ESTAが切れていたのであたふたと登録したり、スーツケースをレンタルしたり。
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
1日目、2日目はチュートリアル聞いてる感じなのかな。てか、チュートリアルが2日もあるってどういうことなの...(16時間聞いてるのかw)。
9:00 AM - 1:00 PM
- SA1: Machine Learning in Time Series Databases (and Everything Is a Time Series!), Eamonn Keogh
- SA2: Security Games, Chris Kiekintveld, Nicola Gatti, and Manish Jain
- SA3: Discourse Structure: Theory and Practice, Bonnie Webber, Markus Egg, and Valia Kordoni
- たぶん去年のACLのチュートリアルの内容と同じかな
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
- MA1: Collective Intelligence, Haym Hirsh
- MA2: Discourse Models for Generating Optimized User Interfaces: Theory from AI and Application in HCI, Hermann Kaindl
- MA3: From Structured Prediction to Inverse Reinforcement Learning, Hal Daume III
- MA4: Opinion Mining and Sentiment Analysis, Bing Liu
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
被ってて迷う...。
- 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)
- 6024: Nectar: Quantity Makes Quality: Learning with Partial Views, Nicolo Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
- 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
- 326: Abductive Markov Logic for Plan Recognition, Parag Singla, Raymond J. Mooney (pdf)
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
被りまくっている。。。
- 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
- 198: Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis, Makoto Yamada, Masashi Sugiyama
- 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
- 543: Semantic Relatedness Using Salient Semantic Analysis, Samer Hassan, Rada Mihalcea
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
ううう、重なってる。。。
- 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
- 912: Mean Field Inference in Dependency Networks: An Empirical Study, Daniel Lowd, Arash Shamaei (pdf)
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
- 474: A Nonparametric Bayesian Model of Multi-Level Category Learning, Kevin R. Canini, Thomas L. Griffiths (pdf)
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
- 749: Memory-Efficient Dynamic Programming for Learning Optimal Bayesian Networks, Brandon Malone, Changhe Yuan, Eric A. Hansen
- 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
自分の発表があるので張り付きます...。
- 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