# AISTATS 2015: a few talks from one day

I attended AISTATS for about a day and change this year — unfortunately due to teaching I missed the poster I had there but Shuang Song presented a work on learning from data sources of different quality, which her work with Kamalika Chaudhuri and myself. This was my first AISTATS. It had single track of oral presentations and then poster sessions for the remaining papers. The difficulty with a single track for me is that my interest in the topics is relatively focused, and the format of a general audience with a specialist subject matter meant that I couldn’t get as much out of the talks as I would have wanted. Regardless, I did get exposed to a number of new problems. Maybe the ideas can percolate for a while and inform something in the future.

Computational Complexity of Linear Large Margin Classification With Ramp Loss
Søren Frejstrup Maibing, Christian Igel
The main result of this paper (I think) is that ERM under ramp loss is NP-hard. They gave the details of the reduction but since I’m not a complexity theorist I got a bit lost in the weeds here.

A la Carte — Learning Fast Kernels
Zichao Yang, Andrew Wilson, Alex Smola, Le Song
Ideas like “random kitchen sinks” and other kernel approximation methods require you to have a kernel you want to approximate, but in many problems you in fact need to learn the kernel from the data. If I give you a kernel function $k(x,x') = k( |x - x'| )$, then you can take the Fourier transform $K(\omega)$ of $k$. This turns out to be a probability distribution, so you can sample random $\{\omega_i\}$ i.i.d. and build a randomized Fourier approximation of $k$. If you don’t know the kernel function, or you have to learn it, then you could instead try to learn/estimate the transform directly. This paper was about trying to do that in a reasonably efficient way.

Learning Where to Sample in Structured Prediction
Tianlin Shi, Jacob Steinhardt, Percy Liang
This was about doing Gibbs sampling, not for MCMC sampling from the stationary distribution, but for “stochastic search” or optimization problems. The intuition was that some coordinates are “easier” than others, so we might want to focus resampling on the harder coordinates. But this might lead to inaccurate sampling. The aim here twas to build a heterogenous sampler that is cheap to compute and still does the right thing.

Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
Mario Lucic, Mesrob Ohannessian, Amin Karbasi, Andreas Krause
This paper won the best student paper award. They looked at a k-means problem where they do “data summarization” to make the problem a bit more efficient — that is, by learning over an approximation/summary of the features, they can find different tradeoffs between the running time, risk, and sample size for learning problems. The idea is to use coresets — I’d recommend reading the paper to get a better sense of what is going on. It’s on my summer reading list.

Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions
Alexandre Defossez, Francis Bach
What if you want to do SGD but you don’t want to sample the points uniformly? You’ll get a bias-variance tradeoff. This is another one of those “you have to read the paper” presentations. A nice result if you know the background literature, but if you are not a stochastic gradient aficionado, you might be totally lost.

Sparsistency of $\ell_1$-Regularized M-Estimators
Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar, Volkan Cevher
In this paper they find a new condition, which they call local structured smoothness, which is sufficient for certain M-estimators to be “sparsistent” — that is, they recover the support pattern of a sparse parameter asymptotically as the number of data points goes to infinity. Examples include the LASSO, regression in general linear models, and graphical model selection.

Some of the other talks which were interesting but for which my notes were insufficient:

• Two-stage sampled learning theory on distributions (Zoltan Szabo, Arthur Gretton, Barnabas Poczos, Bharath Sriperumbudur)
• Generalized Linear Models for Aggregated Data (Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo)
• Efficient Estimation of Mutual Information for Strongly Dependent Variables (Shuyang Gao, Greg Ver Steeg, Aram Galstyan)
• Sparse Submodular Probabilistic PCA (Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, Oluwasanmi Koyejo)

# 2015 North American School of Information Theory

The 2015 ​North American ​School of Information Theory ​(NASIT) will be held on August 10-13, 2015, at the University of California, San Diego in La Jolla. If you or your colleagues have students who might be interested in this event, we would be grateful if you could forward this email to them and encourage their participation. The application deadline is ​Sunday, June 7. As in the past schools, we again have a great set of lecturers this year​​:

We are pleased to announce that ​Paul Siegel will be the​​ Padovani Lecturer of the IEEE Information Theory Society​​ and will give his lecture at the School. The Padovani Lecture is sponsored by a generous gift of Roberto Padovani.

# Tracks: Frequenties and Bayesity

1. Ashkaraballi (Nancy Ajram)
2. Restless Leg (Har Mar Superstar)
3. Let The Good Times Roll (JD McPherson)
4. The Theme From Dangeresque II (Strong Bad)
5. Wild Stallion Mountain (The Bombay Royale)
6. Reel It In (Nikhil P. Yerawadekar & Low Mentality)
7. Central Park Blues (Ultimate Painting)
8. Piazza, New York Catcher (Belle and Sebastian)
9. Golden Slippers (The Prince Myshkins)
10. Nowadays (Del [the Funky Homosapien])
11. Coney Island Baby (Tom Waits)
12. J’veux D’la Musique (Tout Le Temps) (Les Nubians)
13. Howl (JD Brooks & The Uptown Sound)
14. I Believe in a Thing Called Love (The Darkness)
15. Last Month Of The Year (Fairfield Four)
16. A un niño llorando (Schola Antique of Chicago)
17. Rock & Roll Is Cold (Matthew E. White)
18. Try A Little Tenderness

I had a rough semester this Spring, but I did manage to read some books, mostly thanks to an over-aggressive travel schedule.

Dead Ringers: How Outsourcing is Changing How Indians Understand Themselves (Shehzad Nadeem). Published a few years ago, this book is a study of how two kinds of outsourcing — business process (BPO) and information processing outsourcing (IPO) — have changed attitudes of Indians towards work in a globalized economy. Nadeem first lays out the context for outsourcing and tries to dig behind the numbers to see where and to whom the benefits are going. The concept of time arbitrage was a new way of thinking about the 24-hour work cycle that outsourcing enables — this results in a slew of deleterious health effects for workers as well as knock-on effects for family structures and the social fabric. This sets the stage for a discussion of whether or not outsourcing has really brought a different “corporate culture” to India (a topic on which I have heard a lot from friends/relatives). The book brings a critical perspective that complicates the simplified “cyber-coolies” versus “global agents” discussion that we often hear.

Cowboy Feng’s Space Bar and Grille (Steven Brust). Mind-candy, a somewhat slight novel that was a birthday gift back in high school. Science fiction of a certain era, and with a certain lightness.

Hawk (Steven Brust). Part n in a series, also mind-candy at this point. If you haven’t read the whole series up to this point, there’s little use in starting here.

Saga Volumes I-IV (Brian K. Vaughan / Fiona Staples). This series was recommended by several people and since I hadn’t read a graphic novel in a while I figured I’d pick it up. Definitely an interesting world, angels vs. demons in space with androids who have TV heads thrown in for good measure, it’s got a sort of visual freedom that text-based fiction can’t really match up to. Why not have a king with a giant HDTV for a head? Makes total sense to me, if that’s the visual world you live in. Unfortunately, the series is at a cliff-hanger so I have to wait for more issues to come out.

This Earth of Mankind (Pramoedya Ananta Toer): A coming-of-age story set in 1898 Indonesia, which is a place and time about which I knew almost nothing. Toer orally dictated a quartet of novels while imprisoned in Indonesia, of which this is the first. The mélange of ideas around colonialism, independence, cultural stratification in Java, and the benefits and perils of “Western education” echo things I know from reading about India, but are very particular to Indonesia. In particular, the bupati system and relative decentralization of Dutch authority in Indonesia created complex social hierarchies that are hard to understand. The book follows Minke, the only Native (full Javanese) to attend his Dutch-medium school, and his relationship with Annelise, the Indo (half-Native, half Dutch) daughter of a Dutch businessman and his concubine Nyai Ontosoroh. Despite their education and accomplishments, Minke and Nyai Ontosoroh are quite powerless in the face of the racist hierarchies of Dutch law that do not allow Natives a voice. This novel sets the stage for the rest of the quartet, which I am quite looking forward to reading.

The Bone Clocks (David Mitchell): The latest novel from David Mitchell is not as chronologically sprawling as Cloud Atlas. I don’t want to give too much away, but there is an epic behind-the-scenes struggle going on, some sort of mystic cult stuff, and a whole lot of “coincidences” that Mitchell is so good at sprinkling throughout his book. There are also some nice references to his other books, including Black Swan Green and The Thousand Autumns of Jacob de Zoet. I liked the latter novel better than this one, despite its gruesomeness, because it felt a bit more grounded. I think fans of Mitchell’s work will like the Bone Clocks, but of his novels, I don’t think I would recommend starting with this one.

Like many, I was shocked to hear of Prashant Bhargava’s death. I just saw Radhe Radhe with Vijay Iyer’s live score at BAM, and Bhargava was there. I met him once, through Mimosa Shah.

Most people know Yoko Ono as “the person who broke up the Beatles” and think of her art practice as a joke. She’s a much more serious artist than that, and this article tries to lay it out a bit better.

Via Celeste LeCompte, a tool to explore MIT’s research finances. It’s still a work-in-progress. I wonder how hard it would be to make such a thing for Rutgers.

In lieu of taking this course offered by Amardeep Singh, I could at least read the books on the syllabus I guess.

Muscae volitantes, or floaty things in your eyes.

# Re-identification from microbiomes

A (now not-so-recent) paper by Homer et al. made a splash by showing that one could take a DNA sample from a person and detect whether they were part of the Human Genome Project (HGP) based on looking at the SNP variations from that individual together with the reported allele variations in the HGP data. More recently, a paper in PNAS by Franzosa et al. showed reidentification of individuals in the Human Microbiome Project.

Color me unsurprised. Given the richness of the data, from a purely informational point of view it seems pretty clear that people should be identifiable. As with many machine learning problems, however, the secret is in the feature encoding. Many approaches to comparing metagenomes, especially for bacterial ecologies, try to assess the variability in the population of bacteria, perhaps through mapping the to known strains. As mentioned in the Methods section, “reads were additionally mapped to a database of 649 microbial reference genomes using the Burrows-Wheeler aligner.” However, in addition to these mapping statistics, they used a few other more complicated features to help gain some additional robustness in their identification procedure.

Somehow being able to be identified by your microbiome seems less scary than being able to be identified by your genome, perhaps because we have a sense that genes are more “determining” than microbiomes. After all, you could get a fecal transplant and change your gut flora significantly. Is it the same as burning off your fingerprints? Probably not. But perhaps in the future, perpetrators of certain campus shenanigans may be easier to catch.