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Found 13 papers, 3 papers with code
Robust Decision Aggregation with Adversarial Experts
The decision maker does not know the specific information structure, which is a joint distribution of signals, states, and strategies of adversarial experts.
Algorithmic Robust Forecast Aggregation
Given a family of information structures, robust forecast aggregation aims to find the aggregator with minimal worst-case regret compared to the omniscient aggregator.
Robust Decision Aggregation with Second-order Information
When the aggregator is deterministic, we present a robust aggregator that leverages second-order information, which can significantly outperform counterparts without it.
Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms
A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms.
Information Elicitation Meets Clustering
Here in the setting where a large number of people are asked to answer a small number of multi-choice questions (multi-task, large group), we propose an information aggregation method that is robust to people's strategies.
Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels
We refer to such tasks as survey settings because the ground truth is defined through a survey of one or more human raters.
Modularity and Mutual Information in Networks: Two Sides of the Same Coin
At a high level, we show the significance of community structure is equivalent to the amount of information contained in the network.
Community Detection
Social and Information Networks
Equal Affection or Random Selection: the Quality of Subjective Feedback from a Group Perspective
With this assumption, we propose a new definition for uninformative feedback and correspondingly design a family of evaluation metrics, called f-variety, for group-level feedback which can 1) distinguish informative feedback and uninformative feedback (separation) even if their statistics are both uniform and 2) decrease as the ratio of uninformative respondents increases (monotonicity).
Computer Science and Game Theory
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
In this paper, we propose a novel information-theoretic approach, namely \textbf{T}otal \textbf{C}orrelation \textbf{G}ain \textbf{M}aximization (TCGM), for semi-supervised multi-modal learning, which is endowed with promising properties: (i) it can utilize effectively the information across different modalities of unlabeled data points to facilitate training classifiers of each modality (ii) it has theoretical guarantee to identify Bayesian classifiers, i. e., the ground truth posteriors of all modalities.
L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise
To the best of our knowledge, L_DMI is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information.
Ranked #36 on
Image Classification
on Clothing1M
(using extra training data)
L_DMI: An Information-theoretic Noise-robust Loss Function
\emph{To the best of our knowledge, $\mathcal{L}_{DMI}$ is the first loss function that is provably robust to instance-independent label noise, regardless of noise pattern, and it can be applied to any existing classification neural networks straightforwardly without any auxiliary information}.
Ranked #36 on
Image Classification
on Clothing1M
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Furthermore, we devise an accurate data-crowds forecaster that employs both the data and the crowdsourced labels to forecast the ground truth.
Water from Two Rocks: Maximizing the Mutual Information
In co-training/multiview learning, the goal is to aggregate two views of data into a prediction for a latent label.
