Jan 11
Bren Hall 4011 1 pm 
Social network analysis has a long and successful history in the social sciences, often with a focus on relatively small surveybased data sets. In the past decade, driven by the ease of automatically collecting largescale network data sets, there has been significant interest in developing new statistical and machine learning techniques for network analysis. In this talk we will focus on two general modeling themes in this context: the use of latent variables for lowdimensional vectorbased network representations models and eventbased models for temporal network data. We will review the representational capabilities of these models from a generative perspective, discuss some of the challenges of parameter estimation that arise, and emphasize the role of predictive evaluation. The talk will conclude with a brief discussion of future directions in this general area.
Based on joint work with Zach Butler, Chris DuBois, Jimmy Foulds, and Carter Butts 
Jan 18

No Seminar (MLK Day)

Jan 25
Bren Hall 4011 1 pm 
Topic models have become increasingly prominent textanalytic machine learning tools for research in the social sciences and the humanities. In particular, custom topic models can be developed to answer specific research questions. The design of these models requires a nontrivial amount of effort and expertise, motivating generalpurpose topic modeling frameworks. In this talk I will introduce latent topic networks, a flexible class of richly structured topic models designed to facilitate applied research. Custom models can straightforwardly be developed in this framework with an intuitive firstorder logical probabilistic programming language. Latent topic networks admit scalable training via a parallelizable EM algorithm which leverages ADMM in the Mstep. I demonstrate the broad applicability of the models with case studies on modeling influence in citation networks, and U.S. Presidential State of the Union addresses. This talk is based on joint work with Lise Getoor and Shachi Kumar from the University of California, Santa Cruz, published at ICML 2015. 
Feb 1
Bren Hall 4011 1 pm 
Latent or hidden variable models have applications in almost every domain, e.g., social network analysis, natural language processing, computer vision and computational biology. Training latent variable models is challenging due to nonconvexity of the likelihood objective function. An alternative method is based on the spectral decomposition of low order moment matrices and tensors. This versatile framework is guaranteed to estimate the correct model consistently. I will discuss my results on convergence to globally optimal solution for stochastic gradient descent, despite nonconvexity of the objective. I will then discuss largescale implementations (which are highly parallel and scalable) of spectral methods, carried out on CPU/GPU and Spark platforms. We obtain a gain in both accuracies and in running times by several orders of magnitude compared to the stateofart variational methods. I will discuss the following applications in detail: (1) learning hidden user commonalities (communities) in social networks, and (2) learning sentence embeddings for paraphrase detection using convolutional models. More generally, I have applied the methods to a variety of problems such as text and social network analysis, healthcare analytics, and cataloging neuronal cell types in neuroscience. 
Feb 8
Bren Hall 4011 1 pm 
Optimization lies at the core of machine learning. However, most machine learning problems entail nonconvex optimization. In this talk, I will show how spectral and tensor methods can yield guaranteed convergence to globally optimal solutions under transparent conditions for a range of machine learning problems.
In the first part, I will explain how tensor methods are useful for learning latent variable models in an unsupervised manner. The focus of my work is on overcomplete regime where the hidden dimension is larger than the observed dimensionality. I describe how tensor methods enable us to learn these models in the overcomplete regime with theoretical guarantees in recovering the parameters of the model. I also provide efficient sample complexity results for training these models. Next, I will describe a new method for training neural networks for which we provide theoretical guarantees on the performance of the algorithm. We have developed a computationally efficient algorithm for training a twolayer neural network using methodofmoment and tensor decomposition techniques. 
Feb 10
Bren Hall 3011 3 pm 
I will discuss subsampling and sketching with their applications and analysis in machine learning. They can be viewed not only as tools to improve computational and storage efficiency of existing learning algorithms, but also as settings that characterize data measurement/availability/privacy constraints in modern machine learning applications. In this talk I will introduce my recent work, which analyze subsampling and sketching settings in three popular machine learning algorithms: tensor factorization, subspace clustering and linear regression. 
Feb 15

No Seminar (Presidents Day)

Feb 22
Bren Hall 4011 1 pm 
Understanding the semantics of preferences and behavior is incredibly complicated, especially in settings where the visual appearance of items influences our decisions. Three challenges that I’ll discuss in this talk include (1) how can we uncover the semantics of visual preferences, especially in sparse or longtailed data, where new items are constantly introduced? (2) How can we use visual data to understand the relationships between items, and in particular what makes two items “visually compatible”? And (3) how can we understand the temporal dynamics of visual preferences, in order to uncover how “fashions” have evolved over time? 
Feb 29

No Seminar (Cancelled)

Mar 7
Bren Hall 4011 1 pm 
We investigate the potential of lookahead in the context of AND/OR search in graphical models using the minibucket heuristic for combinatorial optimization tasks (e.g. MAP/MPE or weighted CSPs.) We present and analyze the complexity of computing the residual (a.k.a. Bellman update) of the minibucket heuristic, which we call “bucket errors” and show how this can be used to identify which parts of the search space are more likely to benefit from lookahead, therefore facilitating a method to bound its overhead. We also rephrase the lookahead computation as a graphical model to make use of structure exploiting inference schemes. In our empirical results, we demonstrate that our methods can be used to costeffectively increase the power of branchandbound search.
In the second part of the talk, we show how bucket errors can be used to improve the performance of AND/OR bestfirst search algorithms for providing lower bounds on the minsum problem. In our preliminary experiments, we show that when expanding nodes for the AO* algorithm, using bucket errors as a subproblem ordering heuristic can allow us to expand fewer nodes to arrive at the optimal solution compared to the existing ordering approach. 