Notes on accepted papers of NIPS2009

September 15, 2009

今年挺关注这个顶级牛会,NIPS2009的accepted papers出来了,以前没注意,现在发现名单里竟是熟悉的面孔。虽说proceeding还没出来,但可以查到的有意思的信息还蛮多,大致整理如下,欢迎补充(注:只列出了自己了解或者知道的researchers):

带头大哥paper machines:

J. Tenenbaum
NIPS 2009    Poster    Perceptual Multistability as Markov Chain Monte Carlo Inference
NIPS 2009    Poster    Modelling Relational Data using Bayesian Clustered Tensor Factorization
NIPS 2009    Poster    Modeling Human Multiple Object Tracking with Rao-Blackwellized Particle Filtering
NIPS 2009    Poster    Help or Hinder: Bayesian Models of Social Goal Inference

Eric  P  Xing 
NIPS 2009    Poster    Time-Varying Dynamic Bayesian Networks
NIPS 2009    Poster    Sparsistent Learning of Varying-coefficient Models with Structural Changes
NIPS 2009    Poster    Heterogeneous multitask learning with joint sparsity constraints

Charles  Kemp 
NIPS 2009    Poster    Quantification and the language of thought
NIPS 2009    Poster    Individuation, Identification and Object Discovery
NIPS 2009    Poster    Bayesian Belief Polarization
NIPS 2009    Poster    Abstraction and Relational learning
NIPS 2009    Spotlight    Quantification and the language of thought
NIPS 2009    Spotlight    Abstraction and Relational learning

Rong  Jin 
NIPS 2009    Poster    Regularized Distance Metric Learning:Theory and Algorithm
NIPS 2009    Poster    Learning to Rank by Optimizing NDCG Measure
NIPS 2009    Poster    Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering
NIPS 2009    Poster    DUOL: A Double Updating Approach for Online Learning
NIPS 2009    Poster    Adaptive Regularization for Transductive Support Vector Machine
NIPS 2009    Spotlight    Learning to Rank by Optimizing NDCG Measure
NIPS 2009    Spotlight    Adaptive Regularization for Transductive Support Vector Machine

Antonio  Torralba 
NIPS 2009    Poster    Unsupervised Detection of Regions of Interest Using Iterative Link Analysis
NIPS 2009    Poster    Semi-Supervised Learning in Gigantic Image Collections
NIPS 2009    Poster    Nonparametric Bayesian Texture Learning and Synthesis
NIPS 2009    Oral    Semi-Supervised Learning in Gigantic Image Collections

 Zhihua  Zhang  Title    Professor
Institution    Zhejiang University
NIPS 2009    Poster    Probabilistic Relational PCA
NIPS 2009    Poster    Optimal Scoring for Unsupervised Learning
NIPS 2009    Spotlight    Probabilistic Relational PCA

国货当自强

# Accelerated Gradient Methods for Stochastic Optimization and Online Learning
C. Hu, J. Kwok, W. Pan
Zhejiang University; ; Hong Kong UST

# Sufficient Conditions for Agnostic Active Learnable
L. Wang
 Liwei  Wang  Title    Prof
Institution    Center for Information Sciences, Peking University
Homepage    http://www.cis.pku.edu.cn/faculty/vision/wangliwei/

# Ranking Measures and Loss Functions in Learning to Rank
Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhi-Ming Ma, Hang Li
Chinese Academy of Sciences; Microsoft Research Asia; ; ; Microsoft Research Asia

# Probabilistic Relational PCA
W. Li, D. Yeung, Z. Zhang
zju

# Optimizing Multi-class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification
W. Zheng, Z. Lin
 Wenming  Zheng  Title    Professor
Institution    Southeast University
Address    NanjingCHINA

# Optimal Scoring for Unsupervised Learning
g. dai, H. Zhao, Z. Zhang
Institution    zju

# Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering
L. Wu, R. Jin, S. Hoi, J. Zhu, N. Yu
Lei  Wu  Institution    Univ. of Sci. and Tech. of China
Homepage    http://wuleibig.googlepages.com

# Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming
X. WU, A. So, Z. Li, S. Li
Xiao-Ming WU, Anthony Man-Cho So, Zhenguo Li, Shuo-Yen Robert Li
The Chinese University of Hong Kong; The Chinese University of Hong Kong; The Chinese University of Hong Kong;

# Extending Phase Mechanism to Differentiate Motion Opponency for Motion Pop Out
y. meng, B. Shi
 yicong  meng  Institution    hkust
这孟一聪同学7月份我在北京开会时还和她好好聊过,聊得挺投机,恭喜她。

自己关注的一些带头大哥的文章:

# 3D Object Recognition with Deep Belief Nets
V. Nair, G. Hinton

# Analytic Hyper-Laplacian Priors for Fast Image Deconvolution
D. Krishnan, R. Fergus

# An Asymptotic Analysis of Smooth Regularizers
P. Liang, F. Bach, G. Bouchard, M. Jordan

# Application of convolutional RBMs for unsupervised feature learning in audio classification
H. Lee, P. Pham, Y. Largman, A. Ng

# Bayesian Belief Polarization
A. Jern, K. Chang, C. Kemp

# Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships
T. Malisiewicz, A. Efros

# Classifiers that Extrapolate
M. Palatucci, D. Pomerleau, G. Hinton, T. Mitchell

# Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process
C. Wang, D. Blei

# Discovering and Modeling Interactions Between Brain Regions with Hidden Conditional Random Fields
B. Yao, D. Walther, D. Beck, F. Li

# Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis
B. Chai, D. Walther, D. Beck, F. Li(菲菲姐姐的:)

这次NIPS国货里浙大中了好几篇,呼唤DELI师兄,戴老师重出江湖。。。我的打肿脸充胖子那篇以7+7+3=4的分数被拒,不过给3的reviewer的意见确实很在理,收获挺大。也许收录到NIPS都不一定都是好文章,但能收录也一定有它文章的可取之处,取其精华壮大自己,才是王道。等proceeding出来后认真研习研习。想起了DELI师兄的那句话:铁膀担道义,我辈当自强。虽然,人间正道是沧桑。

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: