arXiv research

Non-intrusive algorithm corrects corrupted data in classification models.

MaxEnt Model Correction improves reinforcement learning model accuracy.

New algorithms improve boosting by optimizing chance-corrected measures.

Given a finite set of unknown distributions or arms that can be sampled, we consider the problem of identifying the one with the maximum mean using a $δ$-correct algorithm (an adaptive, sequential algorithm that restricts the probability of error to a specified $δ$) that has minimum sample complexity. Lower bounds for …

Paper tackles graph matching with partially correct seeds, improving performance guarantees.

Cancer analysis and prediction is the utmost important research field for well-being of humankind. The Cancer data are analyzed and predicted using machine learning algorithms. Most of the researcher claims the accuracy of the predicted results within 99%. However, we show that machine learning algorithms can easily pr…

DisCor corrects reinforcement learning issues by re-weighting collected data.

In distributed ML applications, shared parameters are usually replicated among computing nodes to minimize network overhead. Therefore, proper consistency model must be carefully chosen to ensure algorithm's correctness and provide high throughput. Existing consistency models used in general-purpose databases and moder…

Noise-corrected Langevin algorithm improves sampling from noisy data.

Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently. However, learning from demonstrations often suffers from the covariate shift problem, which results in cascading errors of the learned policy. We introduce a notion of conservati…

This paper corrects the proof of the Theorem 2 from the Gower's paper \cite[page 5]{Gower:1982} as well as corrects the Theorem 7 from Gower's paper \cite{Gower:1986}. The first correction is needed in order to establish the existence of the kernel function used commonly in the kernel trick e.g. for $k$-means clusterin…

In this paper, we propose a provably correct algorithm for convolutive nonnegative matrix factorization (CNMF) under separability assumptions. CNMF is a convolutive variant of nonnegative matrix factorization (NMF), which functions as an NMF with additional sequential structure. This model is useful in a number of appl…

Paper corrects bias in online learning algorithms with endogenous data.

QC-ST and CoCo methods correct batch effects in metabolomics data.

We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algori…

YOASOVI improves stochastic VI for large models with fast, self-correcting sampling.

In recent years several trading platforms appeared which provide a backtest engine to calculate historic performance of self designed trading strategies on underlying candle data. The construction of a correct working backtest engine is, however, a subtle task as shown by Maier-Paape and Platen (cf. arXiv:1412.5558 [q-…

New SGD algorithm finds critical points faster with second-order corrections.

New MCMC method corrects bias without extra cost.

New method improves sampling from score-based models by correcting bias.

Corrects pseudo log-likelihood method issues in various applications.

New method combines machine learning with data assimilation for model error correction.

Iterative method 'Concent' corrects spectrum bias in covariance matrices.

MAFLA improves sampling from heavy-tailed distributions using MH-inspired corrections.

Post-processing corrects bias in ML systems without retraining.

We model and correct bias in sequential evaluation, improving ranking accuracy.

WeMix improves data augmentation by correcting bias in deep learning.

We consider community detection in Degree-Corrected Stochastic Block Models (DC-SBM). We propose a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show that this algorithm consistently recovers the block-membership of all but a vanishing fraction of nodes, in the regime where the lowes…

Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease th…

We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that leverages predictive models to accelerate policy learning. The new "PicCoLOed" algorithm optimizes a policy by recursively repeating two ste…

Resampling outperforms reweighting for correcting biased data in machine learning models.

Warfarin is one of the most commonly used oral blood anticoagulant agent in the world, the proper dose of Warfarin is difficult to establish not only because it is substantially variant among patients, but also adverse even severe consequences of taking an incorrect dose. Typical practice is to prescribe an initial dos…

Survey of algorithms to correct past mistakes in prediction.

A new boosting method corrects endogeneity bias in instrumental variable regression.

The paper corrects bias in fluid approximation for better decision-making in stochastic optimization.

Belief Propagation (BP) is one of the most popular methods for inference in probabilistic graphical models. BP is guaranteed to return the correct answer for tree structures, but can be incorrect or non-convergent for loopy graphical models. Recently, several new approximate inference algorithms based on cavity distrib…

The paper corrects for node degree in spectral clustering using random walk Laplacian.

New technique tracks geometric properties to correct base algorithms' poor performance.

Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct resu…

Paper corrects GIRP algorithm to ensure isotonic models.

A new method improves super learner validation efficiency.

Improved LDA method for better classification and dimensionality reduction.

The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genetic algorithms have been used successfully for many problems, one criticism is that hand-crafting its fitness function, the test that aims to…

dtControl uses decision trees to represent controllers efficiently and explainably.

Develops a new tensor model for clustering with degree correction.

Single model corrects JPEG artifacts for various compression settings.

New algorithm corrects bias in LDP-released data for better analysis.

This paper certifies cluster assignments from sum-of-norms clustering algorithms.