Search Results for author: Jiarui Lu

Found 7 papers, 3 papers with code

A Text-guided Protein Design Framework

no code implementations9 Feb 2023 Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, Anima Anandkumar

Current AI-assisted protein design mainly utilizes protein sequential and structural information.

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

1 code implementation5 Jun 2022 Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang

However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.

Feature Engineering Multi-Task Learning +2

TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery

1 code implementation16 Feb 2022 Zhaocheng Zhu, Chence Shi, Zuobai Zhang, Shengchao Liu, Minghao Xu, Xinyu Yuan, Yangtian Zhang, Junkun Chen, Huiyu Cai, Jiarui Lu, Chang Ma, Runcheng Liu, Louis-Pascal Xhonneux, Meng Qu, Jian Tang

However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.

BIG-bench Machine Learning Drug Discovery +1

CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues

1 code implementation NAACL 2021 Bo-Hsiang Tseng, Shruti Bhargava, Jiarui Lu, Joel Ruben Antony Moniz, Dhivya Piraviperumal, Lin Li, Hong Yu

In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.

coreference-resolution Coreference Resolution +1

High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework

no code implementations26 Nov 2019 Abhishek Chakrabortty, Jiarui Lu, T. Tony Cai, Hongzhe Li

Under mild tail assumptions and arbitrarily chosen (working) models for the propensity score (PS) and the outcome regression (OR) estimators, satisfying only some high-level conditions, we establish finite sample performance bounds for the DDR estimator showing its (optimal) $L_2$ error rate to be $\sqrt{s (\log d)/ n}$ when both models are correct, and its consistency and DR properties when only one of them is correct.

Causal Inference regression

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