Search Results for author: Ke Bai

Found 11 papers, 4 papers with code

Everyone Deserves A Reward: Learning Customized Human Preferences

1 code implementation6 Sep 2023 Pengyu Cheng, Jiawen Xie, Ke Bai, Yong Dai, Nan Du

Besides, from the perspective of data efficiency, we propose a three-stage customized RM learning scheme, then empirically verify its effectiveness on both general preference datasets and our DSP set.

Imitation Learning

Open World Classification with Adaptive Negative Samples

no code implementations9 Mar 2023 Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin

Open world classification is a task in natural language processing with key practical relevance and impact.

Classification

Collaborative Anomaly Detection

no code implementations20 Sep 2022 Ke Bai, Aonan Zhang, Zhizhong Li, Ricardo Heano, Chong Wang, Lawrence Carin

In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item.

Anomaly Detection Density Estimation +1

Variational Inference with Holder Bounds

no code implementations4 Nov 2021 Junya Chen, Danni Lu, Zidi Xiu, Ke Bai, Lawrence Carin, Chenyang Tao

In this work, we present a careful analysis of the thermodynamic variational objective (TVO), bridging the gap between existing variational objectives and shedding new insights to advance the field.

Variational Inference

Semantic Matching for Sequence-to-Sequence Learning

no code implementations Findings of the Association for Computational Linguistics 2020 Ruiyi Zhang, Changyou Chen, Xinyuan Zhang, Ke Bai, Lawrence Carin

In sequence-to-sequence models, classical optimal transport (OT) can be applied to semantically match generated sentences with target sentences.

Weakly supervised cross-domain alignment with optimal transport

no code implementations14 Aug 2020 Siyang Yuan, Ke Bai, Liqun Chen, Yizhe Zhang, Chenyang Tao, Chunyuan Li, Guoyin Wang, Ricardo Henao, Lawrence Carin

Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural language processing.

Learning Implicit Text Generation via Feature Matching

no code implementations ACL 2020 Inkit Padhi, Pierre Dognin, Ke Bai, Cicero Nogueira dos santos, Vijil Chenthamarakshan, Youssef Mroueh, Payel Das

Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks.

Conditional Text Generation Style Transfer +2

Regularizing Reasons for Outfit Evaluation with Gradient Penalty

no code implementations2 Feb 2020 Xingxing Zou, Zhizhong Li, Ke Bai, Dahua Lin, Waikeung Wong

In this paper, we build an outfit evaluation system which provides feedbacks consisting of a judgment with a convincing explanation.

Sentence

GO Gradient for Expectation-Based Objectives

1 code implementation ICLR 2019 Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin

Within many machine learning algorithms, a fundamental problem concerns efficient calculation of an unbiased gradient wrt parameters $\gammav$ for expectation-based objectives $\Ebb_{q_{\gammav} (\yv)} [f(\yv)]$.

Adversarial Learning of a Sampler Based on an Unnormalized Distribution

3 code implementations3 Jan 2019 Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin

We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples.

Q-Learning

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