From schneide at cs.cmu.edu Tue Apr 3 11:08:23 2012 From: schneide at cs.cmu.edu (Jeff Schneider) Date: Tue, 03 Apr 2012 11:08:23 -0400 Subject: [Research] Liang's thesis proposal today at 1pm!! Message-ID: <4F7B1267.8040100@cs.cmu.edu> Hi Everyone, Please come to hear the next Auton Lab thesis proposal by Liang Xiong at 1pm. It will be in GHC 6501. The title is "On Learning from Collective Data". Hope to see you there! Jeff. From schneide at cs.cmu.edu Mon Apr 16 16:57:40 2012 From: schneide at cs.cmu.edu (Jeff Schneider) Date: Mon, 16 Apr 2012 16:57:40 -0400 Subject: [Research] AISTATS practice poster session tomorrow Message-ID: <4F8C87C4.4060709@cs.cmu.edu> Hi Everyone! We will have an AutonLab meeting tomorrow at 4pm where Barnabas and Yi will present their posters for AISTATS. We are still searching for a room and I'll send that as soon as we have one. We'll also order some pizza so you have an opportunity to have an early dinner or a late lunch. Jeff. From schneide at cs.cmu.edu Tue Apr 17 15:35:11 2012 From: schneide at cs.cmu.edu (Jeff Schneider) Date: Tue, 17 Apr 2012 15:35:11 -0400 Subject: [Research] AISTATS practice poster session tomorrow In-Reply-To: <4F8C87C4.4060709@cs.cmu.edu> References: <4F8C87C4.4060709@cs.cmu.edu> Message-ID: <4F8DC5EF.1050409@cs.cmu.edu> Hi guys, Let's meet in my office. We'll do our practice poster session here in the "Auton Hallway". Jeff. On 04/16/2012 04:57 PM, Jeff Schneider wrote: > Hi Everyone! > > We will have an AutonLab meeting tomorrow at 4pm where Barnabas and Yi will > present their posters for AISTATS. We are still searching for a room and I'll > send that as soon as we have one. We'll also order some pizza so you have an > opportunity to have an early dinner or a late lunch. > > Jeff. > _______________________________________________ > Research mailing list > Research at autonlab.org > https://www.autonlab.org/mailman/listinfo/research From schneide at cs.cmu.edu Mon Apr 30 09:06:59 2012 From: schneide at cs.cmu.edu (Jeff Schneider) Date: Mon, 30 Apr 2012 09:06:59 -0400 Subject: [Research] Reminder - Thesis Defense - Yi Zhang - 4/30/12 - Learning with Limited Supervision by Input and Output Coding Message-ID: <4F9E8E73.4060000@cs.cmu.edu> Hi Everyone! Today Yi will do his thesis defense! Please come to his talk and support the next Auton Lab PhD! Jeff. -------- Original Message -------- Subject: Reminder - Thesis Defense - Yi Zhang - 4/30/12 - Learning with Limited Supervision by Input and Output Coding Date: Fri, 27 Apr 2012 10:36:22 -0400 From: Diane Stidle To: ml-seminar at cs.cmu.edu, Jerry Zhu Thesis Defense.. Date: April 30th (Monday) Time: 1:00pm Place: 6501 GHC PhD Candidate: Yi Zhang Title: Learning with Limited Supervision by Input and Output Coding Abstract: In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high. Even with a relatively large number of examples, the learning problem may still suffer from limited supervision as the complexity of the prediction function increases. As a result, learning with limited supervision presents a major challenge to machine learning. With the goal of supervision reduction, this thesis studies the representation, discovery and incorporation of extra input and output information in learning. Information about the input space can be encoded by regularization. We first design a regularization method for text classification that encodes the correlation of words inferred from seemingly irrelevant unlabeled text. We then propose a matrix-normal penalty for multi-task learning, which compactly encodes the covariance structure of the joint input space of multiple tasks. To capture structure information that is more general than covariance and correlation, we study a class of regularization penalties on model compressibility. Then we design the projection penalty, which can encode the input information highlighted by a dimension reduction while controlling the risk of information loss during the reduction. Information about the output space can be exploited by error correcting output codes. Inspired by composite likelihoods, we propose an improved pairwise coding for multi-label classification. We then investigate problem-dependent coding schemes, where the encoding is learned from data instead of being predefined. We first propose a multi-label output code using canonical correlation analysis, where predictability of the code is optimized. We then argue that both discriminability and predictability are critical for multi-label output codes, and propose a max-margin formulation that promotes both discriminative and predictable codes. We empirically study our methods in a wide spectrum of applications, including text categorization, landmine detection, face recognition, brain signal classification, handwritten digit recognition, price forecasting, music emotion prediction, medical decision, email analysis, gene function classification and outdoor scene recognition. Under limited supervision, our proposed methods for encoding input and output information lead to significantly improved prediction performance. Thesis Committee: Jeff Schneider, Chair Geoff Gordon Tom Mitchell Xiaojin (Jerry) Zhu, University of Wisconsin-Madison Link to the draft document: http://www.cs.cmu.edu/~yizhang1/docs/thesis_draft.pdf -- ******************************************************************* Diane Stidle Business & Graduate Programs Manager Machine Learning Department School of Computer Science 8203 Gates Hillman Complex Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213-3891 Phone: 412-268-1299 Fax: 412-268-3431 Email: diane at cs.cmu.edu URL:http://www.ml.cmu.edu -------------- next part -------------- A non-text attachment was scrubbed... Name: Thesis Defense Poster-zhang.pdf Type: application/pdf Size: 1019704 bytes Desc: not available URL: