Search Results for author: Changyou Chen

Found 90 papers, 22 papers with code

Rethinking Sentiment Style Transfer

no code implementations Findings (EMNLP) 2021 Ping Yu, Yang Zhao, Chunyuan Li, Changyou Chen

To overcome this issue, we propose a graph-based method to extract attribute content and attribute-independent content from input sentences in the YELP dataset and IMDB dataset.

Style Transfer Text Style Transfer

A Generic Approach for Enhancing GANs by Regularized Latent Optimization

no code implementations7 Dec 2021 Yufan Zhou, Chunyuan Li, Changyou Chen, Jinhui Xu

With the rapidly growing model complexity and data volume, training deep generative models (DGMs) for better performance has becoming an increasingly more important challenge.

Image Inpainting text-guided-image-editing +2

LAFITE: Towards Language-Free Training for Text-to-Image Generation

1 code implementation27 Nov 2021 Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, Tong Sun

One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs.

 Ranked #1 on Text-to-Image Generation on COCO (using extra training data)

Text to image generation Zero-Shot Text-to-Image Generation

Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees

no code implementations17 Nov 2021 Yaman Kumar Singla, Sriram Krishna, Rajiv Ratn Shah, Changyou Chen

Automated Scoring (AS), the natural language processing task of scoring essays and speeches in an educational testing setting, is growing in popularity and being deployed across contexts from government examinations to companies providing language proficiency services.

Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks

no code implementations13 Oct 2021 Anuj Saraswat, Mehar Bhatia, Yaman Kumar Singla, Changyou Chen, Rajiv Ratn Shah

Recent studies in speech perception have been closely linked to fields of cognitive psychology, phonology, and phonetics in linguistics.

Lip Reading Visual Speech Recognition

Rethinking Deep Face Restoration

no code implementations29 Sep 2021 Yang Zhao, Yu-Chuan Su, Chun-Te Chu, Yandong Li, Marius Renn, Yukun Zhu, Changyou Chen, Xuhui Jia

While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces.

Face Generation Face Reconstruction

MINIMAL: Mining Models for Data Free Universal Adversarial Triggers

1 code implementation25 Sep 2021 Swapnil Parekh, Yaman Singla Kumar, Somesh Singh, Changyou Chen, Balaji Krishnamurthy, Rajiv Ratn Shah

It is well known that natural language models are vulnerable to adversarial attacks, which are mostly input-specific in nature.

Natural Language Inference

AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses

no code implementations24 Sep 2021 Yaman Kumar Singla, Swapnil Parekh, Somesh Singh, Junyi Jessy Li, Rajiv Ratn Shah, Changyou Chen

This is in stark contrast to recent probing studies on pre-trained representation learning models, which show that rich linguistic features such as parts-of-speech and morphology are encoded by them.

Representation Learning

Speaker-Conditioned Hierarchical Modeling for Automated Speech Scoring

no code implementations30 Aug 2021 Yaman Kumar Singla, Avykat Gupta, Shaurya Bagga, Changyou Chen, Balaji Krishnamurthy, Rajiv Ratn Shah

In our technique, we take advantage of the fact that oral proficiency tests rate multiple responses for a candidate.

Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval

1 code implementation ACL 2021 Zijing Ou, Qinliang Su, Jianxing Yu, Bang Liu, Jingwen Wang, Ruihui Zhao, Changyou Chen, Yefeng Zheng

With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval.

Information Retrieval

Unsupervised Hashing with Contrastive Information Bottleneck

1 code implementation13 May 2021 Zexuan Qiu, Qinliang Su, Zijing Ou, Jianxing Yu, Changyou Chen

Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible.

Contrastive Learning

Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels

no code implementations10 May 2021 Yufan Zhou, Changyou Chen, Jinhui Xu

Learning high-dimensional distributions is an important yet challenging problem in machine learning with applications in various domains.

Towards Fair Federated Learning with Zero-Shot Data Augmentation

no code implementations27 Apr 2021 Weituo Hao, Mostafa El-Khamy, Jungwon Lee, Jianyi Zhang, Kevin J Liang, Changyou Chen, Lawrence Carin

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data.

Data Augmentation Fairness +1

Meta-Learning with Neural Tangent Kernels

no code implementations7 Feb 2021 Yufan Zhou, Zhenyi Wang, Jiayi Xian, Changyou Chen, Jinhui Xu

We achieve this goal by 1) replacing the adaptation with a fast-adaptive regularizer in the RKHS; and 2) solving the adaptation analytically based on the NTK theory.


Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events

1 code implementation4 Jan 2021 Le Fang, Tao Zeng, Chaochun Liu, Liefeng Bo, Wen Dong, Changyou Chen

Our paper is among the first ones by our knowledge to propose a model and to create datasets for the task of "outline to story".

Keyword Extraction Language Modelling +2

Transformer-based Conditional Variational Autoencoder for Controllable Story Generation

1 code implementation4 Jan 2021 Le Fang, Tao Zeng, Chaochun Liu, Liefeng Bo, Wen Dong, Changyou Chen

In this paper, we advocate to revive latent variable modeling, essentially the power of representation learning, in the era of Transformers to enhance controllability without hurting state-of-the-art generation effectiveness.

Representation Learning Story Generation

SDA: Improving Text Generation with Self Data Augmentation

no code implementations2 Jan 2021 Ping Yu, Ruiyi Zhang, Yang Zhao, Yizhe Zhang, Chunyuan Li, Changyou Chen

Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.

Data Augmentation Imitation Learning +1

What all do audio transformer models hear? Probing Acoustic Representations for Language Delivery and its Structure

no code implementations2 Jan 2021 Jui Shah, Yaman Kumar Singla, Changyou Chen, Rajiv Ratn Shah

In recent times, BERT based transformer models have become an inseparable part of the 'tech stack' of text processing models.

Towards Learning to Remember in Meta Learning of Sequential Domains

no code implementations1 Jan 2021 Zhenyi Wang, Tiehang Duan, Donglin Zhan, Changyou Chen

However, a natural generalization to the sequential domain setting to avoid catastrophe forgetting has not been well investigated.

Continual Learning Meta-Learning

Meta-Learning in Reproducing Kernel Hilbert Space

no code implementations ICLR 2021 Yufan Zhou, Zhenyi Wang, Jiayi Xian, Changyou Chen, Jinhui Xu

Within this paradigm, we introduce two meta learning algorithms in RKHS, which no longer need an explicit inner-loop adaptation as in the MAML framework.


ReMP: Rectified Metric Propagation for Few-Shot Learning

no code implementations2 Dec 2020 Yang Zhao, Chunyuan Li, Ping Yu, Changyou Chen

Few-shot learning features the capability of generalizing from a few examples.

Few-Shot Learning

Bayesian Multi-type Mean Field Multi-agent Imitation Learning

no code implementations NeurIPS 2020 Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong

We demonstrate the performance of our algorithm through benchmarking with three state-of-the-art multi-agent imitation learning algorithms on several tasks, including solving a multi-agent traffic optimization problem in a real-world transportation network.

Imitation Learning

Unpaired Image-to-Image Translation via Latent Energy Transport

1 code implementation CVPR 2021 Yang Zhao, Changyou Chen

Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods.

Image Reconstruction Image-to-Image Translation +1

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.

Structure-Aware Human-Action Generation

1 code implementation ECCV 2020 Ping Yu, Yang Zhao, Chunyuan Li, Junsong Yuan, Changyou Chen

Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence.

Action Generation Frame +2

Generative Semantic Hashing Enhanced via Boltzmann Machines

no code implementations ACL 2020 Lin Zheng, Qinliang Su, Dinghan Shen, Changyou Chen

Generative semantic hashing is a promising technique for large-scale information retrieval thanks to its fast retrieval speed and small memory footprint.

Information Retrieval

Graph Neural Networks with Composite Kernels

no code implementations16 May 2020 Yufan Zhou, Jiayi Xian, Changyou Chen, Jinhui Xu

We then propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space.

Graph Attention

Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition

no code implementations14 May 2020 Tianhang Zheng, Sheng Liu, Changyou Chen, Junsong Yuan, Baochun Li, Kui Ren

We first formulate generation of adversarial skeleton actions as a constrained optimization problem by representing or approximating the physiological and physical constraints with mathematical formulations.

Action Recognition Skeleton Based Action Recognition

Reward Constrained Interactive Recommendation with Natural Language Feedback

no code implementations4 May 2020 Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen, Lawrence Carin

Text-based interactive recommendation provides richer user feedback and has demonstrated advantages over traditional interactive recommender systems.

Recommendation Systems reinforcement-learning +1

Bayesian Meta Sampling for Fast Uncertainty Adaptation

1 code implementation ICLR 2020 Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen

Specifically, we propose a Bayesian meta sampling framework consisting of two main components: a meta sampler and a sample adapter.


Decomposed Adversarial Learned Inference

no code implementations21 Apr 2020 Alexander Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao

Effective inference for a generative adversarial model remains an important and challenging problem.

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

1 code implementation AAAI 2019 Zhenyi Wang, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan, Changyou Chen

In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality.

Action Generation Frame

Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning

no code implementations NeurIPS 2019 Ruiyi Zhang, Tong Yu, Yilin Shen, Hongxia Jin, Changyou Chen

Text-based interactive recommendation provides richer user preferences and has demonstrated advantages over traditional interactive recommender systems.

Recommendation Systems reinforcement-learning +1

Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution

1 code implementation25 Nov 2019 Yitong Yan, Chuangchuang Liu, Changyou Chen, Xianfang Sun, Longcun Jin, Xiang Zhou

Firstly, instead of producing a single score to discriminate images between real and fake, we propose a variant, called Fine-grained Attention Generative Adversarial Network for image super-resolution (FASRGAN), to discriminate each pixel between real and fake.

Image Super-Resolution Object Recognition

Implicit Deep Latent Variable Models for Text Generation

1 code implementation IJCNLP 2019 Le Fang, Chunyuan Li, Jianfeng Gao, Wen Dong, Changyou Chen

Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation.

Language Modelling Response Generation +2

Document Hashing with Mixture-Prior Generative Models

no code implementations IJCNLP 2019 Wei Dong, Qinliang Su, Dinghan Shen, Changyou Chen

Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes.

Information Retrieval

Bayesian Uncertainty Matching for Unsupervised Domain Adaptation

no code implementations24 Jun 2019 Jun Wen, Nenggan Zheng, Junsong Yuan, Zhefeng Gong, Changyou Chen

By imposing distribution matching on both features and labels (via uncertainty), label distribution mismatching in source and target data is effectively alleviated, encouraging the classifier to produce consistent predictions across domains.

Unsupervised Domain Adaptation

On Norm-Agnostic Robustness of Adversarial Training

no code implementations15 May 2019 Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin

Adversarial examples are carefully perturbed in-puts for fooling machine learning models.

Second-Order Adversarial Attack and Certifiable Robustness

no code implementations ICLR 2019 Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin

In this paper, we propose a powerful second-order attack method that reduces the accuracy of the defense model by Madry et al. (2017).

Adversarial Attack

Scalable Thompson Sampling via Optimal Transport

no code implementations19 Feb 2019 Ruiyi Zhang, Zheng Wen, Changyou Chen, Lawrence Carin

Thompson sampling (TS) is a class of algorithms for sequential decision-making, which requires maintaining a posterior distribution over a model.

Decision Making

Adversarial Learning of a Sampler Based on an Unnormalized Distribution

1 code implementation3 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.


PointCloud Saliency Maps

3 code implementations ICCV 2019 Tianhang Zheng, Changyou Chen, Junsong Yuan, Bo Li, Kui Ren

Our motivation for constructing a saliency map is by point dropping, which is a non-differentiable operator.

Self-Adversarially Learned Bayesian Sampling

no code implementations21 Nov 2018 Yang Zhao, Jianyi Zhang, Changyou Chen

Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models.


Variance Reduction in Stochastic Particle-Optimization Sampling

no code implementations ICML 2020 Jianyi Zhang, Yang Zhao, Changyou Chen

Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) algorithms based on Wasserstein gradient flows.


Sequence Generation with Guider Network

no code implementations2 Nov 2018 Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Liqun Chen, Dinghan Shen, Guoyin Wang, Lawrence Carin

Sequence generation with reinforcement learning (RL) has received significant attention recently.


Is PGD-Adversarial Training Necessary? Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only

no code implementations10 Oct 2018 Tianhang Zheng, Changyou Chen, Kui Ren

In this paper, we give a negative answer by proposing a training paradigm that is comparable to PGD adversarial training on several standard datasets, while only using noisy-natural samples.

Adversarial Attack Quantization

Towards More Theoretically-Grounded Particle Optimization Sampling for Deep Learning

no code implementations27 Sep 2018 Jianyi Zhang, Ruiyi Zhang, Changyou Chen

With such theoretical guarantees, SPOS can be safely and effectively applied on both Bayesian DL and deep RL tasks.


AIM: Adversarial Inference by Matching Priors and Conditionals

no code implementations27 Sep 2018 Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao

We propose a novel approach, Adversarial Inference by Matching priors and conditionals (AIM), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model.

Certified Adversarial Robustness with Additive Noise

2 code implementations NeurIPS 2019 Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin

The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm.

Adversarial Attack Adversarial Robustness

Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory

no code implementations5 Sep 2018 Jianyi Zhang, Ruiyi Zhang, Lawrence Carin, Changyou Chen

Particle-optimization-based sampling (POS) is a recently developed effective sampling technique that interactively updates a set of particles.


Distributionally Adversarial Attack

4 code implementations16 Aug 2018 Tianhang Zheng, Changyou Chen, Kui Ren

Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks.

Adversarial Attack

Policy Optimization as Wasserstein Gradient Flows

no code implementations ICML 2018 Ruiyi Zhang, Changyou Chen, Chunyuan Li, Lawrence Carin

Policy optimization is a core component of reinforcement learning (RL), and most existing RL methods directly optimize parameters of a policy based on maximizing the expected total reward, or its surrogate.


A Unified Particle-Optimization Framework for Scalable Bayesian Sampling

no code implementations29 May 2018 Changyou Chen, Ruiyi Zhang, Wenlin Wang, Bai Li, Liqun Chen

There has been recent interest in developing scalable Bayesian sampling methods such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) for big-data analysis.

Learning Structural Weight Uncertainty for Sequential Decision-Making

1 code implementation30 Dec 2017 Ruiyi Zhang, Chunyuan Li, Changyou Chen, Lawrence Carin

Learning probability distributions on the weights of neural networks (NNs) has recently proven beneficial in many applications.

Decision Making Multi-Armed Bandits +1

On Connecting Stochastic Gradient MCMC and Differential Privacy

no code implementations25 Dec 2017 Bai Li, Changyou Chen, Hao liu, Lawrence Carin

Significant success has been realized recently on applying machine learning to real-world applications.

Particle Optimization in Stochastic Gradient MCMC

no code implementations29 Nov 2017 Changyou Chen, Ruiyi Zhang

Stochastic gradient Markov chain Monte Carlo (SG-MCMC) has been increasingly popular in Bayesian learning due to its ability to deal with large data.

ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching

5 code implementations NeurIPS 2017 Chunyuan Li, Hao liu, Changyou Chen, Yunchen Pu, Liqun Chen, Ricardo Henao, Lawrence Carin

We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching.

A Convergence Analysis for A Class of Practical Variance-Reduction Stochastic Gradient MCMC

no code implementations4 Sep 2017 Changyou Chen, Wenlin Wang, Yizhe Zhang, Qinliang Su, Lawrence Carin

However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate.

Stochastic Optimization

Continuous-Time Flows for Efficient Inference and Density Estimation

no code implementations ICML 2018 Changyou Chen, Chunyuan Li, Liqun Chen, Wenlin Wang, Yunchen Pu, Lawrence Carin

Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees.

Density Estimation

Stochastic Gradient Monomial Gamma Sampler

no code implementations ICML 2017 Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin

A framework is proposed to improve the sampling efficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo.

Earliness-Aware Deep Convolutional Networks for Early Time Series Classification

no code implementations14 Nov 2016 Wenlin Wang, Changyou Chen, Wenqi Wang, Piyush Rai, Lawrence Carin

Unlike most existing methods for early classification of time series data, that are designed to solve this problem under the assumption of the availability of a good set of pre-defined (often hand-crafted) features, our framework can jointly perform feature learning (by learning a deep hierarchy of \emph{shapelets} capturing the salient characteristics in each time series), along with a dynamic truncation model to help our deep feature learning architecture focus on the early parts of each time series.

Classification General Classification +2

On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators

no code implementations NeurIPS 2015 Changyou Chen, Nan Ding, Lawrence Carin

Our theoretical results show faster convergence rates and more accurate invariant measures for SG-MCMCs with higher-order integrators.

Stochastic Gradient MCMC with Stale Gradients

no code implementations NeurIPS 2016 Changyou Chen, Nan Ding, Chunyuan Li, Yizhe Zhang, Lawrence Carin

In this paper we develop theory to show that while the bias and MSE of an SG-MCMC algorithm depend on the staleness of stochastic gradients, its estimation variance (relative to the expected estimate, based on a prescribed number of samples) is independent of it.

Nonparametric Bayesian Topic Modelling with the Hierarchical Pitman-Yor Processes

no code implementations22 Sep 2016 Kar Wai Lim, Wray Buntine, Changyou Chen, Lan Du

In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics.

Topic Models

Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling

no code implementations22 Sep 2016 Kar Wai Lim, Changyou Chen, Wray Buntine

Exploiting this additional information, we propose the Twitter-Network (TN) topic model to jointly model the text and the social network in a full Bayesian nonparametric way.

Topic Models

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models

no code implementations2 Jun 2016 Qinliang Su, Xuejun Liao, Changyou Chen, Lawrence Carin

We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning.

General Classification

Towards Unifying Hamiltonian Monte Carlo and Slice Sampling

no code implementations NeurIPS 2016 Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin

We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.

Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization

1 code implementation25 Dec 2015 Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin

Stochastic gradient Markov chain Monte Carlo (SG-MCMC) methods are Bayesian analogs to popular stochastic optimization methods; however, this connection is not well studied.

Stochastic Optimization

High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models

no code implementations23 Dec 2015 Chunyuan Li, Changyou Chen, Kai Fan, Lawrence Carin

Stochastic gradient MCMC algorithms (SG-MCMC) are a family of diffusion-based sampling methods for large-scale Bayesian learning.

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks

no code implementations23 Dec 2015 Chunyuan Li, Changyou Chen, David Carlson, Lawrence Carin

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace and more

Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data

no code implementations18 Aug 2015 Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin

We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors.

Robust Bayesian Max-Margin Clustering

no code implementations NeurIPS 2014 Changyou Chen, Jun Zhu, Xinhua Zhang

We present max-margin Bayesian clustering (BMC), a general and robust framework that incorporates the max-margin criterion into Bayesian clustering models, as well as two concrete models of BMC to demonstrate its flexibility and effectiveness in dealing with different clustering tasks.

Bayesian Sampling Using Stochastic Gradient Thermostats

no code implementations NeurIPS 2014 Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven

To remedy this problem, we show that one can leverage a small number of additional variables in order to stabilize momentum fluctuations induced by the unknown noise.

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