A real-time adaptive background subtraction framework using a rejection cascade of Gaussians.
problem Real-time background subtraction with pixel-wise modeling trade-offs.
method Decompose Gaussian Mixture Model into an adaptive cascade of Gaussians (CoG).
result 4-5x speed-up and 17% accuracy improvement over baseline.
A new jet constituent-based method for top quark tagging achieves high background rejection.
problem Tagging highly energetic jets resulting from top quark decays.
method Sequential approach using ordered jet constituents as inputs, avoiding loss of information.
result Achieves a background rejection of 45 at a 50% efficiency operating point.
SCALOR learns scalable object representations for crowded scenes.
problem Scalability in scenes with many objects.
method Spatially-parallel attention and proposal-rejection mechanisms.
result SCALOR can handle up to a hundred objects in crowded scenes.
Study neural networks in particle physics, identifying key features for stop/top discrimination.
problem Discriminating supersymmetric stop production from Standard Model backgrounds.
method Gradient ascent on input space, artificial event generation, contour maps, mutual information analysis.
result Identifies neurons with high mutual information with mT2ℓℓ, crucial for stop/top discrimination. PRS improves rejection sampling by learning better proposals.
problem High rejection rate in traditional rejection sampling.
method PRS uses a kernel estimator to learn better sampling proposals.
result PRS guarantees a low number of accepted samples.
Paper tackles regression with cost-based rejection, balancing prediction and rejection costs.
problem Regression with cost-based rejection, balancing prediction and rejection costs in a continuous target space.
method Formulated expected risk, derived Bayes optimal solution, proposed surrogate loss function.
result Bayes optimal solution can be recovered by the proposed surrogate loss function.
We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling …
Algorithmic trading systems on DEXs reject most candidate tokens, but the counterfactual outcome of rejected candidates is rarely measured.
problem Measuring the counterfactual outcome of rejected tokens in algorithmic trading systems on decentralized exchanges.
method Post-Rejection Follow-up Sampling (PRFS) introduces a methodology for collecting and analyzing the data needed to evaluate filter precision against actual market outcomes of rejected candidates.
result PRFS produces the data needed to evaluate filter precision against actual market outcomes of rejected candidates, not against synthetic backtest reconstructions.
RMT-Net tackles biased credit scoring data by learning from both default/non-default and rejection/approval tasks.
problem Missing-not-at-random selection bias in financial credit scoring data.
method Reject-aware Multi-Task Network (RMT-Net) that leverages the correlation between default/non-default and rejection/approval tasks.
result RMT-Net improves credit scoring models by learning from both default/non-default and rejection/approval tasks.
Optimal regression with reject option using conditional variance thresholding.
problem Regression with reject option to handle uncertain predictions.
method Derive optimal rule based on thresholding conditional variance, semi-supervised estimation using labeled and unlabeled data.
result The predictor with reject option is almost as good as the optimal predictor in terms of risk and rejection rate.
New algorithm reduces rejection rate in adaptive rejection sampling.
problem Reducing high rejection rates in adaptive rejection sampling.
method Introduces a minimax near-optimal algorithm for adaptive rejection sampling.
result Guarantees a near-optimal rejection rate in a minimax sense.
Paper proposes a self-learning framework for reject inference in credit scoring.
problem Sample bias in credit scoring models due to training on accepted cases only.
method Develops a self-learning framework considering distinct training regimes for iterative labeling and model training, introduces a new evaluation measure.
result Demonstrates the superiority of the adjusted self-learning framework over regular self-learning and previous reject inference strategies.
New models use deep learning to predict creditworthiness of rejected applications.
problem Credit scoring models can be biased and reject inference is needed to improve accuracy.
method Developed semi-supervised Bayesian models using deep generative models and Gaussian mixture.
result Proposed models outperform classical and alternative machine learning models in credit scoring.
A reject option improves partial-label learning's accuracy.
problem Ambiguously labeled data in real-world applications.
method Risk-consistent nearest-neighbor algorithm with a reject option.
result Our method provides the best trade-off between non-rejected predictions' number and accuracy.
SelectiveNet optimizes deep neural networks with a built-in reject option.
problem Selective prediction in deep neural networks.
method End-to-end training of a deep neural architecture to optimize both classification and rejection.
result Improved risk-coverage trade-off over various datasets.
A dealer manages quotes and rejection rules to control slippage risk in FX markets.
problem Managing inventory risk and latency risk in OTC FX market making.
method Dynamic programming and adiabatic-quadratic approximation to optimize quotes and rejection rules.
result Developed a method to optimize quotes and rejection rules for managing slippage risk.
A new approach to model rejection using density ratios.
problem Improving model performance through selective prediction.
method Optimization of a loss's risk with φ-divergence regularization to find an idealized data distribution.
result Model rejection can be made by comparing the density ratio of the idealized distribution to the actual data distribution.
Delayed rejection HMC improves sampling efficiency for multiscale distributions.
problem Hamiltonian Monte Carlo struggles with wide-ranging distributions, especially in high-curvature areas.
method Introduces a delayed rejection variant of HMC, using geometrically smaller step sizes for retries.
result Up to five-fold performance gains in effective sample size per gradient evaluation.
Paper connects rejection learning to Bhattacharyya divergence.
problem Learning models to abstain from predictions.
method Developed a link between rejection and thresholding different statistical divergences, focusing on Bhattacharyya divergence.
result Rejector obtained by joint ideal distribution corresponds to thresholding of skewed Bhattacharyya divergence.
A method for making predictions with a reject option using conformal prediction.
problem Uncertainty in machine learning predictions, especially when models are unsure.
method Formalizing ML with reject option, using conformal prediction for distribution-free error guarantees.
result Theoretical guarantees on error rate for prediction sets with distribution-free validity.
This paper ranks pre-trained DNNs using a novel SI measure.
problem Optimizing pre-trained DNN selection for transfer learning.
method Automated ranking via Separation Index (SI) on target datasets.
result Ranked pre-trained DNNs improve classification performance.
Paper proposes algorithms for active learning of reject option classifiers.
problem Active learning of reject option classifiers is unaddressed in machine learning.
method Developed novel algorithms using double ramp and double sigmoid loss functions.
result Proposed algorithms efficiently reduce the number of labeled examples required.
New method reduces infinite variance in probabilistic programs with rejection sampling.
problem Infinite variance in naive importance sampling for programs with rejection sampling.
method Developed a new amortized importance sampling estimator with finite variance proof.
result Empirically demonstrated efficiency and correctness compared to existing alternatives.
A neural framework for learning with rejection for classification and regression.
problem Developing machine learning systems that can abstain from generating predictions when reliability is low.
method A neural framework involving a predictor and a rejecter model for simultaneous training.
result The proposed method performs at par or better than previously proposed methods for both classification and regression.
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
problem Avoid risky misclassification in error-critical applications.
method Learning an ensemble of cost-sensitive classifiers.
result Improved classification accuracy and flexibility in loss selection.
GWINs improve classifier accuracy by translating uncertain observations.
problem Improving accuracy of uncertain observations in classifiers.
method Generative network recovers correct observation distributions, reject option allows for uncertain predictions.
result GWINs significantly improve classifier accuracy on benchmark datasets.
Study compares rejection policies to acceptance policies for lead-time and price-sensitive demand.
problem Optimizing firm's policy under lead time and price-dependent demand.
method Analytical comparison of M/M/1/1 and M/M/1 models with and without holding and penalty costs.
result Rejection policy can be more profitable under certain conditions.
New method allows reparameterization for acceptance-rejection sampling.
problem Applying reparameterization trick to complex distributions.
method Proposes a new method for reparameterization gradients with acceptance-rejection sampling.
result Significantly lower variance in gradient estimator.
New particle filter estimates model evidence without bias.
problem Unbiased estimation of marginal likelihood for model comparison.
method Particle filter with rejection control.
result Unbiased estimation of marginal likelihood.
New algorithms reduce rejection sampling complexity for shape-constrained distributions.
problem Generating exact samples from shape-constrained distributions efficiently.
method Sublinear query complexity algorithms for rejection sampling.
result Sublinear complexity algorithms for sampling from shape-constrained distributions.
LSTM networks improve top jet tagging at the LHC.
problem Boosted top quark tagging at the LHC.
method Incorporating Long Short-Term Memory (LSTM) networks into jet constituent analysis.
result Best LSTM network achieves 100 background rejection at 50% signal efficiency.
Proposes a method combining CNFs and rejection-resampling for sampling from unnormalized densities.
problem Sampling from unnormalized probability densities, especially multimodal ones.
method Combines continuous normalizing flows with rejection-resampling steps based on importance weights.
result The method improves sampling accuracy and performance compared to state-of-the-art methods.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
problem Analyzing algorithmically-rejected trading events for insights into market dynamics.
method Public dataset of 6,660 algorithmically-rejected trading events, linked to post-rejection price and liquidity trajectories.
result First window of a planned series of datasets extending the time horizon and enabling regime-stratified analysis.
Generative classifier derived from any discriminative classifier rejects illegal inputs.
problem Detecting and rejecting illegal inputs like adversarial examples and out-of-distribution samples.
method SDIM-logit: learns generative classifier from logits of any discriminative classifier, imposing statistical constraints.
result SDIM-logit inherits performance of base classifier without loss and can reject illegal inputs.
Investigates multiclass classification with rejection, achieving state-of-the-art performance and deriving calibration conditions.
problem Multiclass classification with rejection, where a classifier can choose not to predict.
method Two approaches: simultaneous training of classifier and rejector, and confidence scores with rejection criteria.
result Calibration is hard for general loss functions in multiclass case, but achievable with specific rejection criteria.
Generation of pseudorandom numbers from different probability distributions has been studied extensively in the Monte Carlo simulation literature. Two standard generation techniques are the acceptance-rejection and inverse transformation methods. An alternative approach to Monte Carlo simulation is the quasi-Monte Carl…
A novel rejection sampling step improves variational inference for latent variable models.
problem High variance in gradient estimates for approximate posterior in stochastic variational inference.
method Rejection sampling to discard low-likelihood samples and a new gradient estimator.
result Improves marginal log-likelihood estimation by 3.71 nats and 0.21 nats.
New methods for multicategory classification with reject and refine options reduce misclassification costs.
problem Reducing misclassification costs in multicategory classification problems.
method Margin-based multicategory classification methods with reject and refine options.
result The refine option provides more constructive information by ruling out implausible classes.
Accelerated RPCholesky speeds up kernel matrix approximations.
problem Efficiently approximating large kernel matrices.
method Accelerated randomly pivoted Cholesky (RPCholesky) with block matrix computations and rejection sampling.
result Approximates kernel matrices up to 40 times faster.
Framework to mitigate adversarial attacks by allowing classifiers to abstain.
problem Vulnerability of classifiers to adversarial examples.
method Adversarial training with a rejection option.
result ATRO framework improves classifier reliability against adversarial attacks.
Improved inference for models with continuous latent variables.
problem Inference accuracy with traditional variational methods is limited.
method Reparameterized Variational Rejection Sampling (RVRS) using a proposal distribution with a reparameterized gradient estimator.
result RVRS offers a better trade-off between computational cost and inference fidelity.
We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection for typical hyperparameters. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions…
Marxism's rate of profit thesis rejected as illogical.
problem Marxism's rate of profit thesis is challenged as illogical.
method Rejects Marx's thesis on the long-term tendency of the rate of profit to fall.
result Marxism's rate of profit thesis is rejected as illogical.
DRS uses a GAN discriminator to improve sampling accuracy.
problem Improving sampling accuracy in GANs.
method Rejection sampling using discriminator output.
result DRS improves Inception Score and FID on ImageNet.
This paper characterizes and designs loss functions for robust classification with abstention.
problem Ensuring robustness against adversarial attacks and knowing when to abstain from prediction.
method Proposes adversarial robust reject option loss and characterizes surrogates for calibration.
result Shifted Double Ramp Loss and Shifted Double Sigmoid Loss satisfy the calibration conditions.
Study proposes framework for selective classification using uncertainty quantification.
problem Unintended consequences of deep learning in selective classification.
method Mixed-integer programming framework combining model uncertainty and predictive mean.
result Framework outperforms industry standard methods significantly for online fraud management.
We consider bosonic supersymmetric backgrounds of ten-dimensional conformal supergravity. Up to local conformal isometry, we classify the maximally supersymmetric backgrounds, determine their conformal symmetry superalgebras and show how they arise as near-horizon geometries of certain half-BPS backgrounds or as a plan…
The study proposes a framework to accept OOD data based on competence scores.
problem Silent failures in Domain Generalization where models reject OOD data without proper justification.
method A learning to reject framework using proxy incompetence scores to predict trustworthiness.
result Increasing incompetence scores are predictive of reduced accuracy, but not always favorable for accuracy/rejection trade-off.