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Found 28 papers, 4 papers with code
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
In this paper, we aim to develop a scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples.
Can Large Language Models Understand Intermediate Representations?
Intermediate Representations (IRs) are essential in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored.
Leveraging Large Language Models to Analyze Emotional and Contextual Drivers of Teen Substance Use in Online Discussions
This study applies Large Language Models (LLMs) to analyze adolescents' social media posts, uncovering emotional patterns (e. g., sadness, guilt, fear, joy) and contextual factors (e. g., family, peers, school) related to substance use.
Investigating Large Language Models in Inferring Personality Traits from User Conversations
Large Language Models (LLMs) are demonstrating remarkable human like capabilities across diverse domains, including psychological assessment.
Scalable Deep Metric Learning on Attributed Graphs
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks.
Federated Contrastive Learning of Graph-Level Representations
This points to the need for federated learning for graph-level representations, a topic that has not been explored much, especially in an unsupervised setting.
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models
First, we propose a fisher information-based method to adaptively sample data within each device to improve the effectiveness of the FL fine-tuning process.
Demo: SGCode: A Flexible Prompt-Optimizing System for Secure Generation of Code
This paper introduces SGCode, a flexible prompt-optimizing system to generate secure code with large language models (LLMs).
AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices
First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence.
Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)
Inspired by the success of contrastive learning, we systematically examine recommendation losses, including listwise (softmax), pairwise (BPR), and pointwise (MSE and CCL) losses.
(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)
In this paper, we perform a systemic examination of the recommendation losses, including listwise (softmax), pairwise(BPR), and pointwise (mean-squared error, MSE, and Cosine Contrastive Loss, CCL) losses through the lens of contrastive learning.
Zone-based Federated Learning for Mobile Sensing Data
However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users increases, and protects user data privacy.
Generation-Augmented Query Expansion For Code Retrieval
Pre-trained language models have achieved promising success in code retrieval tasks, where a natural language documentation query is given to find the most relevant existing code snippet.
Towards Reliable Item Sampling for Recommendation Evaluation
Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation.
Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning
In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss.
Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
Though other methods (particularly those based on Laplacian Smoothing) have reported better accuracy, a fundamental limitation of all the work is a lack of scalability.
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory
Despite achieving remarkable efficiency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks.
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps.
On the regularization landscape for the linear recommendation models
Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks.
On Sampling Top-K Recommendation Evaluation
Recently, Rendle has warned that the use of sampling-based top-$k$ metrics might not suffice.
Towards a Better Understanding of Linear Models for Recommendation
Through the derivation and analysis of the closed-form solutions for two basic regression and matrix factorization approaches, we found these two approaches are indeed inherently related but also diverge in how they "scale-down" the singular values of the original user-item interaction matrix.
On Estimating Recommendation Evaluation Metrics under Sampling
The proposed approaches either are rather uninformative (linking sampling to metric evaluation) or can only work on simple metrics, such as Recall/Precision (Krichene and Rendle 2020; Li et al. 2020).
Adversarial Attacks on Deep Graph Matching
Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.
Differential Privacy in Adversarial Learning with Provable Robustness
In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples.
Preserving Differential Privacy in Adversarial Learning with Provable Robustness
In this paper, we aim to develop a novel mechanism to preserve differential privacy (DP) in adversarial learning for deep neural networks, with provable robustness to adversarial examples.
Cryptography and Security
A Deep Embedding Model for Co-occurrence Learning
Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items.
Axiomatic Ranking of Network Role Similarity
For the first problem, we justify several axiomatic properties necessary for a role similarity measure or metric: range, maximal similarity, automorphic equivalence, transitive similarity, and the triangle inequality.
Social and Information Networks Physics and Society H.2.8
