Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- posterMay 2024
A Two-Stage Calibration Approach for Mitigating Bias and Fairness in Recommender Systems
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied ComputingApril 2024, pp 1659–1661https://doi.org/10.1145/3605098.3636092Popularity bias and unfairness are problems caused by the lack of calibration in recommender systems. Works that intend to reduce the effect of popularity bias do not consider the distribution of item genres/categories in the users' profiles. Other ...
- research-articleMay 2024
Calibrating Graph Neural Networks from a Data-centric Perspective
WWW '24: Proceedings of the ACM on Web Conference 2024May 2024, pp 745–755https://doi.org/10.1145/3589334.3645562Graph neural networks (GNNs) have gained popularity in modeling various complex networks, e.g., social network and webpage network. Despite the promising accuracy, the confidences of GNNs are shown to be miscalibrated, indicating limited awareness of ...
A Meta-Bayesian Approach for Rapid Online Parametric Optimization for Wrist-based Interactions
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing SystemsMay 2024, Article No.: 410, pp 1–38https://doi.org/10.1145/3613904.3642071Wrist-based input often requires tuning parameter settings in correspondence to between-user and between-session differences, such as variations in hand anatomy, wearing position, posture, etc. Traditionally, users either work with predefined parameter ...
- research-articleMay 2024
Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors
ACM Transactions on Embedded Computing Systems (TECS), Volume 23, Issue 3Article No.: 51, pp 1–23https://doi.org/10.1145/3590956Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, ...
- ArticleMarch 2024
Calibration of Inverse Perspective Mapping for a Humanoid Robot
- Francisco Bruno Dias Ribeiro da Silva,
- Marcos Ricardo Omena de Albuquerque Máximo,
- Takashi Yoneyama,
- Davi Herculano Vasconcelos Barroso,
- Rodrigo Tanaka Aki
AbstractThis paper proposes a method to calibrate the model used for inverse perspective mapping of humanoid robots. It aims at providing a reliable way to determine the robot’s position given the known objects around it. The position of the objects can ...
-
- research-articleMarch 2024
T-Cal: an optimal test for the calibration of predictive models
The Journal of Machine Learning Research (JMLR), Volume 24, Issue 1Article No.: 335, pp 15959–16030The prediction accuracy of machine learning methods is steadily increasing, but the calibration of their uncertainty predictions poses a significant challenge. Numerous works focus on obtaining well-calibrated predictive models, but less is known about ...
- research-articleMarch 2024
A simple approach to improve single-model deep uncertainty via distance-awareness
- Jeremiah Zhe Liu,
- Shreyas Padhy,
- Jie Ren,
- Zi Lin,
- Yeming Wen,
- Ghassen Jerfel,
- Zachary Nado,
- Jasper Snoek,
- Dustin Tran,
- Balaji Lakshminarayanan
The Journal of Machine Learning Research (JMLR), Volume 24, Issue 1Article No.: 42, pp 1667–1729Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive ...
- research-articleNovember 2023
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective
ICAIF '23: Proceedings of the Fourth ACM International Conference on AI in FinanceNovember 2023, pp 253–260https://doi.org/10.1145/3604237.3626837One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options. Traditionally, one employs a mix of intuition, theoretical and empirical analysis to ...
- review-articleNovember 2023
On calibrated inverse probability weighting and generalized boosting propensity score models for mean estimation with incomplete survey data
WIREs Computational Statistics (WICS), Volume 15, Issue 6November/December 2023https://doi.org/10.1002/wics.1616AbstractIncomplete data, whether realized from nonresponse in survey data or counterfactual outcomes in observational studies, may lead to biased estimation of study variables. Nonresponse and selection bias may be mitigated with techniques that weight ...
This article reviews inverse probability weighting and entropy balancing calibration by distinguishing them in the statistical sense of variable balancing, extending propensity score construction to include generalized boosting models, and demonstrating ...
- research-articleOctober 2023
Fast and Optimal Beam Alignment for Off-the-Shelf mmWave Devices
MobiWac '23: Proceedings of the Int'l ACM Symposium on Mobility Management and Wireless AccessOctober 2023, pp 115–123https://doi.org/10.1145/3616390.3618292Overcoming the very high path loss is considered a prerequisite for operating millimeter-wave (mmWave) beamforming to meet the high data-rate requirements of next-generation wireless systems. Existing work chose the optimal beam pattern outside the ...
- research-articleOctober 2023
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error
MM '23: Proceedings of the 31st ACM International Conference on MultimediaOctober 2023, pp 1167–1178https://doi.org/10.1145/3581783.3611808The prevalence of domain adaptive semantic segmentation has prompted concerns regarding source domain data leakage, where private information from the source domain could inadvertently be exposed in the target domain. To circumvent the requirement for ...
- research-articleOctober 2023
Global Calibration of Virtual Multi-camera Vision System Based on 3D Target
ICDIP '23: Proceedings of the 15th International Conference on Digital Image ProcessingMay 2023, Article No.: 86, pp 1–7https://doi.org/10.1145/3604078.3604164Virtual multi-camera vision system (MVS) combining a single camera and catadioptric mirrors has been paid increasing attention for 3D measurement applications due to its wide field of view (FOV), low cost, and synchronization. Setups of the vision ...
- research-articleOctober 2023
Calibrated Passability Perception in Virtual Reality Transfers to Augmented Reality
ACM Transactions on Applied Perception (TAP), Volume 20, Issue 4Article No.: 14, pp 1–16https://doi.org/10.1145/3613450As applications for virtual reality (VR) and augmented reality (AR) technology increase, it will be important to understand how users perceive their action capabilities in virtual environments. Feedback about actions may help to calibrate perception for ...
- short-paperOctober 2023
Leveraging Post-Click User Behaviors for Calibrated Conversion Rate Prediction Under Delayed Feedback in Online Advertising
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementOctober 2023, pp 3918–3922https://doi.org/10.1145/3583780.3615161Obtaining accurately calibrated conversion rate predictions is essential for the bidding and ranking process in online advertising systems. Nevertheless, the inherent latency between clicks and conversions leads to delayed feedback, which may introduce ...
- research-articleOctober 2023
Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance
- Aijun Bai,
- Rolf Jagerman,
- Zhen Qin,
- Le Yan,
- Pratyush Kar,
- Bing-Rong Lin,
- Xuanhui Wang,
- Michael Bendersky,
- Marc Najork
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementOctober 2023, pp 4502–4508https://doi.org/10.1145/3583780.3614712As Learning-to-Rank (LTR) approaches primarily seek to improve ranking quality, their output scores are not scale-calibrated by design. This fundamentally limits LTR usage in score-sensitive applications. Though a simple multi-objective approach that ...
- ArticleOctober 2023
Boundary-Weighted Logit Consistency Improves Calibration of Segmentation Networks
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Oct 2023, pp 367–377https://doi.org/10.1007/978-3-031-43898-1_36AbstractNeural network prediction probabilities and accuracy are often only weakly-correlated. Inherent label ambiguity in training data for image segmentation aggravates such miscalibration. We show that logit consistency across stochastic ...
- ArticleOctober 2023
Maximum Entropy on Erroneous Predictions: Improving Model Calibration for Medical Image Segmentation
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023Oct 2023, pp 273–283https://doi.org/10.1007/978-3-031-43898-1_27AbstractModern deep neural networks achieved remarkable progress in medical image segmentation tasks. However, it has recently been observed that they tend to produce overconfident estimates, even in situations of high uncertainty, leading to poorly ...
- research-articleOctober 2023
Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement
Proceedings of the ACM on Human-Computer Interaction (PACMHCI), Volume 7, Issue CSCW2Article No.: 283, pp 1–26https://doi.org/10.1145/3610074When groups of people are tasked with making a judgment, the issue of uncertainty often arises. Existing methods to reduce uncertainty typically focus on iteratively improving specificity in the overall task instruction. However, uncertainty can arise ...
- research-articleAugust 2023
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
- Xiang-Rong Sheng,
- Jingyue Gao,
- Yueyao Cheng,
- Siran Yang,
- Shuguang Han,
- Hongbo Deng,
- Yuning Jiang,
- Jian Xu,
- Bo Zheng
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningAugust 2023, pp 4813–4822https://doi.org/10.1145/3580305.3599851Despite the development of ranking optimization techniques, pointwise loss remains the dominating approach for click-through rate prediction. It can be attributed to the calibration ability of the pointwise loss since the prediction can be viewed as the ...
- ArticleJuly 2023
Predicting ABM Results with Covering Arrays and Random Forests
AbstractSimulation is a useful and effective way to analyze and study complex, real-world systems. It allows researchers, practitioners, and decision makers to make sense of the inner working of a system that involves many factors often resulting in some ...