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.
Bayes-consistent disagreement discrepancy loss improves model robustness.
This paper characterizes and explains the disagreement between two graph embedding methods.
The study reveals a linear relationship between source and target domain classification errors based on disagreement.
Disagreement is an essential element of science and life in general. The language of probabilities and statistics is often used to describe disagreements quantitatively. In practice, however, we want much more than that. We want disagreements to be resolved. This leaves us with a substantial knowledge gap which is ofte…
Traditional multi-view learning approaches suffer in the presence of view disagreement,i.e., when samples in each view do not belong to the same class due to view corruption, occlusion or other noise processes. In this paper we present a multi-view learning approach that uses a conditional entropy criterion to detect v…
Disagreement-based approaches generate multiple classifiers and exploit the disagreement among them with unlabeled data to improve learning performance. Co-training is a representative paradigm of them, which trains two classifiers separately on two sufficient and redundant views; while for the applications where there…
This study finds ESG rating disagreement reduces corporate productivity, especially in certain types of firms.
Improves reliability of medical diagnosis uncertainty estimates.
Study investor sentiment and disagreement on StockTwits during COVID-19.
Mitigates spurious correlations without bias labels.
SGD-trained models' disagreement predicts test error.
Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural ne…
This paper improves active learning by using robust divergences for committee disagreement.
New algorithm improves active learning in agnostic pool-based classification.
Efficient exploration is a long-standing problem in sensorimotor learning. Major advances have been demonstrated in noise-free, non-stochastic domains such as video games and simulation. However, most of these formulations either get stuck in environments with stochastic dynamics or are too inefficient to be scalable t…
Structured credal learning separates covariate shift and label disagreement.
Our work investigates disagreement in neural network feature attribution methods.
The paper analyzes how multiple classifiers' disagreement and polarization affect overall accuracy.
In correlation clustering, we are given objects together with a binary similarity score between each pair of them. The goal is to partition the objects into clusters so to minimise the disagreements with the scores. In this work we investigate correlation clustering as an active learning problem: each similarity sc…
The issue of disagreements amongst human experts is a ubiquitous one in both machine learning and medicine. In medicine, this often corresponds to doctor disagreements on a patient diagnosis. In this work, we show that machine learning models can be trained to give uncertainty scores to data instances that might result…
We provide new results for noise-tolerant and sample-efficient learning algorithms under -concave distributions. The new class of -concave distributions is a broad and natural generalization of log-concavity, and includes many important additional distributions, e.g., the Pareto distribution and -distribution.…
GRANITE unifies feature-based explanation methods to reduce disagreement.
Study compares and contrasts various ML explanation methods, highlighting their disagreements and similarities.
EXAGREE selects a stakeholder-aligned model to reduce conflicting explanations in machine learning.
Paper tackles ESG rating disagreement in sustainable investing portfolios.
AutoDIME automates design of multi-agent environments for RL.
New method gives provable error bounds for neural nets under distribution shift.
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations. In contrast to Learning from Demonstration (LfD) that involves both action and state supervision, LfO is more practical in leveraging previously inapplicable resources (e.g. videos), yet more challenging…
New method reduces version space for CNNs, improving active learning performance.
Ensembling improves performance when classifiers disagree more than average.
New method pools labels from similar data items to improve learning from small samples.
CreDRO learns credal ensembles via distributionally robust optimization, improving EU quantification.
The paper explores how Shapley value for a feature can vary based on model outcomes and feature distribution.
We introduce a new and improved characterization of the label complexity of disagreement-based active learning, in which the leading quantity is the version space compression set size. This quantity is defined as the size of the smallest subset of the training data that induces the same version space. We show various a…
We show that the disagreement coefficient of certain smooth hypothesis classes is , where is the dimension of the hypothesis space, thereby answering a question posed in \cite{friedman09}.
We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, …
This paper relaxes the common prior assumption in the public and private information game of Morris and Shin (2000, 2004). For the generalized game, where the agent's prior expectations are heterogenous, it derives a sharp condition for the emergence of unique/multiple equilibria. This condition indicates that unique e…
In this paper we study the problem of correlation clustering under fairness constraints. In the classic correlation clustering problem, we are given a complete graph where each edge is labeled positive or negative. The goal is to obtain a clustering of the vertices that minimizes disagreements -- the number of negative…
New method boosts skill learning by encouraging optimistic exploration.
We tackle the PAC-Bayesian Domain Adaptation (DA) problem. This arrives when one desires to learn, from a source distribution, a good weighted majority vote (over a set of classifiers) on a different target distribution. In this context, the disagreement between classifiers is known crucial to control. In non-DA superv…
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…
Three training methods for language models are shown to be variations of one another.
We calculate the Riemann curvature tensor and sectional curvature for the Lie group of volume-preserving diffeomorphisms of the Klein bottle and projective plane. In particular, we investigate the sign of the sectional curvature, and find a possible disagreement with a theorem of Lukatskii. We suggest an amendment to t…
This paper improves ASR performance by aligning frames more accurately.
We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), …
We study online active learning for classifying streaming instances within the framework of statistical learning theory. At each time, the learner either queries the label of the current instance or predicts the label based on past seen examples. The objective is to minimize the number of queries while constraining the…
JoCoR improves deep learning with noisy labels by reducing network diversity.
Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features si…