Search Results for author: Yining Chen

Found 18 papers, 4 papers with code

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.

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

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

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.

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 Question Answering

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

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

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 online learning

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 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.

Domain Adaptation

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

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.

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.

Pretrained Language Models Text Summarization

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

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.

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