A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
PRS improves rejection sampling by learning better proposals.
Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample from distributions admitting a probability density function which can be evaluated exactly at any given point, albeit at a high computational cost. However, without proper tuning, this technique implies a high rejection rate. Several methods h…
Paper reinterprets ARP algorithm and improves its analysis and speed.
Improved adaptive algorithms for identifying the best arm in MABs with fixed budget.
Optimal transport method rejects new classes and adjusts class ratios for open set domain adaptation.
This work addresses two classification problems that fall under the heading of domain adaptation, wherein the distributions of training and testing examples differ. The first problem studied is that of class proportion estimation, which is the problem of estimating the class proportions in an unlabeled testing data set…
Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading
ATLAS adapts HMC step size and trajectory length for complex geometries.
Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the reject…
DART optimizes subset selection in non-linear bandit problems.
EERO optimizes resource usage for efficient classification.
Paper tackles regression with cost-based rejection, balancing prediction and rejection costs.
ABI bypasses likelihood intractability with nonparametric distribution matching.
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 …
A good clustering can help a data analyst to explore and understand a data set, but what constitutes a good clustering may depend on domain-specific and application-specific criteria. These criteria can be difficult to formalize, even when it is easy for an analyst to know a good clustering when she sees one. We presen…
New method controls false discoveries in online testing with deadlines.
Algorithmic trading systems on DEXs reject most candidate tokens, but the counterfactual outcome of rejected candidates is rarely measured.
RMT-Net tackles biased credit scoring data by learning from both default/non-default and rejection/approval tasks.
Optimal regression with reject option using conditional variance thresholding.
A reject option improves partial-label learning's accuracy.
We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets. We define a maximum entropy regularised objective function, referred to as generalised speed measure, which can be robustly optimised over the parameters of the proposal di…
A dealer manages quotes and rejection rules to control slippage risk in FX markets.
A new approach to model rejection using density ratios.
Delayed rejection HMC improves sampling efficiency for multiscale distributions.
Credit scoring models support loan approval decisions in the financial services industry. Lenders train these models on data from previously granted credit applications, where the borrowers' repayment behavior has been observed. This approach creates sample bias. The scoring model (i.e., classifier) is trained on accep…
Paper connects rejection learning to Bhattacharyya divergence.
In this work, we propose a method to reject out-of-distribution samples which can be adapted to any network architecture and requires no additional training data. Publicly available chest x-ray data (38,353 images) is used to train a standard ResNet-50 model to detect emphysema. Feature activations of intermediate laye…
Private online FDR control for adaptive testing under differential privacy.
Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-s…
A method for making predictions with a reject option using conformal prediction.
We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated reject option. Existing rejection mechanisms are based mostly on a threshold over the prediction confidence of a pre-trained network. In contra…
ParaDRAM automates parallel MCMC simulations across languages.
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial perception is jeopardized by the presence of incorrect data association, and in gener…
New algorithms reduce rejection sampling complexity for shape-constrained distributions.
Proposes a method combining CNFs and rejection-resampling for sampling from unnormalized densities.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
Learning with rejection (LWR) allows development of machine learning systems with the ability to discard low confidence decisions generated by a prediction model. That is, just like human experts, LWR allows machine models to abstain from generating a prediction when reliability of the prediction is expected to be low.…
We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers. A conditional generative network recovers the distribution of observations that the classifier labels correctly with high certainty. We introduce a reject option to the cl…
Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for a…
We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood …
This paper reviews information theory in open-world machine learning.
There are many statistical tests that verify the null hypothesis: the variable of interest has the same distribution among k-groups. But once the null hypothesis is rejected, how to present the structure of dissimilarity between groups? In this article, we introduce The Merging Path Plot - a methodology, and factorMerg…
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…
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we de…
Study aggregation of statistical evidence under unknown dependence using group-invariance.
AdaPT-GMM improves multiple testing power with covariates.
NUTS mixing time scales as d^(1/4) for Gaussian distributions.