UCR CS 260: SEMINAR IN COMPUTER SCIENCE

UCR CS 260: SEMINAR IN COMPUTER SCIENCE 9:40 a.m. - 11:00 a.m. CHUNG 139 Winston Chung Hall (CHUNG) Eamonn KeoghComputer Science & Engineering Departmenteamonn(AT)cs.ucr.edu

Format of the Class • Very informal • We will take turns to present and discuss papers • You will be graded on your presentation, and on your participation

Presenting a paper • You can request the original slides from the author (for a conference paper). • Write a very polite email to first author, but CC all authors. • You need to do this well in advance. • You need to augment the slides, and make it clear what are your words/analysis… • Plan to speak for 25 min (not counting questions/discussion)

Prepend anything we need to know before you start discussing the paper. At a minimum, where/when the paper was published and its impact Us a different font color to make it clear these are your words Append a discussion of the paper Us a different font color to make it clear these are your words

Your discussion of the paper • Is the paper • Original? (including original compared to authors previous work) • Well written? • Well motivated? • Well illustrated? • Are the experiments • Forceful • Fair to rival methods • Reproducible • Does the algorithm require many parameters, if so • Is it explained how to set them? • Are we shown how sensitive the results are to them?

Your discussion of the paper • What are the limitations of the work? Are they clearly stated? • What are the assumptions of the work? Are they clearly stated? • Is there a good literature review? If not, what is missing? • Do you think a simpler method might have worked as well as the proposed method?

Your discussion of the paper • Did the paper garner interest? • Consider “Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. ICDM 2005: 226-233” It has 276 citations It was expanded into a journal paper (which has 54 citations)

Your discussion of the paper • Did the paper garner interest? It is the most cited paper, of that conference in that year

You need to understand the paper • I know you did not write the paper, and you are not responsible for the content. • However you cannot simply show someone else slides and shrug you shoulders if asked questions. • You should try to anticipate any questions that an audience member might ask, and research an answer. • So, to present a paper, you might have to read 3 or 5 other papers…. http://videolectures.net/

You need to understand the paper • Hint: many papers have videos of their presentation online. Often you can hear the audience questions • http://videolectures.net/

Lier aide d'interprétation II Sur cette figure, la couleur des flèches à l'intérieur de la liaison de poisson pour les couleurs des flèches de la série chronologique. Cela nous dit exactement comment nous allons partir d'une forme à une série de temps Figure 4: I) A l'intuition visuelle de la conversion d'une forme bidimensionnelle à une "série temporelle" unidimensionnelle. Ii) deux formes qui sont similaires à l’ espace forme sera également similaire dans la forme de séries chronologiques. III) Ici, nouscomparer un poisson (Priacanthus arenatus) à partir d'un Cubain de 40 ans manuscrit à un poisson connexe (Priacanthus de hamrur) à partir d'un manuscrit publié en 1899. Minimalisme aide: Dans ce cas, les numéros sur l'axe X ne veut rien dire, ils sont supprimés

Linking helps interpretability II In this figure, the color of the arrows inside the fish link to the colors of the arrows on the time series. This tells us exactly how we go from a shape to a time series. Note that there are other links, for example in II, you can tell which fish is which based on color or link thickness linking. Minimalism helps: In this case, numbers on the X-axis do not mean anything, so they are deleted.

Where to find Papers? • I have given some suggestions on the next page. • You can also find papers by.. • Looking through the proceeding of major conferences, SIGKDD, ICDM, SDM, ICDE, NIPS, VLDB, SIGMOD, ICML • Topic driven (outside of data mining): If you are interested in astronomy or insect (or anything) there is probably a data mining paper on it. • Data mining Topic driven: I would like to learn about DM for/with social networks, active learning, anytime algorithms, privacy preservation, mobile devices, bloom filters, personalization, deep learning, the reasonable effectiveness of data, fraud detection, anti-terrorism, advertising • Person Driven: You know of an interesting researcher • I want to see interesting papers, sometimes bad papers can be interesting ;-) • Please run your idea by me first

Suggested Papers Toward an Architecture for Never-Ending Language Learning.A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E.R. Hruschka Jr. and T.M. Mitchell. In Proceedings of the Conference on Artificial Intelligence (AAAI), 2010 "Predicting Human Brain Activity Associated with the Meanings of Nouns," T. M. Mitchell, S. V. Shinkareva, A. Carlson, K.M. Chang, V. L. Malave, R. A. Mason, and M. A. Just, Science, 320, 1191, May 30, 2008. DOI: 10.1126/science.1152876. Fei Xu, Ravi Jampani, Mingxi Wu, Chris Jermaine, Tamer Kahveci: Surrogate ranking for very expensive similarity queries. ICDE 2010: 848-859 Mingxi Wu, Chris Jermaine: Guessing the extreme values in a data set: a Bayesian method and its applications. VLDB J. 18(2): 571-597 (2009) Jayendra Venkateswaran, Tamer Kahveci, Christopher M. Jermaine, Deepak Lachwani: Reference-based indexing for metric spaces with costly distance measures. VLDB J. 17(5): 1231-1251 (2008) Pedro Domingos: The Role of Occam's Razor in Knowledge Discovery. Data Min. Knowl. Discov. 3(4): 409-425 (1999) Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. Sanghai, Deepak Verma: Adversarial classification. KDD 2004: 99-108 Smriti Bhagat, Amit Goyal, Laks V. S. Lakshmanan, Maximizing Product Adoption in Social Networks. In WSDM 2012. Text Classification from Labeled and Unlabeled Documents using EM, K. Nigam, A. McCallum, S. Thrun, T. Mitchell, Machine Learning, 39, 103-134, 2000. Marie desJardins, Eric Eaton, Kiri Wagstaff: Learning user preferences for sets of objects.ICML 2006: 273-280 Clustering with Instance-level Constraints Kiri Wagstaff and Claire Cardie. Proceedings of the International Conference on Machine Learning (ICML), p. 1103-1110, 2000. Juha Reunanen: Overfitting in Making Comparisons Between Variable Selection Methods.Journal of Machine Learning Research 3: 1371-1382 (2003)

Sign up sheet • Will be on my door. • You can ONLY sign up in numeric order. • Just sign up your name, and the first five words of the paper title

UCR CS 260: SEMINAR IN COMPUTER SCIENCE