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Found 8 papers, 0 papers with code
Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications
As AIGC has impacted our society profoundly in the past years, ethical issues have received tremendous attention.
Privacy-Preserving Sequential Recommendation with Collaborative Confusion
Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence.
Teach Large Language Models to Forget Privacy
Large Language Models (LLMs) have proven powerful, but the risk of privacy leakage remains a significant concern.
Learning to Prove Trigonometric Identities
We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities.
GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network
GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality.
Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization
In this paper, we propose a novel approach for minimax optimization, called Minimax Cubic Newton (MCN), which could find an $\big(\varepsilon,\kappa^{1. 5}\sqrt{\rho\varepsilon}\,\big)$-second-order stationary point of $P({\bf x})$ with calling ${\mathcal O}\big(\kappa^{1. 5}\sqrt{\rho}\varepsilon^{-1. 5}\big)$ times of second-order oracles and $\tilde{\mathcal O}\big(\kappa^{2}\sqrt{\rho}\varepsilon^{-1. 5}\big)$ times of first-order oracles, where $\kappa$ is the condition number and $\rho$ is the Lipschitz continuous constant for the Hessian of $f({\bf x},{\bf y})$.
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization
Moreover, comparisons between different initial designs with the same model show the advantage of the proposed optimal design.
Accelerated Value Iteration via Anderson Mixing
A2VI is more efficient than the modified policy iteration, which is a classical approximate method for policy evaluation.
