We introduce a method to predict the variability in (probability distribution of) driving time on an arbitrary route in a road network at a given time, using mobile phone GPS data. Although commercial mapping services currently provide a high-quality estimate of driving time on a given route, there can be considerable uncertainty in that prediction due for example to unknown timing of traffic signals, uncertainties in traffic congestion levels, and differences in driver habits. For this reason, a distribution prediction can be more valuable than a deterministic prediction of driving time, by accounting not just for the measured traffic conditions and other available information, but also for the presence of unmeasured conditions that also affect driving time. Accurate distribution predictions can be used to report variability to the user, to provide risk-averse route recommendations, and as a part of vehicle fleet decision support systems. Simple approaches to distribution prediction assume independence in driving time across road segments and as a result dramatically underestimate the variability in driving time. We propose a method that accurately accounts for dependencies in

driving time across road segments, and apply it to large volumes of mobile phone GPS data from the Seattle metropolitan region.

**Title:**Driving Time Variability Prediction Using Mobile Phone Location Data

**Speaker:**Dawn Woodard, Cornell University

**When:**Tuesday, January 13, 2015 - 12:30

**Location:**CSE 305

**Title:**TBA

**Speaker:**Yi Chang, Yahoo! Research

**When:**Tuesday, November 4, 2014 - 12:30

**Location:**CSE 305

**Title:**Deep Representation Learning: Challenges and New Directions

**Speaker:**Honglak Lee, University of Michigan

**When:**Thursday, October 30, 2014 - 12:30

**Location:**CSE 305

Machine learning is a powerful tool for tackling challenging problems

in artificial intelligence. In practice, success of machine learning

algorithms critically depends on the feature representations for input

data, which often becomes a limiting factor. To address this problem,

deep learning methods have recently emerged as successful techniques

to learn feature hierarchies from unlabeled and labeled data. In this

talk, I will present my perspectives on the progress, challenges, and

some new directions. Specifically, I will talk about my recent work to

address the following interrelated challenges: (1) how can we learn

invariant yet discriminative features, and furthermore disentangle

underlying factors of variation to model high-order interactions

between the factors? (2) how can we learn representations of the

output data when the output variables have complex high-order

dependencies? (3) how can we learn shared representations from

heterogeneous input data modalities?

Bio:

Honglak Lee is an Assistant Professor of Computer Science and

Engineering at the University of Michigan, Ann Arbor. He received his

Ph.D. from Computer Science Department at Stanford University in 2010,

advised by Prof. Andrew Ng. His primary research interests lie in

machine learning, which spans over deep learning, unsupervised and

semi-supervised learning, transfer learning, graphical models, and

optimization. He also works on application problems in computer

vision, audio recognition, robot perception, and text processing. His

work received best paper awards at ICML and CEAS. He has served as a

guest editor of IEEE TPAMI Special Issue on Learning Deep

Architectures, as well as area chairs of ICML and NIPS. He received

the Google Faculty Research Award in 2011, and was selected by IEEE

Intelligent Systems as one of AI's 10 to Watch in 2013.

**Title:**Massive, Sparse, Efficient Multilabel Learning

**Speaker:**Charles Elkan, UCSD and Amazon

**When:**Tuesday, October 21, 2014 - 12:30

**Location:**TBA (not CSE 305)

Amazon has many applications whose core is multilabel

classification. This talk will present progress towards a multilabel

learning method that can handle 10^7 training examples, 10^6 features, and

10^5 labels on a single workstation. A sparse linear model is learned for

each label simultaneously by stochastic gradient descent with L2 and L1

regularization. Tractability is achieved through careful use of sparse data

structures, and speed is achieved by using the latest stochastic gradient

methods that do variance reduction. Both theoretically and practically,

these methods achieve order-of-magnitude faster convergence than Adagrad.

We have extended them to handle non-differentiable L1 regularization. We

show experimental results on classifying biomedical articles into 26,853

scientific categories. [Joint work with Galen Andrew, ML intern at Amazon.]

**Bio** Charles Elkan is the first Amazon Fellow, on leave from being a

professor of computer science at the University of California, San Diego.

In the past, he has been a visiting associate professor at Harvard and a

researcher at MIT. His published research has been mainly in machine

learning, data science, and computational biology. The MEME algorithm that

he developed with Ph.D. students has been used in over 3000 published

research projects in biology and computer science. He is fortunate to have

had inspiring undergraduate and graduate students who are in leadership

positions now such as vice president at Google.

**Title:**Learning Mixtures of Ranking Models

**Speaker:**Pranjal Awasthi, Princeton University

**When:**Tuesday, October 7, 2014 - 12:30

**Location:**CSE 305

Probabilistic modeling of ranking data is an extensively studied

problem with applications ranging from understanding user preferences

in electoral systems and social choice theory, to more modern learning

tasks in online web search, crowd-sourcing and recommendation

systems. This work concerns learning the Mallows model -- one of the

most popular probabilistic models for analyzing ranking data. In this

model, the user's preference ranking is generated as a noisy version

of an unknown central base ranking. The learning task is to recover

the base ranking and the model parameters using access to noisy

rankings generated from the model.

Although well understood in the setting of a homogeneous population (a

single base ranking), the case of a heterogeneous population (mixture

of multiple base rankings) has so far resisted algorithms with

guarantees on worst case instances. In this talk I will present the

first polynomial time algorithm which provably learns the parameters

and the unknown base rankings of a mixture of two Mallows models. A

key component of our algorithm is a novel use of tensor decomposition

techniques to learn the top-k prefix in both the rankings. Before this

work, even the question of identifiability in the case of a mixture of

two Mallows models was unresolved.

Joint work with Avrim Blum, Or Sheffet and Aravindan Vijayaraghavan.

**Title:**Convex and Bayesian methods for link prediction using distributed representations

**Speaker:**Guillaume Bouchard, Xerox Research Europe

**When:**Monday, September 29, 2014 - 12:30

**Location:**CSE 305

Many applications involve multiple interlinked data sources, but existing

approach to handle them are often based on latent factor models (i.e.

distributed representations) which are difficult to learn. At the same

time, recent advances in convex analysis, mainly based on the nuclear norm

(relaxation of the matrix rank) and sparse structured approximations, have

shown great theoretical and practical performances to handle very large

matrix factorization problems with non-Gaussian noise and missing data.

In this talk, we will show how multiple matrices or tensors can be jointly

factorized using a convex formulation of the problem, with a particular

focus on:

- Multi-view learning: A popular approach is to assume that, both, the

correlations between the views and the view-specific correlations have

low-rank structure, leading to a model closely related to canonical

correlation analysis called inter-battery factor analysis. We propose a

convex relaxation of this model, based on a structured nuclear norm

regularization. - Collective matrix factorization: When multiple matrices are related, they

share common latent factors, leading to a simple yet powerful way of

handling complex data structures, such as relational databases. Again, a

convex formulation of this approach is proposed. We also show that the

Bayesian version of this model can be used to tune the multiple

regularization parameters involved in such models, avoiding costly

cross-validation.

Another contribution to KB modeling relates to binary tensor and matrix

factorization with many zeros. We show a new learning approaches for binary

data that scales linearly with the number of positive examples. It is based

on a iterative split of the tensor (or matrix) on which the binary loss is

approximated by a Gaussian loss which itself can be efficiently minimized.

Experiments on popular tasks such as data imputation, multi-label

prediction, link prediction in graphs and item recommendation illustrate

the benefit of the proposed approaches.

**Bio**Guillaume Bouchard is senior research scientist at Xerox Research

Centre Europe in Grenoble, France. After an engineering degree and master

in mathematics in Université de Rouen, he obtained a PhD in statistics from

Institut National de Recherche en Information et Automatique (INRIA) in

2004. Since then, he worked for Xerox on multiple machine learning research

project in big data analysis, including user modelling, recommender systems

and natural language processing. He was involved in French and European

research projects called LAVA, FUPOL, Fusepool and Dynamicité. His current

research focuses on the development of distributed statistical relational

models for knowledge bases, applied to the development of virtual agents.

**Title:**A General Framework for Fast Stagewise Algorithms

**Speaker:**Ryan Tibshirani, Carnegie Mellon University

**When:**Tuesday, May 20, 2014 - 12:30

**Location:**CSE 305

Forward stagewise regression follows a very simple strategy for constructing
a sequence of sparse regression estimates: starting with all coefficients
equal to zero, it iteratively updates the coefficient (by a small amount
*ε*) corresponding to the variable that has maximal absolute inner
product with the current residual. This procedure has an interesting
connection to the lasso: under some conditions, it can be shown that the
sequence of forward stagewise estimates exactly coincides with the lasso
path, as the step size *ε* goes to zero. Further, essentially the
same equivalence holds outside of the regression setting, for minimizing a
differentiable convex loss function subject to an *l*_{1} norm constraint
(and the stagewise algorithm now updating the coefficient corresponding to
the maximal absolute component of the gradient).

Even when they do not match their *l*_{1}-constrained analogues, stagewise
estimates provide a useful approximation, and are computationally appealing.
Their success in sparse modeling motivates the question: can a simple,
effective strategy like forward stagewise be applied more broadly in other
regularization settings, beyond the *l*_{1} norm and sparsity? This is the
focus of the talk; we present a general framework for stagewise estimation,
which yields fast algorithms for problems such as group-structured learning,
matrix completion, image denoising, and more.

**Title:**Bayesian Optimization of Machine Learning Algorithms

**Speaker:**Ryan Adams, Harvard University

**When:**Tuesday, May 6, 2014 - 12:30

**Location:**CSE 403

Machine learning algorithms frequently involve careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience, rules of thumb, or sometimes brute-force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. I will describe my recent work on solving this problem with Bayesian nonparametrics, using Gaussian processes. This approach of "Bayesian optimization" models the generalization performance as an unknown objective function with a GP prior. I will discuss new algorithms that account for variable cost in function evaluation and take advantage of parallelism in evaluation. These new algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation for text analysis, structured SVMs for protein motif finding, and convolutional neural networks for visual object recognition.

**Title:**Sparse latent factor models to uncover structure in genomic data

**Speaker:**Barbara Engelhardt, Duke University

**When:**Tuesday, April 29, 2014 - 12:30

**Location:**CSE 305

Within high-dimensional genomic data, including genetic markers, complex phenotypes, and gene expression measurements, hides a substantial amount of low dimensional structure. In order to extract this low dimensional structure from high dimensional data, latent factor models with appropriate priors and modeling assumptions can be applied. A key advantage of sparse latent factor models in genomic applications is interpretability: the low dimensional structure can often be understood in a biological context. Model specification here is important, however, as genomics data generally includes small numbers of samples n with respect to enormous numbers of features p. Sparse linear latent factor models have been used to identify population substructure. Applying these models to high dimensional noisy phenotypes produces interpretable phenotypes that can subsequently tested for association with genetic variants. This same idea can be considered for gene expression traits, where the low dimensional structure captures groups of interacting genes, and the associated genetic variants represent master regulators. In this talk, I will discuss recent work on sparse latent factor models, and results on genomic data.

**Title:**Submodular combinatorial problems in Machine Learning: Algorithms and Applications

**Speaker:**Rishabh Iyer, UW Electrical Engineering

**When:**Tuesday, April 22, 2014 - 12:30

**Location:**CSE 305

Having long been recognized in combinatorics, the concept of submodularity is becoming increasingly important in machine learning, since it can capture intuitive and yet non-trivial interactions between variables. This expressiveness has found many applications in machine learning, particularly in understanding structured data like text, vision and speech. Submodular functions naturally capture aspects of information, coverage and diversity in maximization problems, while also modelling notions of cooperation, sparsity, complexity and economies of scale in minimization problems. In this talk, we shall consider a large class of submodular optimization problems and motivate them with several real world applications, particularly in machine learning. We shall then provide a unifying algorithmic framework for solving several of these optimization problems, and highlight the scalability and practicality of this approach. We willl also highlight novel theoretical characterizations, which provide better connections between theory and practice. We shall ground this entire talk with a large number of applications in machine learning, including image segmentation, image correspondence, image collection summarization, feature selection, and data subset selection. This talk should be self contained and shall not require prior knowledge of submodular functions and optimization.

(this is based on joint work with Jeff Bilmes, Stefanie Jegelka, Sebastian Tschiatschek and Kai Wei)