emplo…
Training on mixed distributions improves test performance even when components are unrelated.
problem Improving test performance with mismatched training and test distributions.
method Analyzing mixture distributions with different training and test proportions.
result Distribution shift can be beneficial, improving test performance even when components are unrelated.
QFIL improves offline RL by filtering data to reduce bias and variance.
problem Improving offline reinforcement learning policies with limited data.
method QFIL uses a filtered dataset to improve policies, trading off bias and variance through quantile selection.
result QFIL provides a safe policy improvement step with function approximation and effectively balances bias and variance.
Paper explores how feature interactions improve XGBoost models.
problem Improving XGBoost model performance and interpretability.
method Identifies feature interactions as constraints in XGBoost models.
result Accurate identification of feature interactions leads to significant model performance improvements.
Paper proposes LCP for structural encodings, outperforming existing methods.
problem Improving Graph Neural Networks performance through effective structural encodings.
method Geometric perspective, Local Curvature Profiles (LCP) for structural encodings, combining with global positional encodings, comparing with rewiring techniques.
result LCP significantly outperforms existing structural encodings and combining LCP with global positional encodings improves performance.
End-to-end approach for weak supervision improves downstream model performance.
problem Data-labeling bottleneck in machine learning applications.
method Directly learning the downstream model by maximizing its agreement with probabilistic labels generated from weak supervision sources.
result Improved performance over prior work in terms of downstream model performance and robustness.
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy. Here we propose a framework to directly support human decision-making, in which the role of machines is to reframe pr…
Paper improves performance guarantees for Rademacher projections.
problem Improving statistical guarantees for Rademacher random projections.
method Algebraic framework for proving Schur-concavity properties.
result Novel Schur-concavity property of Rademacher projections with improved performance.
ViCE uses superpixels to enhance self-supervised learning for better dense visual embeddings.
problem Lack of high-resolution feature maps from self-supervised models.
method Superpixels for dense representation learning, contrasting over regions.
result Improves unsupervised semantic segmentation on benchmarks like Cityscapes and COCO.
New Convolutional Unit improves Batch Whitening performance.
problem Improving the efficiency and effectiveness of Batch Whitening.
method Proposes a new Convolutional Unit that aligns with Batch Whitening theory and empirically analyzes the original Convolutional Unit.
result Significantly improved performance on multiple image classification datasets.
FORK improves model-free reinforcement learning performance.
problem Improving model-free reinforcement learning performance.
method Introducing a new forward-looking Actor (FORK) for Actor-Critic algorithms.
result FORK significantly improves performance in various environments.
LogEI improves Bayesian optimization by simplifying numerical computation of EI and related functions.
problem Numerical pathologies in optimizing EI and related acquisition functions.
method Proposes LogEI, a family of acquisition functions that simplify numerical optimization.
result LogEI members improve optimization performance and match or exceed state-of-the-art methods.
Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environme…
US firms improve ESG performance in response to China trade shock.
problem Impact of China trade shock on US ESG performance.
method Trade policy experiment exploiting tariff changes.
result Greater import competition from China increases US firm ESG performance.
New AI error correctors improve classifier performance with provable guarantees.
problem Improving AI classifier performance with scarce training data.
method Weakly supervised AI error correctors with performance guarantees.
result Provable performance guarantees for AI error correction.
The paper proposes calibration to improve algorithm performance using machine learning predictions.
problem Improving real-world performance of online algorithms with machine learning predictions.
method Calibration as a tool to bridge the gap between prediction uncertainty and algorithm design.
result Calibrated advice leads to more effective guidance in high-variance settings and significant performance improvements in real-world data.
Improves comparison of F-measures for imbalanced datasets.
problem Comparing F-measures for classification algorithms on imbalanced data.
method Two improvements to existing F-measure comparison methods.
result Enhanced accuracy in comparing F-measures for classification.
The method presented extends a given regression neural network to make its performance improve. The modification affects the learning procedure only, hence the extension may be easily omitted during evaluation without any change in prediction. It means that the modified model may be evaluated as quickly as the original…
This paper improves ASR performance by aligning frames more accurately.
problem Disagreement between teacher-student models in frame-level alignment.
method Introduces self-knowledge distillation (SKD) to guide frame-level alignment.
result Improves both resource efficiency and performance in ASR.
We develop a multi-task convolutional neural network (CNN) to classify multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset comprised of over 40,000 ECGs, with labels derived from cardiologist clinical interpretations. Since many clinically important classes can occur in low frequencies, approaches…
Framework uses synthetic data from pretrained models to improve predictive modeling.
problem Limited effectiveness of synthetic data from generative models for improving predictive performance.
method Proposes an end-to-end framework that generates and filters synthetic data through domain-specific statistical methods.
result Consistent improvements in predictive performance across various settings.
Global constraints and reranking have not been used in cognates detection research to date. We propose methods for using global constraints by performing rescoring of the score matrices produced by state of the art cognates detection systems. Using global constraints to perform rescoring is complementary to state of th…
One of the classical problems in machine learning and data mining is feature selection. A feature selection algorithm is expected to be quick, and at the same time it should show high performance. MeLiF algorithm effectively solves this problem using ensembles of ranking filters. This article describes two different wa…
Enhancement attacks can falsely improve machine learning model performance in biomedical research.
problem The trustworthiness of machine learning in biomedical research is threatened by enhancement attacks.
method Developed two techniques to enhance prediction performance with minimal changes to features.
result Falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities.