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09/12/2023
BatMan-CLR: Making Few-shots Meta-Learners Resilient Against Label Noise
The negative impact of label noise is well studied in classical supervis...
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02/03/2023
GTV: Generating Tabular Data via Vertical Federated Learning
Generative Adversarial Networks (GANs) have achieved state-of-the-art re...
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11/17/2022
Permutation-Invariant Tabular Data Synthesis
Tabular data synthesis is an emerging approach to circumvent strict regu...
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10/13/2022
Federated Learning for Tabular Data: Exploring Potential Risk to Privacy
Federated Learning (FL) has emerged as a potentially powerful privacy-pr...
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10/12/2022
FCT-GAN: Enhancing Table Synthesis via Fourier Transform
Synthetic tabular data emerges as an alternative for sharing knowledge w...
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10/12/2022
Aergia: Leveraging Heterogeneity in Federated Learning Systems
Federated Learning (FL) is a popular approach for distributed deep learn...
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04/28/2022
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Federated learning is a private-by-design distributed learning paradigm ...
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04/23/2022
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets
Federated learning allows clients to collaboratively train models on dat...
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04/01/2022
CTAB-GAN+: Enhancing Tabular Data Synthesis
While data sharing is crucial for knowledge development, privacy concern...
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02/07/2022
Blind leads Blind: A Zero-Knowledge Attack on Federated Learning
Attacks on Federated Learning (FL) can severely reduce the quality of th...
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01/31/2022
MEGA: Model Stealing via Collaborative Generator-Substitute Networks
Deep machine learning models are increasingly deployedin the wild for pr...
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01/24/2022
Attacks and Defenses for Free-Riders in Multi-Discriminator GAN
Generative Adversarial Networks (GANs) are increasingly adopted by the i...
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12/18/2021
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision
Deep neural networks (DNNs) have become ubiquitous techniques in mobile ...
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08/18/2021
Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data
Generative Adversarial Networks (GANs) are typically trained to synthesi...
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08/04/2021
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classif...
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06/20/2021
Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning
Shapley Value is commonly adopted to measure and incentivize client part...
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03/19/2021
Enhancing Robustness of On-line Learning Models on Highly Noisy Data
Classification algorithms have been widely adopted to detect anomalies f...
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02/16/2021
CTAB-GAN: Effective Table Data Synthesizing
While data sharing is crucial for knowledge development, privacy concern...
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12/07/2020
SGD_Tucker: A Novel Stochastic Optimization Strategy for Parallel Sparse Tucker Decomposition
Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a g...
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11/13/2020
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Labeling real-world datasets is time consuming but indispensable for sup...
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11/13/2020
An Exploratory Analysis on Users' Contributions in Federated Learning
Federated Learning is an emerging distributed collaborative learning par...
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10/27/2020
Active Learning for Noisy Data Streams Using Weak and Strong Labelers
Labeling data correctly is an expensive and challenging task in machine ...
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10/01/2020
PipeTune: Pipeline Parallelism of Hyper and System Parameters Tuning for Deep Learning Clusters
DNN learning jobs are common in today's clusters due to the advances in ...
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07/13/2020
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Robustness to label noise is a critical property for weakly-supervised c...
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07/10/2020
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Today's available datasets in the wild, e.g., from social media and open...
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01/28/2020
QActor: On-line Active Learning for Noisy Labeled Stream Data
Noisy labeled data is more a norm than a rarity for self-generated conte...
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11/11/2019
RAD: On-line Anomaly Detection for Highly Unreliable Data
Classification algorithms have been widely adopted to detect anomalies f...
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09/12/2019
