HMMs improve music transcription accuracy.
problem Improving automatic transcription of music.
method Employed PLCA for multi-pitch estimation and integrated HMMs for note segmentation and post-processing.
result HMMs enhance transcription accuracy on different instruments.
A method for generating future samples in auto-regressive processes using confidence-based sampling.
problem Generating future samples in auto-regressive processes efficiently and accurately.
method Confidence-based sampling to simplify and parallelize the auto-regressive generation process.
result The method successfully captures complex data structures and generates meaningful future samples with lower computational cost.
Study evaluates post-processing methods for improving solar power forecasts.
problem Improving accuracy of probabilistic solar energy forecasts through model chain approaches.
method Systematically evaluates different post-processing strategies for ensemble weather forecasts and direct solar power forecasting.
result Post-processing significantly improves solar power generation forecasts, especially when applied to power predictions.
Post-processing corrects bias in ML systems without retraining.
problem Correcting bias in ML systems that are already in use.
method Proposes general post-processing algorithms for individual fairness based on graph Laplacian regularization.
result Empirically, post-processing algorithms correct individual biases in large-scale NLP models while preserving accuracy.
MimickNet approximates clinical ultrasound post-processing without proprietary data.
problem Matching proprietary clinical-grade ultrasound post-processing techniques.
method Deep learning framework MimickNet that transforms raw DAS beams into post-processed images.
result MimickNet achieves high SSIM scores (0.930-0.967) on test sets.
This paper combines and improves probabilistic forecasts of wind speeds using advanced statistical methods.
problem Improving accuracy and reliability of probabilistic forecasts in wind speed prediction.
method Adapting prediction with expert advice theory to probabilistic forecasts, combining raw or post-processed ensembles, and using CRPS and Jolliffe-Primo tests.
result Combining probabilistic forecasts can lead to more reliable and skillful predictions, as shown by the Jolliffe-Primo test.
Algorithm ensures demographic parity in regression without sensitive attribute data.
problem Performing regression with demographic parity constraints.
method Post-processing algorithm using accurate estimates and sensitive attribute predictor.
result Generates predictions meeting demographic parity constraint.
Proposes a sequential framework for fairness in multiple sensitive attributes.
problem Fairness in the presence of multiple sensitive attributes.
method Sequential framework using multi-marginal Wasserstein barycenters.
result Closed-form solution for sequentially fair predictor.
Risk-controlled post-processing optimizes decision policies under risk constraints.
problem Optimizing decision policies with risk constraints for better outcomes.
method Developed a post-processing algorithm that selects a threshold based on fitted fallback policy and score, leveraging tools from algorithmic stability and stochastic processes.
result The post-processed policy achieves precise expected risk control under exchangeability and meets or nearly meets risk budgets while preserving more agreement with the baseline.
Neural networks improve post-processing of ensemble weather forecasts.
problem Improving reliability and accuracy of probabilistic weather forecasts.
method Flexible neural network approach for estimating forecast distribution parameters.
result Neural network approach significantly outperforms traditional methods in 2-meter temperature forecasts.
Post-process Bayesian inference speeds up posterior approximation.
problem Leveraging pre-existing model evaluations for quick posterior approximation.
method Variational Sparse Bayesian Quadrature (VSBQ) using sparse Gaussian process (GP) surrogate model.
result VSBQ builds high-quality posterior approximations from existing optimization traces.
New algorithm reduces bias in trained models, near-optimal performance proven.
problem Reduction of bias in trained machine learning models.
method Scalable post-processing algorithm for debiasing trained models, including deep neural networks (DNNs).
result Proven to be near-optimal by bounding its excess Bayes risk.
Deep neural networks improve ensemble weather forecasts.
problem Improving accuracy and efficiency of ensemble weather forecasts.
method Mixed model combining subset of trajectories with deep neural networks for post-processing.
result Achieved over 14% relative improvement in ensemble forecast skill.
The paper explores how mixing and diffusion mechanisms can enhance privacy in data processing.
problem Enhancing privacy guarantees of data mechanisms through post-processing.
method The study uses Markov operators and coupling arguments to analyze privacy amplification.
result The introduction of a new family of diffusion-based mechanisms that are closed under post-processing.
A graph neural network improves multivariate post-processing of ensemble forecasts.
problem Systematic biases in ensemble forecasts and loss of dependencies across forecast dimensions.
method A composite-Loss Graph Neural Network (dualGNN) trained with a composite loss function combining ES and VS.
result The dualGNN outperforms traditional methods in multivariate verification metrics and captures spatial relationships.
Unsupervised method improves word vectors by suppressing high variance features.
problem Improving semantic information in word vectors.
method Using conceptors to suppress high variance features in word vectors.
result Post-processed word vectors outperform existing alternatives in lexical evaluation tasks.
The study explores fairness in classifier post-processing methods.
problem Achieving fairness in binary decision-making classifiers with imperfect information.
method Examines fairness properties of post-processing calibrated scores and deferring decisions.
result Deferring decisions can help achieve fairness in PPV, NPV, FPR, and FNR across protected groups.
New post-processing methods improve word embedding performance.
problem Boosting the performance of word embeddings for similarity and analogy tasks.
method Optimizing a semi-Riemannian manifold with Centralised Kernel Alignment (CKA) to shrink the covariance matrix towards a scaled identity matrix.
result Improved performance on downstream tasks after smoothing the spectrum of word vectors.
Post-processing predictors reduces calibration errors for decision-making.
problem Predictors with low calibration error for machine learning may have high error for decision-making.
method Post-processing with ε distance to calibration adds noise to make predictions differentially private.
result Post-processing achieves O(√ε) ECE and CDL, asymptotically optimal.
Neural network-based post-processing improves ensemble forecast sharpness
problem Reducing the width of central prediction intervals in ensemble forecasts
method Extending loss function with a penalty term
result 8.2%-12.5% reduction in width of central prediction interval
Paper proposes a new method for probabilistic electricity price forecasting.
problem Accurate estimation of forecast uncertainties for optimal decision making.
method Implicit generative ensemble post-processing using an ensemble of point forecasting models.
result Method outperforms well-established model combination benchmarks.
New method improves decision-making accuracy without complex calculations.
problem Improving decision-making accuracy in machine learning.
method Introducing a new measure called calibration decision loss (CDLK) for structured families of post-processing functions. result Proves upper and lower bounds for natural classes K of post-processing functions. Enhances fairness in multi-output models using optimal transport.
problem Improving fairness in multi-output models like multi-task/multi-class classification and representation learning.
method Post-processing method using optimal transport mappings to move model outputs towards empirical Wasserstein barycenter.
result Demonstrates effectiveness of the proposed approach on multi-task/multi-class classification and representation learning tasks.
Enhances fairness in predictions without sacrificing accuracy.
problem Balancing fairness and predictive performance in machine learning.
method Model ensemble-based post-processing framework.
result Framework effectively enhances fairness while maintaining predictive accuracy.
New method improves fairness in biased predictions.
problem Improving fairness in biased classifier predictions.
method Individual bias detector prioritizes data samples for a bias mitigation algorithm.
result Superior performance in individual and group fairness on real-world datasets.
Post-processing improves WMH segmentation accuracy from randomly-initialized U-nets.
problem Improving WMH segmentation accuracy from randomly-initialized U-nets.
method Post-processing technique involving thresholding and averaging U-net outputs.
result Superior performance of the proposed post-processing method.
Develops a fair post-processing method for student success predictions.
problem Ensuring fairness in predictive student models for educational applications.
method Uses the MADD metric to improve model fairness while maintaining accuracy.
result Successfully improved fairness of predictive models for student success.
Unified approach adjusts classifiers to meet system-level constraints.
problem Multi-class classification under system-level constraints.
method Post-processing approach using linearly constrained stochastic program and entropic regularization.
result Finite-sample guarantees for risk and constraint satisfaction.
Meta-theorems validate fair regression algorithms under demographic parity constraints.
problem Regression under demographic parity constraints.
method Meta-theorems and post-processing methods.
result Fair minimax optimal regression can be achieved through post-processing.
Enhanced visibility forecasts using CAMS data improve accuracy.
problem Improving the accuracy of visibility predictions in weather forecasts.
method Statistical post-processing with historical observations and CAMS forecasts.
result Post-processed forecasts with CAMS data are substantially superior to raw and climatological predictions.
The study compares statistical post-processing methods for solar radiation forecasts.
problem Improving accuracy and uncertainty quantification of solar radiation forecasts.
method Statistical post-processing techniques using relationships between meteorological variables and solar radiation.
result Quantile regression and generalized random forests generally perform best in probabilistic forecasts.
Transformers improve solving mixed-integer programs, especially CLSP.
problem Solving Capacitated Lot Sizing Problem (CLSP) with mixed-integer programming.
method Employing transformer models to predict binary variables in CLSP.
result Transformer model outperforms CPLEX and LSTM in solving CLSP.
ForestPrune optimizes tree ensemble pruning for compactness and speed.
problem Large tree ensembles in predictive models consume excessive memory and reduce interpretability.
method Developed a specialized optimization algorithm to efficiently prune tree ensembles by depth layers.
result ForestPrune produces compact, high-performing models that outperform existing post-processing methods.
New method wraps black-box classifiers to reduce bias.
problem Reduction of bias in black-box predictions.
method Post-processing with α-trees and boosting algorithms.
result Demonstrated effectiveness in reducing bias across multiple fairness metrics.
Framework improves fairness in predictions across subgroups.
problem Systematic biases in machine learning predictions.
method Multiaccuracy auditing and post-processing for black-box models.
result Improves accuracy for minority subgroups in diverse applications.
In this article, we have proposed several approaches for post processing a large ensemble of prediction models or rules. The results from our simulations show that the post processing methods we have considered here are promising. We have used the techniques developed here for estimation of quantitative traits from mar…
Improved visibility forecasts using statistical post-processing.
problem Accurate and reliable predictions of visibility are crucial in aviation and transportation.
method Calibrated ensemble forecasts using locally, semi-locally, and regionally trained POLR and MLP classifiers.
result Post-processing improves forecast skill and POLR models outperform MLPs.
Paper characterizes fairness vs. accuracy tradeoff in classification.
problem Mitigating bias in machine learning models.
method Characterizes the tradeoff between fairness and accuracy, provides a post-processing algorithm.
result Post-processing algorithm yields optimal fair classifier when score is Bayes optimal.
RAMPART ranks top-k features more accurately than existing methods.
problem Accurate ranking of important features in machine learning.
method Adaptive sequential halving strategy combined with ensembling techniques.
result RAMPART achieves the correct top-k ranking with high probability.
This paper tackles inventory control with general arrival dynamics and post-processing, improving profitability.
problem Inventory control with arbitrary arrival dynamics and post-processing constraints.
method Formulated as an exogenous decision process, incorporating deep generative models for arrivals, and applying supervised learning techniques.
result Improves profitability over production baselines and real-world A/B test data.
GAMA is a user-friendly AutoML system for machine learning pipeline optimization.
problem Empowering users to control and optimize machine learning pipelines.
method Modular AutoML system with three search algorithms and two post-processing steps.
result GAMA allows users to track and control the search process for optimal machine learning pipelines.
Post-processes deep networks with StoNet to quantify uncertainty.
problem Uncertainty quantification in predictions from large-scale deep neural networks.
method Feeds DNN output into StoNet, trains StoNet with sparse penalty, constructs prediction intervals.
result Proposed approach constructs honest confidence intervals with shorter lengths and better calibration.
HLTF generates chemically valid 3D molecules with improved topology control.
problem Generating chemically valid 3D molecules is challenging due to bond topology errors.
method HLTF uses a latent multi-scale plan for global context and a constraint-aware sampler to suppress topology-driven failures.
result HLTF achieves high validity and uniqueness on QM9 and GEOM-DRUGS datasets.
Framework purifies approximate differential privacy to pure differential privacy.
problem Achieving pure differential privacy from approximate differential privacy.
method Randomized post-processing with calibrated noise to eliminate δ parameter.
result First statistically and computationally efficient reduction from approximate DP to pure DP.
CPP improves predictive model outputs for better intervention decisions.
problem Predictive models often misguide resource allocation.
method Causal post-processing techniques using limited experimental data.
result CPP can improve intervention decisions, especially with imperfect causal signals.
Accurate calibration of probabilistic predictive models learned is critical for many practical prediction and decision-making tasks. There are two main categories of methods for building calibrated classifiers. One approach is to develop methods for learning probabilistic models that are well-calibrated, ab initio. The…
Efficiently searches for optimal neural architecture and hyperparameters.
problem Separate tuning of architecture and hyperparameters leads to suboptimal results.
method Combines Bayesian optimization and Hyperband for joint search.
result Joint search yields better results with fewer epochs.
New method removes MRI banding without post-processing.
problem MRI banding in reconstructed images.
method Adversarial training to penalize banding structures.
result Superior banding removal compared to baseline.