Search Results for author:
Found 67 papers, 32 papers with code
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process.
On the Generalization Ability of Unsupervised Pretraining
Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization.
Cross-modality debiasing: using language to mitigate sub-population shifts in imaging
Specifically, in the context of the distributional robustness of CLIP, we propose to leverage natural language inputs to debias the image feature representations, to improve worst-case performance on sub-populations.
Stochastic Two Points Method for Deep Model Zeroth-order Optimization
The theoretical properties also shed light on a faster and more stable S2P variant, Accelerated S2P (AS2P), through exploiting our new convergence properties that better represent the dynamics of deep models in training.
Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning
This study assesses the ability of state-of-the-art large language models (LLMs) including GPT-3. 5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment (MCI) from discharge summaries and examines instances where the models' responses were misaligned with their reasoning.
User-Aware Prefix-Tuning is a Good Learner for Personalized Image Captioning
Moreover, we employ GPT-2 as the frozen large language model.
Understanding Deep Gradient Leakage via Inversion Influence Functions
Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors.
Safe and Robust Watermark Injection with a Single OoD Image
Training a high-performance deep neural network requires large amounts of data and computational resources.
FedNoisy: Federated Noisy Label Learning Benchmark
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients.
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model.
Backdoor Defense for Data-Free Distillation with Poisoned Teachers
Data-free Knowledge Distillation
Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks
Direct sharing of the data distribution may be prohibitive due to the additional private information that is sent from the clients.
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models.
Discovering Predictable Latent Factors for Time Series Forecasting
Many real-world data mining tasks, however, lack sufficient variables for relation reasoning, and therefore these methods may not properly handle such forecasting problems.
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization
We propose to extract medical facts of the input medical report, its gold summary, and candidate summaries based on the RadGraph schema and design the fact-guided reranker to efficiently incorporate the extracted medical facts for selecting the optimal summary.
FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks
One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates.
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection
Developing a mechanism for battling financial crimes is an impending task that requires in-depth collaboration from multiple institutions, and yet such collaboration imposed significant technical challenges due to the privacy and security requirements of distributed financial data.
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge.
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL.
MECTA: Memory-Economic Continual Test-Time Model Adaptation
The proposed MECTA is efficient and can be seamlessly plugged into state-of-theart CTA algorithms at negligible overhead on computation and memory.
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training at a low-power and cost-effective end device.
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
As a result, both the stem and the classification head in the final network are hardly affected by backdoor training samples.
RUSH: Robust Contrastive Learning via Randomized Smoothing
In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing.
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts
Increasing concerns have been raised on deep learning fairness in recent years.
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness.
Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets
In this work, we proposed a DDI detection method based on molecular structures using graph convolutional networks and deep sets.
Persona Authentication through Generative Dialogue
In this paper we define and investigate the problem of \emph{persona authentication}: learning a conversational policy to verify the consistency of persona models.
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm
We first discuss the limitations of directly using the noisy-label defense algorithms to defend against backdoor attacks.
Equalized Robustness: Towards Sustainable Fairness Under Distributional Shifts
In this paper, we first propose a new fairness goal, termed Equalized Robustness (ER), to impose fair model robustness against unseen distribution shifts across majority and minority groups.
Federated Adversarial Debiasing for Fair and Transferable Representations
While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework.
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning
In this paper, we study a novel FL strategy: propagating adversarial robustness from rich-resource users that can afford AT, to those with poor resources that cannot afford it, during federated learning.
MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph
Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks.
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data.
MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors
Despite the popularity of this single-task schema, it may neglect interactions among calibration tasks of different sensors, which encompass underlying information to promote calibration performance.
FedFace: Collaborative Learning of Face Recognition Model
DNN-based face recognition models require large centrally aggregated face datasets for training.
Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation
To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning.
Off-Policy Imitation Learning from Observations
To further accelerate the learning procedure, we regulate the policy update with an inverse action model, which assists distribution matching from the perspective of mode-covering.
Learning Deep Neural Networks under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance.
Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent
In this paper, we provide comprehensive analysis of noise influence in dynamic privacy schedules to answer these critical questions.
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders
Unsupervised anomaly detection plays a crucial role in many critical applications.
Provable Robust Learning under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervisions is challenging as the corrupted data points may significantly impact the generalization performance.
On Dynamic Noise Influence in Differential Private Learning
In this paper, we provide comprehensive analysis of noise influence in dynamic privacy schedules to answer these critical questions.
Robust Collaborative Learning with Noisy Labels
In this paper, we study the underlying mechanism of how disagreement and agreement between networks can help reduce the noise in gradients and develop a novel framework called Robust Collaborative Learning (RCL) that leverages both disagreement and agreement among networks.
Transfer Learning in Deep Reinforcement Learning: A Survey
Reinforcement learning is a learning paradigm for solving sequential decision-making problems.
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg
For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings.
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
SAIL bridges the advantages of IL and RL to reduce the sample complexity substantially, by effectively exploiting sup-optimal demonstrations and efficiently exploring the environment to surpass the demonstrated performance.
Ranking Policy Gradient
Sample inefficiency is a long-lasting problem in reinforcement learning (RL).
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records
In this paper, we propose MetaPred, a meta-learning for clinical risk prediction from longitudinal patient EHRs.
Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution
In this paper, we aim to infer the significance of every item's appearance in consumer decision making and identify the group of items that are suitable for screenless shopping.
Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation
The user personalized non-collaborative methods based on item features can be used to address this item cold-start problem.
Boosted Sparse and Low-Rank Tensor Regression
We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.
Model-Protected Multi-Task Learning
Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together.
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans.
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features.
Distributed Data Vending on Blockchain
However, one key challenge in distributed data vending is the trade-off dilemma between the effectiveness of data retrieval, and the leakage risk from indexing the data.
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients.
Differentially Private Generative Adversarial Network
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models.
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet Management
Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency.
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation
Even though there is mild cognitive decline in MCI patients, they have normal overall cognition and thus is challenging to distinguish from normal aging.
Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models
The surging availability of electronic medical records (EHR) leads to increased research interests in medical predictive modeling.
Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN
Generating high fidelity identity-preserving faces with different facial attributes has a wide range of applications.
Patient Subtyping via Time-Aware LSTM Networks
We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes.
Ranked #4 on
Multivariate Time Series Forecasting
on USHCN-Daily
Missing Modalities Imputation via Cascaded Residual Autoencoder
To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities.
Collaborative Deep Reinforcement Learning
Motivated by human collaborative learning, in this paper we propose a collaborative deep reinforcement learning (CDRL) framework that performs adaptive knowledge transfer among heterogeneous learning agents.
Asynchronous Multi-Task Learning
The models for each hospital may be different because of the inherent differences in the distributions of the patient populations.
Learning A Task-Specific Deep Architecture For Clustering
In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both.
A Safe Screening Rule for Sparse Logistic Regression
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection.
Clustered Multi-Task Learning Via Alternating Structure Optimization
We further establish the equivalence relationship between the proposed convex relaxation of CMTL and an existing convex relaxation of ASO, and show that the proposed convex CMTL formulation is significantly more efficient especially for high-dimensional data.
