Learning Similarity Metrics
Kursbeschreibung
| Studiengang | Modulkürzel | Leistungs- bewertung |
|---|---|---|
| BA-2010 | AS-CL | 8 LP |
| NBA | AS-CL | 8 LP |
| Master | SS-CL, SS-TAC | 8 LP |
| Magister | - | - |
| Dozenten/-innen | Stefan Riezler, Artem Sokolov |
| Veranstaltungsart | Hauptseminar |
| Erster Termin | 23.04.2013 |
| Zeit und Ort | Di, 11:15–12:45, INF 327 / SR 2 (SR) |
| Commitment-Frist | 20.05.–13.07.2013 |
Teilnahmevoraussetzungen
Foundations of Probability Theory und Statistics.
Leistungsnachweis
Regular and active participation, oral presentation.
Sprache
English
Inhalt
Similarity metrics are ubiquitous in machine learning and build the algorithmic foundation of numerous natural language processing applications, including information retrieval, machine translation, or paraphrasing. The goal of this seminar is to learn statistical similarity metrics from data instead of applying common similarity measures borrowed from, for example, linear algebra. For example, the emphasis is on the difference between applying a kernel and learning to adapt a kernel to the data; instead of applying cosine similarity to tfidf vectors we could rather learn the importance weights for tfidf entries, or replace standard singular value decomposition of term-document matrices with an optimal matrix factorization given a specific ranking problem.
Specific topics include:
- Locality sensitive hashing
- Distance metric learning
- Kernel alignment
and others
Kursübersicht
Seminarplan
| Datum | Referent | Material | Thema |
| 23.4. | Artem Sokolov, Stefan Riezler | Overview slides | Introduction |
| 30.4. | Schigehiko Schamoni | Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Olivier Chapelle, and Kilian Weinberger. 2010. Learning to rank with (a lot of) word features. Information Retrieval 13, 3 | Applications to NLP |
| 7.5. | Christoph Mayer | Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola: On kernel-target alignment. NIPS 2001: 367-373 | Learning Kernels and Features |
| 14.5. (N.B. moved to 23.7.) | Lyubov Nakryyko | Koby Crammer, Joseph Keshet, Yoram Singer: Kernel Design using Boosting. NIPS 2002: 537-544 | |
| 21.5. | Michael Haas | Brian Kulis & Kristen Grauman. Kernelized Locality-Sensitive Hashing. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 34, no. 6, pp. 1092--1104, 2012 | Learning to Hash |
| 28.5. (N.B. moved to 25.6.) | Angela Schneider | Ruslan Salakhutdinov, Geoffrey Hinton. Semantic Hashing. International Journal of Approximate Reasoning 50 (2009) 969–978 | |
| 4.6. | Sariya Karimova | Yair Weiss, Antonio Torralba, Robert Fergus. Spectral Hashing. NIPS, pp 1753-1760. 2008 | |
| 11.6. cancelled | Danny Rehl | Killian Weinberger and Lawrence K. Saul, Distance Metric Learning for Large Margin Nearest Neighbor Classification, Journal of Machine Learning Research, 10(Feb):207--244, 2009 | Metric Learning |
| 18.6. | Carolin Haas | Prateek Jain, Brian Kulis, Inderjit Dhillon & Kristen Grauman. Online Metric Learning and Fast Similarity Search. NIPS, 2008 | |
| 25.6. | Angela Schneider | Ruslan Salakhutdinov, Geoffrey Hinton. Semantic Hashing. International Journal of Approximate Reasoning 50 (2009) 969–978 | Learning to Hash |
| 2.7. | Mareike Hartmann | Chunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel. "Positive Semidefinite Metric Learning Using Boosting-like Algorithms". Journal of Machine Learning Research, 13(Apr):1007−1036, 2012 | Metric Learning |
| 9.7. | Bartosz Bogacz | Maria-Florina Balcan, Avrim Blum, Santosh Vempala: Kernels as features: On kernels, margins, and low-dimensional mappings. Machine Learning 65(1): 79-94 (2006) | Learning Kernels and Features |
| 16.7. | Benjamin Heinzerling | Zhuoran Wang and John Shawe-Taylor. 2010. A kernel regression framework for SMT. Machine Translation 24, 2 (June 2010), 87-102 | Applications to NLP |
| 23.7. | Lyubov Nakryyko | Koby Crammer, Joseph Keshet, Yoram Singer: Kernel Design using Boosting. NIPS 2002: 537-544 | Learning Kernels and Features |
