Search Results for author: Zheng Yu

Found 19 papers, 5 papers with code

How to Inverting the Leverage Score Distribution?

no code implementations21 Apr 2024 Zhihang Li, Zhao Song, Weixin Wang, Junze Yin, Zheng Yu

Leverage score is a fundamental problem in machine learning and theoretical computer science.

VL-Mamba: Exploring State Space Models for Multimodal Learning

no code implementations20 Mar 2024 Yanyuan Qiao, Zheng Yu, Longteng Guo, Sihan Chen, Zijia Zhao, Mingzhen Sun, Qi Wu, Jing Liu

The extensive experiments on diverse multimodal benchmarks with competitive performance show the effectiveness of our proposed VL-Mamba and demonstrate the great potential of applying state space models for multimodal learning tasks.

Language Modelling Large Language Model +1

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts

1 code implementation19 Sep 2023 Jiahao Yu, Xingwei Lin, Zheng Yu, Xinyu Xing

Remarkably, GPTFuzz achieves over 90% attack success rates against ChatGPT and Llama-2 models, even with suboptimal initial seed templates.

VLN-PETL: Parameter-Efficient Transfer Learning for Vision-and-Language Navigation

1 code implementation ICCV 2023 Yanyuan Qiao, Zheng Yu, Qi Wu

The performance of the Vision-and-Language Navigation~(VLN) tasks has witnessed rapid progress recently thanks to the use of large pre-trained vision-and-language models.

Transfer Learning Vision and Language Navigation +1

March in Chat: Interactive Prompting for Remote Embodied Referring Expression

1 code implementation ICCV 2023 Yanyuan Qiao, Yuankai Qi, Zheng Yu, Jing Liu, Qi Wu

Nevertheless, this poses more challenges than other VLN tasks since it requires agents to infer a navigation plan only based on a short instruction.

Referring Expression Vision and Language Navigation

Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

1 code implementation20 Feb 2023 Zheng Yu, Yikuan Li, Joseph Kim, Kaixuan Huang, Yuan Luo, Mengdi Wang

In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost.

Anomaly Detection Medical Diagnosis +3

Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability

no code implementations15 Oct 2022 Zhao Song, Yitan Wang, Zheng Yu, Lichen Zhang

In this paper, we propose a novel sketching scheme for the first order method in large-scale distributed learning setting, such that the communication costs between distributed agents are saved while the convergence of the algorithms is still guaranteed.

Federated Learning

Fast Graph Neural Tangent Kernel via Kronecker Sketching

no code implementations4 Dec 2021 Shunhua Jiang, Yunze Man, Zhao Song, Zheng Yu, Danyang Zhuo

Given a kernel matrix of $n$ graphs, using sketching in solving kernel regression can reduce the running time to $o(n^3)$.


Iterative Sketching and its Application to Federated Learning

no code implementations29 Sep 2021 Zhao Song, Zheng Yu, Lichen Zhang

Though most federated learning frameworks only require clients and the server to send gradient information over the network, they still face the challenges of communication efficiency and data privacy.

Federated Learning LEMMA

Fast Sketching of Polynomial Kernels of Polynomial Degree

no code implementations21 Aug 2021 Zhao Song, David P. Woodruff, Zheng Yu, Lichen Zhang

Recent techniques in oblivious sketching reduce the dependence in the running time on the degree $q$ of the polynomial kernel from exponential to polynomial, which is useful for the Gaussian kernel, for which $q$ can be chosen to be polylogarithmic.

BIG-bench Machine Learning

On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method

no code implementations NeurIPS 2021 Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvari, Mengdi Wang

By assuming the overparameterizaiton of policy and exploiting the hidden convexity of the problem, we further show that TSIVR-PG converges to global $\epsilon$-optimal policy with $\tilde{\mathcal{O}}(\epsilon^{-2})$ samples.

Reinforcement Learning (RL)

Oblivious Sketching-based Central Path Method for Solving Linear Programming Problems

no code implementations1 Jan 2021 Zhao Song, Zheng Yu

In this work, we propose a sketching-based central path method for solving linear programmings, whose running time matches the state of art results [Cohen, Lee, Song STOC 19; Lee, Song, Zhang COLT 19].

Generalized Leverage Score Sampling for Neural Networks

no code implementations NeurIPS 2020 Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu

Leverage score sampling is a powerful technique that originates from theoretical computer science, which can be used to speed up a large number of fundamental questions, e. g. linear regression, linear programming, semi-definite programming, cutting plane method, graph sparsification, maximum matching and max-flow.

Learning Theory regression

Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling

no code implementations24 Feb 2020 Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, Scott A. Sisson

In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.

Link Prediction

Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel

no code implementations17 Jan 2020 Zheng Yu, Xuhui Fan, Marcin Pietrasik, Marek Reformat

Besides, the proposed model infers the network structure and models community evolution, manifested by appearances and disappearances of communities, using the discrete fragmentation coagulation process (DFCP).


Scalable Influence Maximization with General Marketing Strategies

no code implementations13 Feb 2018 Ruihan Wu, Zheng Yu, Wei Chen

In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector $\mathbf{x}=(x_1, \ldots, x_d)$ and could activate a node $v$ in the social network with probability $h_v(\mathbf{x})$.

Social and Information Networks Data Structures and Algorithms

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