Better late than never, I suppose. A few weeks ago I escaped the cold of New Jersey to my old haunts of San Diego. Although La Jolla was always a bit fancy for my taste, it’s hard to beat a conference which boasts views like this:

I’ll just recap a few of the talks that I remember from my notes — I didn’t really take notes during the plenaries so I don’t have much to say about them. Mostly this was due to laziness, but finding the time to blog has been challenging in this last year, so I think I have to pick my battles. Here’s a smattering consisting of

*(Information theory)*

**Emina Soljanin** talked about designing codes that are good for fast access to the data in distributed storage. Initial work focused on how to repair codes under disk failures. She looked at how easy it is to retrieve the information afterwords to guarantee some QoS for the storage system. **Adam Kalai** talked about designing compression schemes that work for an “audience” of decoders. The decoders have different priors on the set of elements/messages so the idea is to design an encoder that works for this ensemble of decoders. I kind of missed the first part of the talk so I wasn’t quite sure how this relates to classical work in mismatched decoding as done in the information theory world. **Gireeja Ranade** gave a great talk about defining notions of capacity/rate need to control a system which as multiplicative uncertainty. That is, where has the uncertainty. She gave a couple of different notions of capacity, relating to the ratio — either the expected value of the square or the log, appropriately normalized. She used a “deterministic model” to give an explanation of how control in this setting is kind of like controlling the number of significant bits in the state: uncertainty increases this and you need a certain “amount” of control to cancel that growth.

*(Learning and statistics)*

I learned about active regression approaches from **Sivan Sabato** that provably work better than passive learning. The idea there is do to use a partition of the X space and then do piecewise constant approximations to a weight function that they use in a rejection sampler. The rejection sampler (which I thought of as sort of doing importance sampling to make sure they cover the space) helps limit the number of labels requested by the algorithm. Somehow I had never met **Raj Rao Nadakuditi** until now, and I wish I had gotten a chance to talk to him further. He gave a nice talk on robust PCA, and in particular how outliers “break” regular PCA. He proposed a combination of shrinkage and truncation to help make PCA a bit more stable/robust. **Laura Balzano** talked about “estimating subspace projections from incomplete data.” She proposed an iterative algorithm for doing estimation on the Grassmann manifold that can do subspace tracking. **Constantine Caramanis** talked about a convex formulation for mixed regression that gives a guaranteed solution, along with minimax sample complexity bounds showing that it is basically optimal. **Yingbin Liang** talked about testing approaches for understanding if there is an “anomalous structure” in a sequence of data. Basically for a sequence , the null hypothesis is that they are all i.i.d. and the (composite) alternative is that there an interval of indices which are instead. She proposed a RKHS-based discrepancy measure and a threshold test on this measure. **Pradeep Ravikumar** talked about a “simple” estimator that was a “fix” for ordinary least squares with some soft thresholding. He showed consistency for linear regression in several senses, competitive with LASSO in some settings. Pretty neat, all said, although he also claimed that least squares was “something you all know from high school” — I went to a pretty good high school, and I don’t think we did least squares! **Sanmi Koyejo** talked about a Bayesian devision theory approach to variable selection that involved minimizing some KL-divergence. Unfortunately, the resulting optimization ended up being NP-hard (for reasons I can’t remember) and so they use a greedy algorithm that seems to work pretty well.

*(Privacy)*

**Cynthia Dwork** gave a tutorial on differential privacy with an emphasis on the recent work involving false discovery rate. In addition to her plenary there were several talks on differential privacy and other privacy measures. **Kunal Talwar** talked about their improved analysis of the SuLQ method for differentially private PCA. Unfortunately there were two privacy sessions in parallel so I hopped over to see **John Duchi** talk about definitions of privacy and how definitions based on testing are equivalent to differential privacy. The testing framework makes it easier to prove minimax bounds, though, so it may be a more useful view at times. **Nadia Fawaz** talked about privacy for time-series data such as smart meter data. She defined different types of attacks in this setting and showed that they correspond to mutual information or directed mutual information, as well as empirical results on a real data set. **Raef Bassily** studied a estimation problem in the streaming setting where you want to get a histogram of the most frequent items in the stream. They reduce the problem to one of finding a “unique heavy hitter” and develop a protocol that looks sort of like a code for the MAC: they encode bits into a real vector, had noise, and then add those up over the reals. It’s accepted to STOC 2015 and he said the preprint will be up soon.