Happy New Year and I know I've been silent but I've been busy. But no teaching this semester (YAY!) so maybe you'll see more posts.

At any rate, I'm really late to the table, but here are my comments about this past year's NIPS. Before we get to that, I hope that everyone knows that this coming NIPS will be in Granada, and then for (at least) the next five years will be in Tahoe. Now that I'm not in ski-land, it's nice to have a yearly ski vacation ... erm I mean scientific conference.

But since this was the last year of NIPS in Vancouver, I thought I'd share a conversation that occurred this year at NIPS, with participants anonymized. (I hope everyone knows to take this in good humor: I'm perfectly happy to poke fun at people from the States, too...). The context is that one person in a large group, which was going to find lunch, had a cell phone with a data plan that worked in Canada:

Okay it's not that funny, but it was funny at the time. (And really "B" is as much a joke about the US as it was about Canada :P.)A:Wow, that map is really taking a long time to load.

B:I know. It's probably some socialized Canadian WiFi service.

C:No, it's probably just slow because every third bit has to be a Canadian bit?

D:No no, it's because every bit has to be sent in both English and French!

But I'm sure you are here to hear about papers, not stupid Canada jokes. So here's my take.

The tutorial on optimization by Stephen Wright was awesome. I hope this shows up on videolectures soon.

**(Update: it has!)**I will make it required reading / watching for students. There's just too much great stuff in it to go in to, but how about this: momentum is the same as CG! Really?!?! There's tons of stuff that I want to look more deeply into, such as robust mirror descent, some work by Candes about SVD when we don't care about near-zero SVs, regularized stochastic gradient (Xiao) and sparse eigenvector work. Lots of awesome stuff. My favorite part of NIPS.

**Some papers I saw that I really liked:**

A Theory of Multiclass Boosting (Indraneel Mukherjee, Robert Schapire): Formalizes boosting in a multiclass setting. The crux is a clever generalization of the "weak learning" notion from binary. The idea is that a weak binary classifier is one that has a small

*advantage*over random guessing (which, in the binary case, gives 50/50). Generalize this and it works.

Structured sparsity-inducing norms through submodular functions (Francis Bach): I need to read this. This was one of those talks where I understood the first half and then got lost. But the idea is that you can go back-and-forth between submodular functions and sparsity-inducing norms.

Construction of Dependent Dirichlet Processes based on Poisson Processes (Dahua Lin, Eric Grimson, John Fisher): The title says it all! It's an alternative construction to the Polya urn scheme and also to the stick-breaking scheme.

A Reduction from Apprenticeship Learning to Classification (Umar Syed, Robert Schapire): Right up my alley, some surprising results about apprenticeship learning (aka Hal's version of structured prediction) and classification. Similar to a recent paper by Stephane Ross and Drew Bagnell on Efficient Reductions for Imitation Learning.

Variational Inference over Combinatorial Spaces (Alexandre Bouchard-Cote, Michael Jordan): When you have complex combinatorial spaces (think traveling salesman), how can you construct generic variational inference algorithms?

Implicit Differentiation by Perturbation (Justin Domke): This is a great example of a paper that I never would have read, looked at, seen, visited the poster of, known about etc., were it not for serendipity at conferences (basically Justin was the only person at his poster when I showed up early for the session, so I got to see this poster). The idea is if you have a graphical model, and some loss function L(.) which is defined over the marginals mu(theta), where theta are the parameters of the model, and you want to optimize L(mu(theta)) as a function of theta. Without making any serious assumptions about the form of L, you can actually do gradient descent, where each gradient computation costs two runs of belief propagation. I think this is amazing.

Probabilistic Deterministic Infinite Automata (David Pfau, Nicholas Bartlett, Frank Wood): Another one where the title says it all. DP-style construction of infinite automata.

Graph-Valued Regression (Han Liu, Xi Chen, John Lafferty, Larry Wasserman): The idea here is to define a regression function over a graph. It should be regularized in a sensible way. Very LASSO-esque model, as you might expect given the author list :).

**Other papers I saw that I liked but not enough to write mini summaries of:**

Word Features for Latent Dirichlet Allocation (James Petterson, Alexander Smola, Tiberio Caetano, Wray Buntine, Shravan Narayanamurthy)

Tree-Structured Stick Breaking for Hierarchical Data (Ryan Adams, Zoubin Ghahramani, Michael Jordan)

Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations (Danial Lashkari, Ramesh Sridharan, Polina Golland)

Trading off Mistakes and Don't-Know Predictions (Amin Sayedi, Morteza Zadimoghaddam, Avrim Blum)

Joint Analysis of Time-Evolving Binary Matrices and Associated Documents (Eric Wang, Dehong Liu, Jorge Silva, David Dunson, Lawrence Carin)

Learning Efficient Markov Networks (Vibhav Gogate, William Webb, Pedro Domingos)

Tree-Structured Stick Breaking for Hierarchical Data (Ryan Adams, Zoubin Ghahramani, Michael Jordan)

Construction of Dependent Dirichlet Processes based on Poisson Processes (Dahua Lin, Eric Grimson, John Fisher)

Supervised Clustering (Pranjal Awasthi, Reza Bosagh Zadeh)

**Two students who work with me (though one isn't actually mine :P), who went to NIPS also shared their favorite papers. The first is a list from Avishek Saha:**

A Theory of Multiclass Boosting (Indraneel Mukherjee, Robert Schapire)

Repeated Games against Budgeted Adversaries (Jacob Abernethy, Manfred Warmuth)

Non-Stochastic Bandit Slate Problems (Satyen Kale, Lev Reyzin, Robert Schapire)

Trading off Mistakes and Don't-Know Predictions (Amin Sayedi, Morteza Zadimoghaddam, Avrim Blum)

Learning Bounds for Importance Weighting (Corinna Cortes, Yishay Mansour, Mehryar Mohri)

Supervised Clustering (Pranjal Awasthi, Reza Bosagh Zadeh)

**The second list is from Piyush Rai, who apparently aimed for recall (though not with a lack of precision) :P:**

Online Learning: Random Averages, Combinatorial Parameters, and Learnability (Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari): defines several complexity measures for online learning akin to what we have for the batch setting (e.g., radamacher averages, covering numbers etc).

Online Learning in The Manifold of Low-Rank Matrices (Uri Shalit, Daphna Weinshall, Gal Chechik): nice general framework applicable in a number of online learning settings. could also be used for online multitask learning.

Fast global convergence rates of gradient methods for high-dimensional statistical recovery (Alekh Agarwal, Sahand Negahban, Martin Wainwright): shows that the properties of sparse estimation problems that lead to statistical efficiency also lead to computational efficiency which explains the faster practical convergence of gradient methods than what the theory guarantees.

Copula Processes (Andrew Wilson, Zoubin Ghahramani): how do you determine the relationship between random variables which could have different marginal distributions (say one has gamma and the other has gaussian distribution)? copula process gives an answer to this.

Graph-Valued Regression (Han Liu, Xi Chen, John Lafferty, Larry Wasserman): usually undirected graph structure learning involves a set of random variables y drawn from a distribution p(y). but what if y depends on another variable x? this paper is about learning the graph structure of the distribution p(y|x=x).

Structured sparsity-inducing norms through submodular functions (Francis Bach): standard sparse recovery uses l1 norm as a convex proxy for the l0 norm (which constrains the number of nonzero coefficients to be small). this paper proposes several more general set functions and their corresponding convex proxies, and links them to known norms.

Trading off Mistakes and Don't-Know Predictions (Amin Sayedi, Morteza Zadimoghaddam, Avrim Blum): an interesting paper -- what if in an online learning setting you could abstain from making a prediction on some of the training examples and just say "i don't know"? on others, you may or may not make the correct prediction. lies somewhere in the middle of always predicting right or wrong (i.e., standard mistake driven online learning) versus the recent work on only predicting correctly or otherwise saying "i don't know".

Variational Inference over Combinatorial Spaces (Alexandre Bouchard-Cote, Michael Jordan): cool paper. applicable to lots of settings.

A Theory of Multiclass Boosting (Indraneel Mukherjee, Robert Schapire): we know that boosting in binary case requires "slightly better than random" weak learners. this paper characterizes conditions on the weak learners for the multi-class case, and also gives a boosting algorithm.

Multitask Learning without Label Correspondences (Novi Quadrianto, Alexander Smola, Tiberio Caetano, S.V.N. Vishwanathan, James Petterson): usually mtl assumes that the output space is the same for all the tasks but in many cases this may not be true. for instance, we may have two related prediction problems on two datasets but the output spaces for both may be different and may have some complex (e.g., hierarchical, and potentially time varying) output spaces. the paper uses a mutual information criteria to learn the correspondence between the output spaces.

Learning Multiple Tasks with a Sparse Matrix-Normal Penalty (Yi Zhang, Jeff Schneider): presents a general multitask learning framework and many recently proposed mtl models turn out to be special cases. models both feature covariance and task covariance matrices.

Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions (Achintya Kundu, vikram Tankasali, Chiranjib Bhattacharyya, Aharon Ben-Tal): the title says it all. :) multiple kernel learning is usually applied in classification setting but due to the applicability of the proposed method for a wide variety of loss functions, one can possibly also use it for unsupervised learning problems as well (e.g., spectral clustering, kernel pca, etc).

Getting lost in space: Large sample analysis of the resistance distance (Ulrike von Luxburg, Agnes Radl, Matthias Hein): large sample analysis of the commute distance: shows a rather surprising result that commute distance between two vertices in the graph if the graph is "large" and nodes represent high dimensional variables is meaningless. the paper proposes a correction and calls it "amplified commute distance".

A Bayesian Approach to Concept Drift (Stephen Bach, Mark Maloof): gives a bayesian approach for segmenting a sequence of observations such that each "block" of observations has the same underlying concept.

MAP Estimation for Graphical Models by Likelihood Maximization (Akshat Kumar, Shlomo Zilberstein): they show that you can think of an mrf as a mixture of bayes nets and then the map problem on the mrf corresponds to solving a form of the maximum likelihood problem on the bayes net. em can be used to solve this in a pretty fast manner. they say that you can use this methods with the max-product lp algorithms to yield even better solutions, with a quicker convergence.

Energy Disaggregation via Discriminative Sparse Coding (J. Zico Kolter, Siddharth Batra, Andrew Ng): about how sparse coding could be used to save energy. :)

Semi-Supervised Learning with Adversarially Missing Label Information (Umar Syed, Ben Taskar): standard ssl assumes that labels for the unlabeled data are missing at random but in many practical settings this isn't actually true.this paper gives an algorithm to deal with the case when the labels could be adversarially missing.

Multi-View Active Learning in the Non-Realizable Case (Wei Wang, Zhi-Hua Zhou): shows that (under certain assumptions) exponential improvements in the sample complexity of active learning are still possible if you have a multiview learning setting.

Self-Paced Learning for Latent Variable Models (M. Pawan Kumar, Benjamin Packer, Daphne Koller): an interesting paper, somewhat similar in spirit to curriculum learning. basically, the paper suggests that in learning a latent variable model, it helps if you provide the algorithm easy examples first.

More data means less inference: A pseudo-max approach to structured learning (David Sontag, Ofer Meshi, Tommi Jaakkola, Amir Globerson): a pseudo-max approach to structured learning: this is somewhat along the lines of the paper on svm's inverse dependence on training size from icml a couple of years back. :)

Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning (Prateek Jain, Sudheendra Vijayanarasimhan, Kristen Grauman): selecting the most uncertain example in a pool based active learning can be expensive if the number of candidate examples is very large. this paper suggests some hashing tricks to expedite the search.

Active Instance Sampling via Matrix Partition (Yuhong Guo): frames batch mode active learning as a matrix partitioning problems and proposes local optimization technique for the matrix partitioning problem.

A Discriminative Latent Model of Image Region and Object Tag Correspondence (Yang Wang, Greg Mori): it's kind of doing correspondence lda on image+captions but they additionally infer the correspondences between tags and objects in the images, and show that this gives improvements over corr-lda.

Factorized Latent Spaces with Structured Sparsity (Yangqing Jia, Mathieu Salzmann, Trevor Darrell): a multiview learning algorithm that uses sparse coding to learn shared as well as private features of different views of the data.

Word Features for Latent Dirichlet Allocation (James Petterson, Alexander Smola, Tiberio Caetano, Wray Buntine, Shravan Narayanamurthy): extends lda for the case when you have access to features for each word in the vocabulary

## 4 comments:

Hi Hal, thanks for the blurb about our apprenticeship learning paper. The paper by Drew Bagnell that is closely related is:

Ross, Stephane & Bagnell, Drew "Efficient Reductions for Imitation Learning", AISTATS 2010

Thanks for the notes! As usual, this is very helpful!

I have found the videolecture, the link is: http://videolectures.net/nips2010_wright_oaml/

@honglangwang: thanks, i've updated the post!

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