Yu Bai

North University of China

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Co-Authors

Yang Gao

Beijing Institute of Technology

Jiaao Zhan

Beijing Institute of Technology

Boxing Chen

Huawei Technologies (Canada)

Chien-Sheng Wu

Salesforce (United States)

Long He

Tsinghua University

Yang Gao

Nanjing University

Furong Wang

State Key Laboratory of Vehicle NVH and Safety Technology

Caiming Xiong

Salesforce (United States)

Nazneen Fatema Rajani

Hugging Face

Prafulla Kumar Choubey

Salesforce (United States)

Cooperative Institutions

Institute of High Energy Physics

313

University of Chinese Academy of Sciences

131

COMSATS University Islamabad

103

Chinese Academy of Sciences

China Center of Advanced Science and Technology

University of Science and Technology of China

Central China Normal University

Ruhr University Bochum

Inner Mongolia University

Beihang University

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Do Mental Health Problems Affect Safety and Permanency for Children Served by Child Welfare?

PsycEXTRA Dataset (2007)

Yu Bai Marianne M. Hillemeier Rebecca G. Wells

Affect

10.1037/e522532014-126

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Citations (0)

Comparative Analysis of Reinforcement Learning Algorithm based on Tennis Environment

Highlights in Science Engineering and Technology (2023)

Yu Bai Haoyu Dong Qiwei Lian

Reinforcement learning and deep reinforcement learning, as a research hotspot in the field of machine learning, have been widely used in our daily life. In this field, game is playing an extremely important role in the developing of reinforcement learning algorithms. Based on the Tennis environment built by Unity ML Agents, this paper used three algorithms, Proximal Policy Optimization (PPO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG) and Soft Actor-Critic (SAC), combined with PyTorch framework, solved the continuous control problem of this environment. Meanwhile, a group of optimal parameters are obtained through multiple trainings, so that Agents can achieve a perfect effect of solving the continuous control problem in Tennis environment. At the end, this paper compared and analyzed the difference among these three algorithms, summarized the application and properties of each algorithm. For different parameters of the algorithm, this paper also maked a comparison and explained the reasons for some special cases as well which can be used for the future work.

Temporal difference learning

Optimization algorithm

Hotspot (geology)

10.54097/hset.v39i.6721

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Conformal Predictor for Improving Zero-Shot Text Classification Efficiency

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2022)

Prafulla Kumar Choubey Yu Bai Chien-Sheng Wu Wenhao Liu Nazneen Fatema Rajani

Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. 0shot models based on natural language inference (NLI) and next sentence prediction (NSP) employ cross-encoder architecture and infer by making a forward pass through the model for each label-text pair separately. This increases the computational cost to make inferences linearly in the number of labels. In this work, we improve the efficiency of such cross-encoder-based 0shot models by restricting the number of likely labels using another fast base classifier-based conformal predictor (CP) calibrated on samples labeled by the 0shot model. Since a CP generates prediction sets with coverage guarantees, it reduces the number of target labels without excluding the most probable label based on the 0shot model. We experiment with three intent and two topic classification datasets. With a suitable CP for each dataset, we reduce the average inference time for NLI- and NSP-based models by 25.6% and 22.2% respectively, without dropping performance below the predefined error rate of 1%.

Zero (linguistics)

10.18653/v1/2022.emnlp-main.196

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Citations (1)

Local Calibration: Metrics and Recalibration

arXiv (Cornell University) (2021)

Rachel Luo Aadyot Bhatnagar Yu Bai Shengjia Zhao Huan Wang

Probabilistic classifiers output confidence scores along with their predictions, and these confidence scores should be calibrated, i.e., they should reflect the reliability of the prediction. Confidence scores that minimize standard metrics such as the expected calibration error (ECE) accurately measure the reliability on average across the entire population. However, it is in general impossible to measure the reliability of an individual prediction. In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability. For each individual prediction, the LCE measures the average reliability of a set of similar predictions, where similarity is quantified by a kernel function on a pretrained feature space and by a binning scheme over predicted model confidences. We show theoretically that the LCE can be estimated sample-efficiently from data, and empirically find that it reveals miscalibration modes that are more fine-grained than the ECE can detect. Our key result is a novel local recalibration method LoRe, to improve confidence scores for individual predictions and decrease the LCE. Experimentally, we show that our recalibration method produces more accurate confidence scores, which improves downstream fairness and decision making on classification tasks with both image and tabular data.

Kernel (algebra)

Similarity (geometry)

10.48550/arxiv.2102.10809

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Citations (5)

DePA: Improving Non-autoregressive Machine Translation with Dependency-Aware Decoder

arXiv (Cornell University) (2022)

Jiaao Zhan Qian Chen Boxing Chen Wen Wang Yu Bai

Non-autoregressive machine translation (NAT) models have lower translation quality than autoregressive translation (AT) models because NAT decoders do not depend on previous target tokens in the decoder input. We propose a novel and general Dependency-Aware Decoder (DePA) to enhance target dependency modeling in the decoder of fully NAT models from two perspectives: decoder self-attention and decoder input. First, we propose an autoregressive forward-backward pre-training phase before NAT training, which enables the NAT decoder to gradually learn bidirectional target dependencies for the final NAT training. Second, we transform the decoder input from the source language representation space to the target language representation space through a novel attentive transformation process, which enables the decoder to better capture target dependencies. DePA can be applied to any fully NAT models. Extensive experiments show that DePA consistently improves highly competitive and state-of-the-art fully NAT models on widely used WMT and IWSLT benchmarks by up to 1.88 BLEU gain, while maintaining the inference latency comparable to other fully NAT models.

Nat

Representation

10.48550/arxiv.2203.16266

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Citations (2)

Offline Learning in Markov Games with General Function Approximation

arXiv (Cornell University) (2023)

Yuheng Zhang Yu Bai Nan Jiang

We study offline multi-agent reinforcement learning (RL) in Markov games, where the goal is to learn an approximate equilibrium -- such as Nash equilibrium and (Coarse) Correlated Equilibrium -- from an offline dataset pre-collected from the game. Existing works consider relatively restricted tabular or linear models and handle each equilibria separately. In this work, we provide the first framework for sample-efficient offline learning in Markov games under general function approximation, handling all 3 equilibria in a unified manner. By using Bellman-consistent pessimism, we obtain interval estimation for policies' returns, and use both the upper and the lower bounds to obtain a relaxation on the gap of a candidate policy, which becomes our optimization objective. Our results generalize prior works and provide several additional insights. Importantly, we require a data coverage condition that improves over the recently proposed "unilateral concentrability". Our condition allows selective coverage of deviation policies that optimally trade-off between their greediness (as approximate best responses) and coverage, and we show scenarios where this leads to significantly better guarantees. As a new connection, we also show how our algorithmic framework can subsume seemingly different solution concepts designed for the special case of two-player zero-sum games.

Sample complexity

Markov perfect equilibrium

Offline learning

10.48550/arxiv.2302.02571

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PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization

arXiv (Cornell University) (2022)

Xiaochen Liu Yu Bai Jiawei Li Yinan Hu Yang Gao

Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we designed a novel soft prompts architecture coupled with a prompt pre-training plus fine-tuning paradigm that is effective and tunes only extremely light parameters. The soft prompts include continuous input embeddings across an encoder and a decoder to fit the structure of the generation models. Importantly, a novel inner-prompt placed in the text is introduced to capture document-level information. The aim is to devote attention to understanding the document that better prompts the model to generate document-related content. The first step in the summarization procedure is to conduct prompt pre-training with self-supervised pseudo-data. This teaches the model basic summarizing capabilities. The model is then fine-tuned with few-shot examples. Experimental results on the CNN/DailyMail and XSum datasets show that our method, with only 0.1% of the parameters, outperforms full-model tuning where all model parameters are tuned. It also surpasses Prompt Tuning by a large margin and delivers competitive results against Prefix-Tuning with 3% of the parameters.

Margin (machine learning)

Prefix

10.48550/arxiv.2204.04413

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Citations (7)

Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification

arXiv (Cornell University) (2021)

Yu Bai Mei Song Huan Wang Caiming Xiong

Modern machine learning models with high accuracy are often miscalibrated -- the predicted top probability does not reflect the actual accuracy, and tends to be over-confident. It is commonly believed that such over-confidence is mainly due to over-parametrization, in particular when the model is large enough to memorize the training data and maximize the confidence. In this paper, we show theoretically that over-parametrization is not the only reason for over-confidence. We prove that logistic regression is inherently over-confident, in the realizable, under-parametrized setting where the data is generated from the logistic model, and the sample size is much larger than the number of parameters. Further, this over-confidence happens for general well-specified binary classification problems as long as the activation is symmetric and concave on the positive part. Perhaps surprisingly, we also show that over-confidence is not always the case -- there exists another activation function (and a suitable loss function) under which the learned classifier is under-confident at some probability values. Overall, our theory provides a precise characterization of calibration in realizable binary classification, which we verify on simulations and real data experiments.

Parametrization (atmospheric modeling)

Binary classification

Binary data

Source

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Citations (0)

Lightweight evaluation algorithm for infix arithmetic expression

Journal of Computer Applications (2013)

Yu Bai Xian'e GUO

Expression (computer science)

10.3724/sp.j.1087.2013.03163

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