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09/20/2023
Is GPT4 a Good Trader?
Recently, large language models (LLMs), particularly GPT-4, have demonst...
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08/13/2023
SAILOR: Structural Augmentation Based Tail Node Representation Learning
Graph Neural Networks (GNNs) have achieved state-of-the-art performance ...
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08/12/2023
Semantic Equivariant Mixup
Mixup is a well-established data augmentation technique, which can exten...
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06/05/2023
Uncertainty in Natural Language Processing: Sources, Quantification, and Applications
As a main field of artificial intelligence, natural language processing ...
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06/02/2023
Calibrating Multimodal Learning
Multimodal machine learning has achieved remarkable progress in a wide r...
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05/29/2023
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Most named entity recognition (NER) systems focus on improving model per...
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05/26/2023
Attention Paper: How Generative AI Reshapes Digital Shadow Industry?
The rapid development of digital economy has led to the emergence of var...
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04/09/2023
Reweighted Mixup for Subpopulation Shift
Subpopulation shift exists widely in many real-world applications, which...
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04/04/2023
SLPerf: a Unified Framework for Benchmarking Split Learning
Data privacy concerns has made centralized training of data, which is sc...
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04/03/2023
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Large-scale language models (LLMs) have demonstrated outstanding perform...
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03/23/2023
Fairness-guided Few-shot Prompting for Large Language Models
Large language models have demonstrated surprising ability to perform in...
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03/23/2023
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing dee...
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02/23/2023
Federated Nearest Neighbor Machine Translation
To protect user privacy and meet legal regulations, federated learning (...
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11/28/2022
Post-training Quantization on Diffusion Models
Denoising diffusion (score-based) generative models have recently achiev...
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11/16/2022
Learning with Noisy Labels over Imbalanced Subpopulations
Learning with Noisy Labels (LNL) has attracted significant attention fro...
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10/20/2022
Vertical Federated Linear Contextual Bandits
In this paper, we investigate a novel problem of building contextual ban...
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09/19/2022
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Subpopulation shift wildly exists in many real-world machine learning ap...
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09/16/2022
ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
The last decade has witnessed a prosperous development of computational ...
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05/20/2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business ...
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04/16/2022
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup
Recently, federated learning has emerged as a promising approach for tra...
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02/15/2022
Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift
Deep graph learning (DGL) has achieved remarkable progress in both busin...
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01/24/2022
DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery – A Focus on Affinity Prediction Problems with Noise Annotations
AI-aided drug discovery (AIDD) is gaining increasing popularity due to i...
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12/14/2021
Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis
Recently, generalization bounds of the non-convex empirical risk minimiz...
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05/04/2021
HASCO: Towards Agile HArdware and Software CO-design for Tensor Computation
Tensor computations overwhelm traditional general-purpose computing devi...
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12/17/2020
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
Deep Neural Networks (DNNs) have achieved remarkable progress in various...
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11/06/2020
ASFGNN: Automated Separated-Federated Graph Neural Network
Graph Neural Networks (GNNs) have achieved remarkable performance by tak...
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09/19/2020
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
Dynamic inference is a feasible way to reduce the computational cost of ...
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09/04/2020
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
Transfer learning is widely used for transferring knowledge from a sourc...
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05/25/2020
Privacy-Preserving Graph Neural Network for Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses ...
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03/05/2020
Practical Privacy Preserving POI Recommendation
Point-of-Interest (POI) recommendation has been extensively studied and ...
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02/06/2020
Secure Social Recommendation based on Secret Sharing
Nowadays, privacy preserving machine learning has been drawing much atte...
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11/16/2019
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Recently, dynamic inference has emerged as a promising way to reduce the...
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10/05/2019
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Bayesian deep learning is recently regarded as an intrinsic way to chara...
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08/21/2019
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
In this paper, we aim to understand the generalization properties of gen...
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06/03/2019
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Recently, deep learning as a service (DLaaS) has emerged as a promising ...
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05/30/2019
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
Recently, deep convolutional neural networks (CNNs) have achieved great ...
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06/30/2018
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Pathological glomerulus classification plays a key role in the diagnosis...
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12/16/2017
