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no code implementations • 30 Jan 2022 • Liu Ziyin, HANLIN ZHANG, Xiangming Meng, Yuting Lu, Eric Xing, Masahito Ueda

This work theoretically studies stochastic neural networks, a main type of neural network in use.

1 code implementation • 3 Jan 2022 • Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric Xing

This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework.

no code implementations • 3 Dec 2021 • Zechun Liu, Zhiqiang Shen, Yun Long, Eric Xing, Kwang-Ting Cheng, Chas Leichner

Then we use the synthesized data and their predicted soft-labels to guide neural architecture search.

2 code implementations • 2 Dec 2021 • Zhiqiang Shen, Eric Xing

In this study, we present a Fast Knowledge Distillation (FKD) framework that replicates the distillation training phase and generates soft labels using the multi-crop KD approach, while training faster than ReLabel since no post-processes such as RoI align and softmax operations are used.

Ranked #309 on Image Classification on ImageNet

1 code implementation • 29 Nov 2021 • Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric Xing, Zhiqiang Shen

The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i. e., uniform strategy, due to its superior representational capacity.

1 code implementation • 11 Nov 2021 • Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric Xing, Pengtao Xie

We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision.

1 code implementation • 9 Nov 2021 • Zhiqiang Shen, Zechun Liu, Eric Xing

The proposed sliced recursive operation allows us to build a transformer with more than 100 or even 1000 layers effortlessly under a still small size (13~15M), to avoid difficulties in optimization when the model size is too large.

Ranked #138 on Image Classification on ImageNet

no code implementations • 5 Nov 2021 • Haohan Wang, Zeyi Huang, HANLIN ZHANG, Eric Xing

Machine learning has demonstrated remarkable prediction accuracy over i. i. d data, but the accuracy often drops when tested with data from another distribution.

no code implementations • 5 Nov 2021 • Haohan Wang, Bryon Aragam, Eric Xing

Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS.

no code implementations • NeurIPS 2021 • Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song

We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports.

no code implementations • 29 Sep 2021 • Han Guo, Bowen Tan, Zhengzhong Liu, Eric Xing, Zhiting Hu

We apply the approach to a wide range of text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation.

no code implementations • NeurIPS Workshop AI4Scien 2021 • Shentong Mo, Xi Fu, Chenyang Hong, Yizhen Chen, Yuxuan Zheng, Xiangru Tang, Yanyan Lan, Zhiqiang Shen, Eric Xing

In this work, we propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call GeneBERT.

1 code implementation • ACL 2021 • Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie

Training complex dialog generation models on small datasets bears high risk of overfitting.

1 code implementation • ACL 2021 • Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

In this paper, we aim to develop a pathological visual question answering framework to analyze pathology images and answer medical questions related to these images.

no code implementations • 17 Jun 2021 • Shuai Lin, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, Xiaodan Liang

However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i. e., the negatives likely having the same semantic structure with the query, leading to performance degradation.

1 code implementation • 20 May 2021 • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing

In this work, we provide some theoretical insight into the properties of QNNs by presenting and analyzing a new form of invariance embedded in QNNs for both quantum binary classification and quantum representation learning, which we term negational symmetry.

no code implementations • 30 Jan 2021 • Maruan Al-Shedivat, Liam Li, Eric Xing, Ameet Talwalkar

Meta-learning has enabled learning statistical models that can be quickly adapted to new prediction tasks.

no code implementations • 1 Jan 2021 • Haohan Wang, Zeyi Huang, Eric Xing

In this paper, we formally study the generalization error bound for this setup with the knowledge of how the spurious features are associated with the label.

no code implementations • 1 Jan 2021 • Haohan Wang, Zeyi Huang, Xindi Wu, Eric Xing

Data augmentation is one of the most popular techniques for improving the robustness of neural networks.

1 code implementation • ICLR 2021 • Benedikt Boecking, Willie Neiswanger, Eric Xing, Artur Dubrawski

Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.

no code implementations • NeurIPS 2020 • Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric Xing

Synchronization is a key step in data-parallel distributed machine learning (ML).

1 code implementation • ICLR 2021 • Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective.

no code implementations • 6 Oct 2020 • Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

To deal with the issue that a publicly available pathology VQA dataset is lacking, we create PathVQA dataset.

no code implementations • 28 Sep 2020 • Ben Lengerich, Eric Xing, Rich Caruana

Conversely, the probability of an interaction of $k$ variables surviving Dropout at rate $p$ is $\mathcal{O}((1-p)^k)$.

1 code implementation • 17 Jun 2020 • Xingyi Yang, Nandiraju Gireesh, Eric Xing, Pengtao Xie

To address this problem, we develop methods to generate view-consistent, high-fidelity, and high-resolution X-ray images from radiology reports to facilitate radiology training of medical students.

1 code implementation • 11 May 2020 • Wenmian Yang, Guangtao Zeng, Bowen Tan, Zeqian Ju, Subrato Chakravorty, Xuehai He, Shu Chen, Xingyi Yang, Qingyang Wu, Zhou Yu, Eric Xing, Pengtao Xie

On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.

no code implementations • ACL 2019 • Baoyu Jing, Zeya Wang, Eric Xing

In this work, we propose a novel framework that exploits the structure information between and within report sections for generating CXR imaging reports.

1 code implementation • medRxiv 2020 • Xuehai He, Xingyi Yang, Shanghang Zhang, Jinyu Zhao, Yichen Zhang, Eric Xing, Pengtao Xie

Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain.

no code implementations • 7 Apr 2020 • Emmanouil Antonios Platanios, Maruan Al-Shedivat, Eric Xing, Tom Mitchell

Many machine learning systems today are trained on large amounts of human-annotated data.

2 code implementations • 11 Mar 2020 • Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, Eric Xing

This drawback hinders the model from learning subtle variance and fine-grained information.

3 code implementations • 7 Mar 2020 • Xuehai He, Yichen Zhang, Luntian Mou, Eric Xing, Pengtao Xie

To achieve this goal, the first step is to create a visual question answering (VQA) dataset where the AI agent is presented with a pathology image together with a question and is asked to give the correct answer.

1 code implementation • 20 Dec 2019 • Kevin Tran, Willie Neiswanger, Junwoong Yoon, Eric Xing, Zachary W. Ulissi

These uncertainty estimates are instrumental for determining which materials to screen next, but there is not yet a standard procedure for judging the quality of such uncertainty estimates objectively.

Materials Science Computational Physics

no code implementations • 28 Sep 2019 • Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing

The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses.

no code implementations • 25 Sep 2019 • Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing

Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.

no code implementations • 25 Sep 2019 • Emmanouil Antonios Platanios, Maruan Al-Shedivat, Eric Xing, Tom Mitchell

Many machine learning systems today are trained on large amounts of human-annotated data.

1 code implementation • 12 Jun 2019 • Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, Ruslan Salakhutdinov

The SMM objective can be viewed as a two-player, zero-sum game between a state density model and a parametric policy, an idea that we use to build an algorithm for optimizing the SMM objective.

no code implementations • 31 May 2019 • Gregory Plumb, Maruan Al-Shedivat, Eric Xing, Ameet Talwalkar

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality.

no code implementations • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

Machine learning (ML) techniques are enjoying rapidly increasing adoption.

3 code implementations • 19 Feb 2019 • Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric Xing

Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when explaining a black-box decision system.

1 code implementation • NeurIPS 2020 • Gregory Plumb, Maruan Al-Shedivat, Angel Alexander Cabrera, Adam Perer, Eric Xing, Ameet Talwalkar

Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable.

1 code implementation • 31 Jan 2019 • Willie Neiswanger, Kirthevasan Kandasamy, Barnabas Poczos, Jeff Schneider, Eric Xing

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum.

1 code implementation • Findings of the Association for Computational Linguistics 2020 • Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric Xing, Zhiting Hu

That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.

1 code implementation • 1 Jan 2019 • Wanrong Zhu, Zhiting Hu, Eric Xing

Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of retrieval-and-rewriting.

no code implementations • 24 Nov 2018 • Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric Xing

Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency.

no code implementations • 20 Nov 2018 • Xiangan Liu, Keyang Xu, Pengtao Xie, Eric Xing

Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs).

1 code implementation • 16 Nov 2018 • Maruan Al-Shedivat, Lisa Lee, Ruslan Salakhutdinov, Eric Xing

Next, we propose to measure the complexity of each environment by constructing dependency graphs between the goals and analytically computing \emph{hitting times} of a random walk in the graph.

no code implementations • 31 Oct 2018 • Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur MD, Frank Papay MD, Ashish K. Khanna MD, Jacek B. Cywinski MD, Kamal Maheshwari MD, Pengtao Xie, Eric Xing

This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes.

1 code implementation • 4 Oct 2018 • Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.

no code implementations • ICLR 2019 • Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing

Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.

no code implementations • 5 Aug 2018 • Hongbao Zhang, Pengtao Xie, Eric Xing

In this paper, we propose a probabilistic framework based on deep generative models for MVI.

no code implementations • ECCV 2018 • Xiaodan Liang, Tairui Wang, Luona Yang, Eric Xing

To our knowledge, this is the first successful case of the learned driving policy through reinforcement learning in the high-fidelity simulator, which performs better-than supervised imitation learning.

no code implementations • 10 Jul 2018 • Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric Xing

Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models.

no code implementations • ICML 2018 • Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric Xing

Variable selection is a classic problem in machine learning (ML), widely used to find important explanatory factors, and improve generalization performance and interpretability of ML models.

no code implementations • ACL 2018 • Pengtao Xie, Eric Xing

The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases.

no code implementations • WS 2018 • Zhiting Hu, Zichao Yang, Tiancheng Zhao, Haoran Shi, Junxian He, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Lianhui Qin, Devendra Singh Chaplot, Bowen Tan, Xingjiang Yu, Eric Xing

The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.

1 code implementation • ICML 2018 • Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, Shimon Whiteson

Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives.

no code implementations • NeurIPS 2018 • Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Xiaodan Liang, Lianhui Qin, Haoye Dong, Eric Xing

The broad set of deep generative models (DGMs) has achieved remarkable advances.

2 code implementations • ICML 2018 • Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric Xing, Ruslan Salakhutdinov

Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture.

no code implementations • NAACL 2018 • Mrinmaya Sachan, Eric Xing

The two tasks of question answering and question generation are usually tackled separately in the NLP literature.

no code implementations • CVPR 2018 • Xiaodan Liang, Hongfei Zhou, Eric Xing

Moreoever, we demonstrate a universal segmentation model that is jointly trained on diverse datasets can surpass the performance of the common fine-tuning scheme for exploiting multiple domain knowledge.

Ranked #53 on Semantic Segmentation on Cityscapes test

no code implementations • 12 Feb 2018 • Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu

Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution.

1 code implementation • NeurIPS 2018 • Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, Eric Xing

A common use case for BO in machine learning is model selection, where it is not possible to analytically model the generalisation performance of a statistical model, and we resort to noisy and expensive training and validation procedures to choose the best model.

no code implementations • ECCV 2018 • Luona Yang, Xiaodan Liang, Tairui Wang, Eric Xing

In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale.

no code implementations • 6 Dec 2017 • Christy Li, Dimitris Konomis, Graham Neubig, Pengtao Xie, Carol Cheng, Eric Xing

The hope is that the tool can be used to reduce mis-diagnosis.

4 code implementations • ACL 2018 • Baoyu Jing, Pengtao Xie, Eric Xing

To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the pre- diction of tags and the generation of para- graphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs.

no code implementations • 4 Nov 2017 • Yuan Yang, Pengtao Xie, Xin Gao, Carol Cheng, Christy Li, Hongbao Zhang, Eric Xing

Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen to plan for and what possible changes on initial medication may occur during an inpatient stay.

no code implementations • EMNLP 2017 • Mrinmaya Sachan, Kumar Dubey, Eric Xing

These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.

no code implementations • SEMEVAL 2017 • Mrinmaya Sachan, Eric Xing

As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks.

no code implementations • ACL 2017 • Pengtao Xie, Eric Xing

Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task.

no code implementations • 30 May 2017 • Junier B. Oliva, Kumar Avinava Dubey, Barnabas Poczos, Eric Xing, Jeff Schneider

After, an RNN is used to compute the conditional distributions of the latent covariates.

no code implementations • ICCV 2017 • Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric Xing

In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space.

no code implementations • 25 Jul 2016 • Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric Xing, Jieping Ye

Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.

no code implementations • ICML 2017 • Willie Neiswanger, Eric Xing

However, we demonstrate that IS will fail for many choices of the target prior, depending on its parametric form and similarity to the false prior.

2 code implementations • ACL 2016 • Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric Xing

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models.

Ranked #53 on Sentiment Analysis on SST-2 Binary classification

no code implementations • 23 Dec 2015 • Pengtao Xie, Yuntian Deng, Eric Xing

On two popular latent variable models --- restricted Boltzmann machine and distance metric learning, we demonstrate that MAR can effectively capture long-tail patterns, reduce model complexity without sacrificing expressivity and improve interpretability.

no code implementations • 19 Dec 2015 • Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric Xing

To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks.

no code implementations • 26 Nov 2015 • Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yao-Liang Yu, Eric Xing

Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.

no code implementations • 23 Nov 2015 • Pengtao Xie, Yuntian Deng, Eric Xing

Recently diversity-inducing regularization methods for latent variable models (LVMs), which encourage the components in LVMs to be diverse, have been studied to address several issues involved in latent variable modeling: (1) how to capture long-tail patterns underlying data; (2) how to reduce model complexity without sacrificing expressivity; (3) how to improve the interpretability of learned patterns.

no code implementations • 13 Nov 2015 • William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert van Panhuis, Eric Xing

We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance structure.

no code implementations • 14 Oct 2015 • Willie Neiswanger, Chong Wang, Eric Xing

We develop a parallel variational inference (VI) procedure for use in data-distributed settings, where each machine only has access to a subset of data and runs VI independently, without communicating with other machines.

no code implementations • 19 Dec 2014 • Pengtao Xie, Eric Xing

In this paper, we propose Cauchy Principal Component Analysis (Cauchy PCA), a very simple yet effective PCA method which is robust to various types of noise.

no code implementations • 18 Dec 2014 • Pengtao Xie, Eric Xing

In large scale machine learning and data mining problems with high feature dimensionality, the Euclidean distance between data points can be uninformative, and Distance Metric Learning (DML) is often desired to learn a proper similarity measure (using side information such as example data pairs being similar or dissimilar).

no code implementations • 27 Oct 2014 • Junier Oliva, Willie Neiswanger, Barnabas Poczos, Eric Xing, Jeff Schneider

Function to function regression (FFR) covers a large range of interesting applications including time-series prediction problems, and also more general tasks like studying a mapping between two separate types of distributions.

no code implementations • 19 Sep 2014 • Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yao-Liang Yu, Eric Xing

Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology.

no code implementations • 16 Jan 2014 • Le Song, Han Liu, Ankur Parikh, Eric Xing

Tree structured graphical models are powerful at expressing long range or hierarchical dependency among many variables, and have been widely applied in different areas of computer science and statistics.

no code implementations • 19 Nov 2013 • Willie Neiswanger, Chong Wang, Eric Xing

This embarrassingly parallel algorithm allows each machine to act independently on a subset of the data (without communication) until the final combination stage.

no code implementations • 10 Nov 2013 • Junier B. Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

We study the problem of distribution to real-value regression, where one aims to regress a mapping $f$ that takes in a distribution input covariate $P\in \mathcal{I}$ (for a non-parametric family of distributions $\mathcal{I}$) and outputs a real-valued response $Y=f(P) + \epsilon$.

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