どこのsession行こうか迷ったりするので、自分用メモ。書いて見たらすごい量があることに愕然としている(EMNLPのときは3並列くらいだったので)。Natural Language Processingというsessionがいくつもあるのが新鮮。

Sunday, August 7


9:00 AM - 1:00 PM
2:00 PM - 6:00 PM
  • SP1: Event Processing - State of the Art and Research Challenges, Opher Etzion and Yagil Engel
  • SP2: Human Computation: Core Research Questions and State of the Art, Luis von Ahn and Edith Law
  • SP3: Large-Scale Data Processing with MapReduce, Jimmy Lin
    • Data-Intensive Text Processing With MapReduceの人
  • SP4: Recognizing Behavior in a Spatio-Temporal Context, Hans W. Guesgen, Mehul Bhatt, and Stephen Marsland

Monday, August 8

9:00 AM - 1:00 PM
2:00 PM - 6:00 PM
  • MP1: Algorithms for Classical Planning, Jussi Rintanen
  • MP2: Conformal Predictions for Reliable Machine Learning: Theory and Applications, Vineeth N. Balasubramanian and Shen-Shyang Ho
  • MP3: Information Organization and Retrieval with Collaboratively Generated Content, Eugene Agichtein and Evgeniy Gabrilovich
  • MP4: Philosophy as AI and AI as Philosophy, Aaron Sloman

Tuesday, August 9

8:30 - 9:00 am
  • Grand Ballroom, Street Level AAAI-11/IAAI-11 Opening Ceremony
9:15 - 10:00 am
  • AAAI-11 25th Conference Anniversary Panel Moderator: Manuela Veloso, AAAI President-Elect (Carnegie Mellon University)
10:20 - 11:20 am
  • IAAI-11/AAAI-11 Joint Invited Talk: Building Watson: An Overview of DeepQA for the Jeopardy! Challenge David Ferrucci (IBM T J Watson Research Center)
11:30 am - 12:30 pm


  • Machine Learning 1
    • 6024: Nectar: Quantity Makes Quality: Learning with Partial Views, Nicolo Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
      • 聞きたい
    • 31: Symmetric Graph Regularized Constraint Propagation, Zhenyong Fu, Zhiwu Lu, Horace H. S. Ip, Yuxin Peng, Hongtao Lu
      • 聞きたい
    • 994: Improving Semi-Supervised Support Vector Machines through Unlabeled Instances Selection, Yu-Feng Li, Zhi-Hua Zhou (pdf)
  • Relational Probabilistic Models
    • 326: Abductive Markov Logic for Plan Recognition, Parag Singla, Raymond J. Mooney (pdf)
      • 聞きたい
    • 681: Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation, Marion Neumann, Babak Ahmadi, Kristian Kersting (pdf)
    • 305: Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models, Chloe Kiddon, Pedro Domingos (pdf)
      • 聞きたい
1:50 - 2:50 pm
  • Grand Ballroom, Street Level, AAAI-11 Invited Talk: From Turn-taking to Social Ties, Karrie Karahalios (University of Illinois)
3:00 - 4:00 pm


  • Classification 1
    • 799: Across-Model Collective Ensemble Classification, Hoda Eldardiry, Jennifer Neville
    • 385: Towards Maximizing the Area under the ROC Curve for Multi-Class, Classification Problems Ke Tang, Rui Wang, Tianshi Chen
    • 998: Adaptive Large Margin Training for Multilabel Classification, Yuhong Guo, Dale Schuurmans (pdf)
      • 聞きたい
  • Natural Language Processing 1
    • 151: WikiSimple: Automatic Simplification of Wikipedia Articles, Kristian Woodsend, Mirella Lapata
    • 237: Leveraging Wikipedia Characteristics for Search and Candidate Generation in Question Answering Jennifer Chu-Carroll, James Fan
    • 872: Grammatical Error Detection for Corrective Feedback Provision in Oral Conversations, Sungjin Lee, Hyungjong Noh, Kyusong Lee, Gary Geunbae Lee
  • Graphical Models
    • 1025: Pushing the Power of Stochastic Greedy Ordering Schemes for Inference in Graphical Models, Kalev Kask, Andrew Gelfand, Lars Otten, Rina Dechter (pdf)
    • 1029: Stopping Rules for Randomized Greedy Triangulation Schemes, Andrew E. Gelfand, Kalev Kask, Rina Dechter (pdf)
    • 6028: Nectar: Global Seismic Monitoring: A Bayesian Approach, Nimar S. Arora, Stuart Russell, Paul Kidwell, Erik Sudderth
      • 聞きたい
4:20 - 5:20 pm

Sparse Methodsのほうが聞きたいのが多いかもしれない。

  • Sparse Methods
    • 527: Sparse Matrix-Variate t Process Blockmodels, Zenglin Xu, Feng Yan, Yuan Qi
    • 148: Sparse Group Restricted Boltzmann Machines, Heng Luo, Ruimin Shen, Changyong Niu, Carsten Ullrich (pdf)
      • 聞きたい
    • 53: Efficiently Learning a Distance Metric for Large Margin Nearest Neighbor Classification, Kyoungup Park, Chunhua Shen, Zhihui Hao, Junae Kim
      • 聞きたい
  • Natural Language Processing 2
    • 6: Enhancing Semantic Role Labeling for Tweets Using Self-Training, Xiaohua Liu, Kuan Li, Ming Zhou, Zhongyang Xiong
    • 402: Learning to Interpret Natural Language Navigation Instructions from Observations, David L. Chen, Raymond J. Mooney
    • 3032: Analogical Dialogue Acts: Supporting Learning by Reading Analogies in Instructional Texts, David M. Barbella, Kenneth D. Forbus (pdf)
      • 聞きたい

Wednesday, August 10

9:00 - 10:00 am
  • AAAI-11 Invited Talk: Registration and Recognition for Robotics, Kurt Konolige (Willow Garage, Inc and Stanford University)
10:20 - 11:20 am
  • Learning Preferences and Social Recommendations
    • 256: Social Recommendation Using Low-Rank Semidefinite Program, Jianke Zhu, Hao Ma, Chun Chen, Jiajun Bu
    • 380: Collaborative Users’ Brand Preference Mining across Multiple Domains from Implicit Feedbacks, Jian Tang, Jun Yan, Lei Ji, Ming Zhang, Shaodan Guo, Ning Liu, Xianfang Wang, Zheng Chen
    • 491: Scaling Up Reinforcement Learning through Targeted Exploration, Timothy A. Mann, Yoonsuck Choe
  • Natural Language Processing 3
    • 488: Identifying Evaluative Sentences in Online Discussions, Zhongwu Zhai, Bing Liu, Lei Zhang, Hua Xu, Peifa Jia
    • 579: Partially Supervised Text Classification with Multi-Level Examples, Tao Liu, Xiaoyong Du, Minghui Li, Yongdong Xu, Xiaolong Wang (pdf)
      • 正例とラベルなしがいっぱいの状況で学習しましょう系のお話。最初にラベルなしをいくつかのクラスに分けてからやるようだ。ばっと読んだがびみょい...
    • 742: Exploiting Phase Transition in Latent Networks for Clustering, Vahed Qazvinian, Dragomir R. Radev (pdf)
      • 聞きたい
11:30 am - 12:30 pm
  • Density Ratio Estimation and Manifolds
    • 198: Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis, Makoto Yamada, Masashi Sugiyama
      • 聞きたい
    • 293: A Generalised Solution to the Out-of-Sample Extension Problem in Manifold Learning, Harry Strange, Reyer Zwiggelaar
    • 10: Ordinal Regression via Manifold Learning, Yang Liu, Yan Liu, Keith C. C. Chan
  • Natural Language Processing 4
    • 561: Tree Sequence Kernel for Natural Language, Jun Sun, Min Zhang, Chew Lim Tan
    • 632: A Simple and Effective Unsupervised Word Segmentation Approach, Songjian Chen, Yabo Xu, Huiyou Chang
    • 728: Lossy Conservative Update (LCU) Sketch: Succinct Approximate Count Storage, Amit Goyal, Hal Daume III
      • 聞きたい
1:50 - 2:50 pm
  • AAAI-11 Invited Talk: Strategic Intelligence in Social Networks, Michael Kearns (University of Pennsylvania)
3:00 - 4:00 pm
  • Natural Language Processing 5
    • 543: Semantic Relatedness Using Salient Semantic Analysis, Samer Hassan, Rada Mihalcea
      • 聞きたい
    • 906: Using Semantic Cues to Learn Syntax, Tahira Naseem, Regina Barzilay (pdf)
      • 聞きたい。semanticなものをimproveするためにsyntacticな情報(htmlのmarkupとか)を使うことはよくあるが、その逆方向をやる。生成モデル
    • 738: Integrating Clustering and Multi-Document Summarization by Bi-Mixture Probabilistic Latent Semantic Analysis (PLSA) with Sentence Bases, Chao Shen, Tao Li, Chris H. Q. Ding
4:20 - 5:20 pm
  • Reinforcement Learning 1
    • 496: Tracking User-Preference Varying Speed in Collaborative Filtering, Ruijiang Li, Bin Li, Cheng Jin, Xiangyang Xue, Xingquan Zhu
    • 962: An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems, Byron Boots, Geoffrey J. Gordon (pdf)
      • 聞きたい
    • 368: Non-Parametric Approximate Linear Programming for MDPs, Jason Pazis, Ronald Parr (pdf)
  • Search Engines & Question Answering
    • 4035: AIW: A Whole Page Click Model to Better Interpret Search Engine Click Data, Weizhu Chen, Zhanglong Ji, Si Shen, Qiang Yang
    • 4140: AIW: Artificial Intelligence for Artificial Artificial Intelligence, Peng Dai, Mausam, Daniel S. Weld
    • 4066: AIW: Fast Query Recommendation by Search, Qixia Jiang, Maosong Sun
6:30 - 9:30 pm
  • AAAI-11 Poster Session Reception
    • 自分もポスターやります

Thursday, August 11

9:00 - 10:00 am
  • AAAI-11 Invited Talk: Towards Artificial Systems: What Can We Learn from Human Perception? Heinrich H. Buelthoff (Max Planck Institute for Biological Cybernetics)
10:20 - 11:20 am
  • Reinforcement Learning 2
    • 455: Differential Eligibility Vectors for Advantage Updating and Gradient Methods, Francisco S. Melo
    • 635: Basis Function Discovery Using Spectral Clustering and Bisimulation Metrics, Gheorghe Comanici, Doina Precup
    • 41: Value Function Approximation in Reinforcement Learning Using the Fourier Basis, George Konidaris, Sarah Osentoski, Philip Thomas
11:30 am - 12:30 pm


  • Machine Learning 2
    • 912: Mean Field Inference in Dependency Networks: An Empirical Study, Daniel Lowd, Arash Shamaei (pdf)
      • 聞きたい。Bayesian networkは効率的に学習できるけどcycleを避けないといけない、Markov networkみたいなのはよりflexibilityがあるけど計算大変というのの間をいける方法としてDependency Networkというのがあるらしい。それの推論がいままでGibbsしかなかったので、平均場近似でできる方法を提案。高速にもっといいところにいけるというのを実験的に示した
    • 172: Efficient Subspace Segmentation via Quadratic Programming, Shusen Wang, Xiaotong Yuan, Tiansheng Yao, Shuicheng Yan, Jialie Shen
    • 433: Automatic Group Sparse Coding, Fei Wang, Noah Lee, Jimeng Sun, Jianying Hu, Shahram Ebadollahi
      • 聞きたい
  • Transfer Learning
    • 951: Selective Transfer between Learning Tasks Using Task-Based Boosting, Eric Eaton, Marie desJardins (pdf)
      • 聞きたい
    • 924: Transfer Learning by Structural Analogy, Huayan Wang, Qiang Yang (pdf)
      • 聞きたい
    • 195: Heterogeneous Transfer Learning with RBMs, Bin Wei, Christopher Pal
1:50 - 2:50 pm
  • Mult-Task Learning
    • 108: Multi-Task Learning in Square Integrable Space, Wei Wu, Hang Li, Yunhua Hu, Rong Jin
    • 241: Multi-Task Learning in Heterogeneous Feature Spaces, Yu Zhang, Dit-Yan Yeung
      • 聞きたい
    • 142: Learning Structured Embeddings of Knowledge Bases, Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio (pdf)
      • 聞きたい
  • Classification 2
    • 474: A Nonparametric Bayesian Model of Multi-Level Category Learning, Kevin R. Canini, Thomas L. Griffiths (pdf)
      • 聞きたい
    • 805: Convex Sparse Coding, Subspace Learning, and Semi-Supervised Extensions, Xinhua Zhang, Yaoliang Yu, Martha White, Ruitong Huang, Dale Schuurmans
      • 聞きたい
    • 33: Learning Instance Specific Distance for Multi-Instance Classification, Hua Wang, Feiping Nie, Heng Huang
      • 聞きたい
3:00 - 4:00 pm
  • Machine Learning 3
    • 913: Optimal Rewards versus Leaf-Evaluation Heuristics in Planning Agents, Jonathan Sorg, Satinder Singh, Richard L. Lewis
    • 6027: Nectar: End-User Feature Labeling via Locally Weighted Logistic Regression, Weng-Keen Wong, Ian Oberst, Shubhomoy Das, Travis Moore, Simone Stumpf, Kevin McIntosh, Margaret Burnett
    • 533: Fast Newton-CG Method for Batch Learning of Conditional Random Fields, Yuta Tsuboi, Yuya Unno, Hisashi Kashima, Naoaki Okazaki
  • Clustering 1
    • 145: Large Scale Spectral Clustering with Landmark-Based Representation, Xinlei Chen, Deng Cai
    • 481: Localized K-Flats, Yong Wang, Yuan Jiang, Yi Wu, Zhi-Hua Zhou
    • 82: Learning a Kernel for Multi-Task Clustering, Quanquan Gu, Zhenhui Li, Jiawei Han
      • 聞きたい
  • Reasoning under Uncertainty 1
    • 749: Memory-Efficient Dynamic Programming for Learning Optimal Bayesian Networks, Brandon Malone, Changhe Yuan, Eric A. Hansen
      • 聞きたい
    • 22: Dual Decomposition for Marginal Inference, Justin Domke
      • 聞きたい
    • 101: Efficient Methods for Lifted Inference with Aggregate Factors, Jaesik Choi, Rodrigo de Salvo Braz, Hung H. Bui
  • Clustering 2
    • 363: Nonnegative Spectral Clustering with Discriminative Regularization, Yi Yang, Heng Tao Shen, Feiping Nie, Rongrong Ji, Xiaofang Zhou
    • 791: Transfer Latent Semantic Learning: Microblog Mining with Less Supervision, Dan Zhang, Yan Liu, Richard D. Lawrence, Vijil Chenthamarakshan
    • 794: Linear Discriminant Analysis: New Formulations and Overfit Analysis, Dijun Luo, Chris Ding, Heng Huang
4:20 - 5:20 pm


  • Ranking
    • 4007: AIW: CCRank: Parallel Learning to Rank with Cooperative Coevolution, Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady W. Lauw
    • 4135: AIW: Maximum Entropy Context Models for Ranking Biographical Answers to Open- Domain Definition Questions Alejandro Figueroa, John Atkinson
    • 4137: AIW: Transfer Learning for Multiple-Domain Sentiment Analysis — Identifying Domain Dependent/Independent Word Polarity Yasuhisa Yoshida, Tsutomu Hirao, Tomoharu Iwata, Masaaki Nagata, Yuji Matsumoto