Related papers: RoLNiP: Robust Learning Using Noisy Pairwise Comparisons

RoLNiP: Robust Learning Using Noisy Pairwise Comparisons

URL: http://arxiv.org/abs/2303.02341v1
Date: Sat, 4 Mar 2023 06:28:08 GMT
Title: RoLNiP: Robust Learning Using Noisy Pairwise Comparisons
Authors: Samartha S Maheshwara and Naresh Manwani
Abstract summary: This paper presents a robust approach for learning from noisy pairwise comparisons. We experimentally show that the proposed approach RoLNiP outperforms the robust state-of-the-art methods for learning with noisy pairwise comparisons.
Score: 6.624726878647541
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper presents a robust approach for learning from noisy pairwise comparisons. We propose sufficient conditions on the loss function under which the risk minimization framework becomes robust to noise in the pairwise similar dissimilar data. Our approach does not require the knowledge of noise rate in the uniform noise case. In the case of conditional noise, the proposed method depends on the noise rates. For such cases, we offer a provably correct approach for estimating the noise rates. Thus, we propose an end-to-end approach to learning robust classifiers in this setting. We experimentally show that the proposed approach RoLNiP outperforms the robust state-of-the-art methods for learning with noisy pairwise comparisons.

Related papers

Denoising-Aware Contrastive Learning for Noisy Time Series [35.97130925600067]
Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. We propose denoising-aware contrastive learning (DECL) to mitigate the noise in the representation and automatically selects suitable denoising methods for every sample.
arXiv Detail & Related papers (2024-06-07T04:27:32Z)
May the Noise be with you: Adversarial Training without Adversarial Examples [3.4673556247932225]
We investigate the question: Can we obtain adversarially-trained models without training on adversarial? Our proposed approach incorporates inherentity by embedding Gaussian noise within the layers of the NN model at training time. Our work contributes adversarially trained networks using a completely different approach, with empirically similar robustness to adversarial training.
arXiv Detail & Related papers (2023-12-12T08:22:28Z)
Noisy Pair Corrector for Dense Retrieval [59.312376423104055]
We propose a novel approach called Noisy Pair Corrector (NPC) NPC consists of a detection module and a correction module. We conduct experiments on text-retrieval benchmarks Natural Question and TriviaQA, code-search benchmarks StaQC and SO-DS.
arXiv Detail & Related papers (2023-11-07T08:27:14Z)
Label Noise: Correcting the Forward-Correction [0.0]
Training neural network classifiers on datasets with label noise poses a risk of overfitting them to the noisy labels. We propose an approach to tackling overfitting caused by label noise. Motivated by this observation, we propose imposing a lower bound on the training loss to mitigate overfitting.
arXiv Detail & Related papers (2023-07-24T19:41:19Z)
Latent Class-Conditional Noise Model [54.56899309997246]
We introduce a Latent Class-Conditional Noise model (LCCN) to parameterize the noise transition under a Bayesian framework. We then deduce a dynamic label regression method for LCCN, whose Gibbs sampler allows us efficiently infer the latent true labels. Our approach safeguards the stable update of the noise transition, which avoids previous arbitrarily tuning from a mini-batch of samples.
arXiv Detail & Related papers (2023-02-19T15:24:37Z)
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation [80.07065346699005]
It is widely assumed that the optimal noise distribution should be made equal to the data distribution, as in Generative Adversarial Networks (GANs) We turn to Noise-Contrastive Estimation which grounds this self-supervised task as an estimation problem of an energy-based model of the data. We soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.
arXiv Detail & Related papers (2023-01-23T19:57:58Z)
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think [80.07065346699005]
We show that deviating from this assumption can actually lead to better statistical estimators. In particular, the optimal noise distribution is different from the data's and even from a different family.
arXiv Detail & Related papers (2022-03-02T13:59:20Z)
Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery [8.581512812219737]
We propose a new framework for low-rank matrix recovery from observations corrupted with noise and outliers. Inspired by the square root Lasso, this new formulation does not require prior knowledge of the noise level. We show that a single, universal choice of the regularization parameter suffices to achieve reconstruction error proportional to the (a priori unknown) noise level.
arXiv Detail & Related papers (2021-06-17T02:28:11Z)
Learning with Group Noise [106.56780716961732]
We propose a novel Max-Matching method for learning with group noise. The performance on arange of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching.
arXiv Detail & Related papers (2021-03-17T06:57:10Z)
Robust Imitation Learning from Noisy Demonstrations [81.67837507534001]
We show that robust imitation learning can be achieved by optimizing a classification risk with a symmetric loss. We propose a new imitation learning method that effectively combines pseudo-labeling with co-training. Experimental results on continuous-control benchmarks show that our method is more robust compared to state-of-the-art methods.
arXiv Detail & Related papers (2020-10-20T10:41:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.