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.
Proposes a novel SVM model for binary classification with different misclassification costs.
Efficient algorithm for CLSBM reduces misclassification rate.
We carefully study how well minimizing convex surrogate loss functions, corresponds to minimizing the misclassification error rate for the problem of binary classification with linear predictors. In particular, we show that amongst all convex surrogate losses, the hinge loss gives essentially the best possible bound, o…
Convolutional neural networks improve image classification accuracy.
We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate without having to learn a Bayes-optimal classifier. Our learning to benchmark framework improves on previ…
Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the e…
The paper analyzes classification algorithms on Korobov space and derives learning rates.
Convolutional neural networks converge quickly with gradient descent.
SEDA improves RLDA for high-dimensional data.
We consider a high dimensional binary classification problem and construct a classification procedure by minimizing the empirical misclassification risk with a penalty on the number of selected features. We derive non-asymptotic probability bounds on the estimated sparsity as well as on the excess misclassification ris…
We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighte…
PANDA improves linear discriminant analysis in high dimensions with minimal tuning.
A method to control false membership rate in unsupervised mixture models.
Minimizes indecisions in selective classification to control misclassification rates.
New research shows logistic regression can achieve optimal error rate for agnostic learning of halfspaces.
Study identifies and mitigates causes of image misclassifications in CNN models.
The paper studies how to use AI-generated labels in econometrics to avoid bias.
Recent research has demonstrated the brittleness of machine learning systems to adversarial perturbations. However, the studies have been mostly limited to perturbations on images and more generally, classification that does not deal with temporally varying inputs. In this paper we ask "Are adversarial perturbations po…
Method minimizes total cost of classification by acquiring covariates efficiently.
Paper introduces a new uncertainty measure for misclassification detection.
New algorithm detects communities near KS threshold with optimal rate, even in noisy conditions.
This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track 2) for context-aware movie recommendation systems. The train dataset comprises 4,536,891 ratings provided by 171,670 users on 23,974$ movies, as well as the household groupings of a subset of the users. The test dataset comprises 5,450 rating…
For high-dimensional classification, it is well known that naively performing the Fisher discriminant rule leads to poor results due to diverging spectra and noise accumulation. Therefore, researchers proposed independence rules to circumvent the diverse spectra, and sparse independence rules to mitigate the issue of n…
We analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is conducted via choice-based queries, where the user selects her preferred option among a small subset of offered alternatives. These queries …
Non-convex SGD learns halfspaces with adversarial label noise efficiently.
A new framework selects information sources to test hypotheses robustly, even with misclassifications.
Convolutional neural networks handle rotated image symmetries without dimensionality issues.
In his seminal work, Schapire (1990) proved that weak classifiers could be improved to achieve arbitrarily high accuracy, but he never implied that a simple majority-vote mechanism could always do the trick. By comparing the asymptotic misclassification error of the majority-vote classifier with the average individual …
New method compresses large sample data for faster discriminant analysis.
This work investigates square loss in overparametrized neural networks, revealing its advantages in robustness and calibration.
One-bit clustering method for two-component sub-Gaussian mixture models
Multi-view data, that is matched sets of measurements on the same subjects, have become increasingly common with advances in multi-omics technology. Often, it is of interest to find associations between the views that are related to the intrinsic class memberships. Existing association methods cannot directly incorpora…
Paper develops a novel approach for classifying high-dimensional mixed data.
RaSE ensemble framework improves sparse classification accuracy.
New method improves classification accuracy in imbalanced high-dimensional data.
New algorithm reduces misclassification costs in neural networks.
We propose a new splitting criterion for a meta-learning approach to multiclass classifier design that adaptively merges the classes into a tree-structured hierarchy of increasingly difficult binary classification problems. The classification tree is constructed from empirical estimates of the Henze-Penrose bounds on t…
Proposes cost-sensitive feature selection for SVMs.
Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets. In these scenarios we may need to rely on a human oracle to evaluate individual predictions; presenting the challenge to create query algorithms to guide the search for points that provide the most informa…
Unified framework for cost-sensitive ensemble learning.
In many machine learning applications, crowdsourcing has become the primary means for label collection. In this paper, we study the optimal error rate for aggregating labels provided by a set of non-expert workers. Under the classic Dawid-Skene model, we establish matching upper and lower bounds with an exact exponent …
Optimal subset selection for hypothesis testing with penalties.
Study improves theoretical understanding of Bayesian deep learning for classification tasks.
We study the problem of {\em distribution-independent} PAC learning of halfspaces in the presence of Massart noise. Specifically, we are given a set of labeled examples drawn from a distribution on such that the marginal distribution on the unlabeled points $\mathbf{x}…
Despite having excellent performances for a wide variety of tasks, modern neural networks are unable to provide a reliable confidence value allowing to detect misclassifications. This limitation is at the heart of what is known as an adversarial example, where the network provides a wrong prediction associated with a s…
Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted M…
Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment…
Optimal classification rules control error rates in multiclass mixture models.