Search Results for author: Yining Chen

Found 21 papers, 7 papers with code

Recurrent Neural Networks as Weighted Language Recognizers

no code implementations NAACL 2018 Yining Chen, Sorcha Gilroy, Andreas Maletti, Jonathan May, Kevin Knight

We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages.

Weakly Supervised Disentanglement with Guarantees

1 code implementation ICLR 2020 Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole

Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning.

Disentanglement

BERT-AL: BERT for Arbitrarily Long Document Understanding

no code implementations ICLR 2020 Ruixuan Zhang, Zhuoyu Wei, Yu Shi, Yining Chen

When we apply BERT to long text tasks, e. g., document-level text summarization: 1) Truncating inputs by the maximum sequence length will decrease performance, since the model cannot capture long dependency and global information ranging the whole document.

document understanding Text Summarization

A random forest based approach for predicting spreads in the primary catastrophe bond market

no code implementations28 Jan 2020 Despoina Makariou, Pauline Barrieu, Yining Chen

The random forest shows an impressive predictive power on unseen primary catastrophe bond data explaining 93% of the total variability.

Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning

no code implementations19 May 2020 Zhenhui Ye, Yining Chen, Guanghua Song, Bowei Yang, Shen Fan

We demonstrate our approach by combining it with MADDPG and verifing the performance in two homogeneous and one heterogeneous environments.

Multi-agent Reinforcement Learning reinforcement-learning +1

Active Online Domain Adaptation

no code implementations ICML Workshop LifelongML 2020 Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang

We propose a surprisingly simple algorithm that adaptively balances its regret and its number of label queries in settings where the data streams are from a mixture of hidden domains.

Online Domain Adaptation regression

Self-training Avoids Using Spurious Features Under Domain Shift

no code implementations NeurIPS 2020 Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma

In unsupervised domain adaptation, existing theory focuses on situations where the source and target domains are close.

Unsupervised Domain Adaptation

Active Online Learning with Hidden Shifting Domains

no code implementations25 Jun 2020 Yining Chen, Haipeng Luo, Tengyu Ma, Chicheng Zhang

We propose a surprisingly simple algorithm that adaptively balances its regret and its number of label queries in settings where the data streams are from a mixture of hidden domains.

Domain Adaptation regression

Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

1 code implementation ICLR 2021 Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma

Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed.

Image Classification

Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

no code implementations ICLR 2021 Colin Wei, Kendrick Shen, Yining Chen, Tengyu Ma

Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks.

Generalization Bounds Unsupervised Domain Adaptation

ChemistryQA: A Complex Question Answering Dataset from Chemistry

no code implementations1 Jan 2021 Zhuoyu Wei, Wei Ji, Xiubo Geng, Yining Chen, Baihua Chen, Tao Qin, Daxin Jiang

We notice that some real-world QA tasks are more complex, which cannot be solved by end-to-end neural networks or translated to any kind of formal representations.

Machine Reading Comprehension Math +1

Maria: A Visual Experience Powered Conversational Agent

1 code implementation ACL 2021 Zujie Liang, Huang Hu, Can Xu, Chongyang Tao, Xiubo Geng, Yining Chen, Fan Liang, Daxin Jiang

The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image.

Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments

no code implementations18 Jun 2021 Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski

Domain generalization aims at performing well on unseen test environments with data from a limited number of training environments.

Domain Generalization

Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers

no code implementations28 Jul 2021 Colin Wei, Yining Chen, Tengyu Ma

A common lens to theoretically study neural net architectures is to analyze the functions they can approximate.

Generalization Bounds

Statistically Meaningful Approximation: a Theoretical Analysis for Approximating Turing Machines with Transformers

no code implementations29 Sep 2021 Colin Wei, Yining Chen, Tengyu Ma

A common lens to theoretically study neural net architectures is to analyze the functions they can approximate.

Zero-shot causal learning

1 code implementation NeurIPS 2023 Hamed Nilforoshan, Michael Moor, Yusuf Roohani, Yining Chen, Anja Šurina, Michihiro Yasunaga, Sara Oblak, Jure Leskovec

There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it.

Marketing Meta-Learning

Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

no code implementations14 Dec 2023 Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Gao, Leopold Aschenbrenner, Yining Chen, Adrien Ecoffet, Manas Joglekar, Jan Leike, Ilya Sutskever, Jeff Wu

Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outputs.

Analytical Reasoning of Text

1 code implementation Findings (NAACL) 2022 Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.

Cannot find the paper you are looking for? You can Submit a new open access paper.