1 code implementation • 29 Mar 2024 • Peng Ding, Jiading Fang, Peng Li, Kangrui Wang, Xiaochen Zhou, Mo Yu, Jing Li, Matthew R. Walter, Hongyuan Mei
The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?"
1 code implementation • 14 Nov 2023 • Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, ShuJian Huang
Finally, we analyze the failure of LLMs defense from the perspective of prompt execution priority, and propose corresponding defense strategies.
1 code implementation • 27 Sep 2023 • Dennis Shen, Dogyoon Song, Peng Ding, Jasjeet S. Sekhon
Deep learning research has uncovered the phenomenon of benign overfitting for over-parameterized statistical models, which has drawn significant theoretical interest in recent years.
no code implementations • 14 Sep 2023 • Mengsi Gao, Peng Ding
Although we focus on regression-based point estimators and standard errors, our theory holds under the design-based framework, which assumes that the randomness comes solely from the design of network experiments and allows for arbitrary misspecification of the regression models.
1 code implementation • 13 Jul 2023 • Zhan Shi, Xin Ding, Peng Ding, Chun Yang, Ru Huang, Xiaoxuan Song
Four tiny SOAP models are also created by replacing the convolutional blocks in Mobile-SOAP with four small-scale networks, respectively.
no code implementations • 16 Jan 2023 • Wenlong Mou, Peng Ding, Martin J. Wainwright, Peter L. Bartlett
When it is violated, the classical semi-parametric efficiency bound can easily become infinite, so that the instance-optimal risk depends on the function class used to model the regression function.
1 code implementation • 29 Jul 2022 • Dennis Shen, Peng Ding, Jasjeet Sekhon, Bin Yu
A central goal in social science is to evaluate the causal effect of a policy.
no code implementations • 30 Mar 2021 • Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, Michael I. Jordan
Across simulations and two case studies with real data, we show that this control variate can significantly reduce the variance of the ATE estimate.
no code implementations • ICCV 2019 • Xiaofeng Liu, Yang Zou, Tong Che, Peng Ding, Ping Jia, Jane You, Kumar B. V. K
We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.
no code implementations • 26 Jun 2019 • Xinran Li, Peng Ding
R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance (ANCOVA) in the analysis stage.
Statistics Theory Methodology Statistics Theory
no code implementations • SEMEVAL 2018 • Peng Ding, Xiaobing Zhou
This paper describes the system submitted to SemEval-2018 Task 12 (The Argument Reasoning Comprehension Task).
no code implementations • SEMEVAL 2018 • Peng Ding, Xiaobing Zhou
We firstly use GloVe to learn the distributed representations automatically from the instance, question and answer triples.
no code implementations • IJCNLP 2017 • Min Wang, Qingxun Liu, Peng Ding, Yongbin Li, Xiaobing Zhou
In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer.