Publications

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Conference Paper
Moghaddam, Baback, Benjamin M. Marlin, Mohammad Emtiyaz Khan, and Kevin P. Murphy. "Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models." NIPS. 2009. 1285-1293. Abstract

In this paper we make several contributions towards accelerating approximate Bayesian structural inference for non-decomposable GGMs. Our first contribution is to show how to efficiently compute a BIC or Laplace approximation to the marginal likelihood of non-decomposable graphs using convex methods for precision matrix estimation. This optimization technique can be used as a fast scoring function inside standard Stochastic Local Search (SLS) for generating posterior samples. Our second contribution is a novel framework for efficiently generating large sets of high-quality graph topologies without performing local search. This graph proposal method, which we call "Neighborhood Fusion" (NF), samples candidate Markov blankets at each node using sparse regression techniques. Our final contribution is a hybrid method combining the complementary strengths of NF and SLS. Experimental results in structural recovery and prediction tasks demonstrate that NF and hybrid NF/SLS out-perform state-of-the-art local search methods, on both synthetic and real-world data sets, when realistic computational limits are imposed.

Boutilier, Craig, Richard S. Zemel, and Benjamin M. Marlin. "Active Collaborative Filtering." UAI. 2003. 98-106. Abstract

Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the recommendations made? We cast this terms of expected value of information (EVOI); but the online computational cost of computing optimal queries is prohibitive. We show how offline prototyping and computation of bounds on EVOI can be used to dramatically reduce the required online computation. The framework we develop is general, but we focus on derivations and empirical study in the specific case of the multiple-cause vector quantization model.

Huang, Haibin, Evangelos Kalogerakis, and Benjamin Marlin. "Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces." Symposium on Geometry Processing. 2015. Abstracthuang-sgp2015.pdf

We present a method for joint analysis and synthesis of geometrically diverse 3D shape families. Our method first learns part-based templates such that an optimal set of fuzzy point and part correspondences is computed between the shapes of an input collection based on a probabilistic deformation model. In contrast to previous template-based approaches, the geometry and deformation parameters of our part-based templates are learned from scratch. Based on the estimated shape correspondence, our method also learns a probabilistic generative model that hierarchically captures statistical relationships of corresponding surface point positions and parts as well as their existence in the input shapes. A deep learning procedure is used to capture these hierarchical relationships. The resulting generative model is used to produce control point arrangements that drive shape synthesis by combining and deforming parts from the input collection. The generative model also yields compact shape descriptors that are used to perform fine-grained classification. Finally, it can be also coupled with the probabilistic deformation model to further improve shape correspondence. We provide qualitative and quantitative evaluations of our method for shape correspondence, segmentation, fine-grained classification and synthesis. Our experiments demonstrate superior correspondence and segmentation results than previous state-of-the-art approaches.

Jacek, Nicholas, Meng-Chieh Chiu, Benjamin Marlin, and Eliot J. B. Moss. "Assessing the Limits of Program-Specific Garbage Collection Performance." Programming Language Design and Implementation. 2016. Abstractp584-jacek.pdf

Distinguished Paper Award

We consider the ultimate limits of program-specific garbage collector performance for real programs. We first characterize the GC schedule optimization problem using Markov Decision Processes (MDPs). Based on this characterization, we develop a method of determining, for a given program run and heap size, an optimal schedule of collections for a non-generational collector. We further explore the limits of performance of a generational collector, where it is not feasible to search the space of schedules to prove optimality. Still, we show significant improvements with Least Squares Policy Iteration, a reinforcement learning technique for solving MDPs. We demonstrate that there is considerable promise to reduce garbage collection costs by developing program-specific collection policies.

Marlin, Benjamin M., and Nando de Freitas. "Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood." UAI. 2011. 497-505. Abstractestimators_uai11_paper.pdf

Standard maximum likelihood estimation cannot be applied to discrete energy-based models in the general case because the computation of exact model probabilities is intractable. Recent research has seen the proposal of several new estimators designed specifically to overcome this intractability, but virtually nothing is known about their theoretical properties. In this paper, we present a generalized estimator that unifies many of the classical and recently proposed estimators. We use results from the standard asymptotic theory for M-estimators to derive a generic expression for the asymptotic covariance matrix of our generalized estimator. We apply these results to study the relative statistical efficiency of classical pseudolikelihood and the recently-proposed ratio matching estimator.

Marlin, Benjamin M., Richard S. Zemel, Sam T. Roweis, and Malcolm Slaney. "Collaborative Filtering and the Missing at Random Assumption." UAI. 2007. 267-275. Abstract

Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a user study in which we collect a random sample of ratings from current users of an online radio service. An analysis of the rating data collected in the study shows that the sample of random ratings has markedly different properties than ratings of user-selected songs. When asked to report on their own rating behaviour, a large number of users indicate they believe their opinion of a song does affect whether they choose to rate that song, a violation of the MAR condition. Finally, we present experimental results showing that incorporating an explicit model of the missing data mechanism can lead to significant improvements in prediction performance on the random sample of ratings.

Li, Steven Cheng-Xian, and Benjamin M. Marlin. "Collaborative Multi-Output Gaussian Processes for Collections of Sparse Multivariate Time Series,." NIPS Time Series Workshop. 2015. Abstractli-nips-ts2015.pdf

Collaborative Multi-Output Gaussian Processes (COGPs) are a flexible tool for modeling multivariate time series. They induce correlation across outputs through the use of shared latent processes. While past work has focused on the computational challenges that result from a single multivariate time series with many observed values, this paper explores the problem of fitting the COGP model to collections of many sparse and irregularly sampled multivariate time series. This work is motivated by applications to modeling physiological data (heart rate, blood pressure, etc.) in Electronic Health Records (EHRs).

Marlin, Benjamin M., and Richard S. Zemel. "Collaborative prediction and ranking with non-random missing data." RecSys. 2009. 5-12. Abstract

A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative filtering and recommender systems is founded on the assumption that missing ratings are missing at random. The statistical theory of missing data shows that incorrect assumptions about missing data can lead to biased parameter estimation and prediction. In a recent study, we demonstrated strong evidence for violations of the missing at random condition in a real recommender system. In this paper we present the first study of the effect of non-random missing data on collaborative ranking, and extend our previous results regarding the impact of non-random missing data on collaborative prediction.

Natarajan, Annamalai, Edward Gaiser, Gustavo Angarita, Robert Malison, Deepak Ganesan, and Benjamin Marlin. "Conditional Random Fields for Morphological Analysis of Wireless ECG Signals." Proceedings of the 5th Annual conference on Bioinformatics, Computational Biology and Health Informatics. Newport Beach, CA: ACM, 2014. Abstractcrf_bcb2014.pdf

Thanks to advances in mobile sensing technologies, it has recently become practical to deploy wireless electrocardiograph sensors for continuous recording of ECG signals. This capability has diverse applications in the study of human health and behavior, but to realize its full potential, new computational tools are required to effectively deal with the uncertainty that results from the noisy and highly non-stationary signals collected using these devices. In this work, we present a novel approach to the problem of extracting the morphological structure of ECG signals based on the use of dynamically structured conditional random field (CRF) models. We apply this framework to the problem of extracting morphological structure from wireless ECG sensor data collected in a lab-based study of habituated cocaine users. Our results show that the proposed CRF-based approach significantly out-performs independent prediction models using the same features, as well as a widely cited open source toolkit.

Marlin, Benjamin M., and Godfried T. Toussaint. "Constructing convex 3-polytopes from two triangulations of a polygon." CCCG. 2002. 36-39. Abstract

Guibas conjectured that given a convex polygon P in the xy-plane along with two triangulations of it, T1 and T2 that share no diagonals, it is always possible to assign height values to the vertices of P such that P U T1 U T 2 becomes a convex 3-polytope. Dekster found a counter example but left open the questions of deciding if a given configuration corresponds to a convex 3-polytope, and constructing such realizations when they exist. This paper gives a proof that a relaxed version of Guibas' conjecture always holds true. The question of deciding the realizability of Guibas' conjecture is characterized in terms of a linear programming problem. This leads to an algorithm for deciding and constructing such realizations that incorporates a linear programming step with O(n) inequality constraints and n variables.

Natarajan, Annamalai, Abhinav Parate, Edward Gaiser, Gustavo Angarita, Robert Malison, Benjamin M. Marlin, and Deepak Ganesan. "Detecting cocaine use with wearable electrocardiogram sensors." UbiComp. 2013. 123-132. Abstractcocaine_ubicomp13_paper.pdf

Ubiquitous physiological sensing has the potential to profoundly improve our understanding of human behavior, leading to more targeted treatments for a variety of disorders. The long term goal of this work is development of novel computational tools to support the study of addiction in the context of cocaine use. The current paper takes the first step in this important direction by posing a simple, but crucial question: Can cocaine use be reliably detected using wearable electrocardiogram (ECG) sensors? The main contributions in this paper include the presentation of a novel clinical study of cocaine use, the development of a computational pipeline for inferring morphological features from noisy ECG waveforms, and the evaluation of feature sets for cocaine use detection. Our results show that 32mg/70kg doses of cocaine can be detected with the area under the receiver operating characteristic curve levels above 0.9 both within and between-subjects.

Natarajan, Annamalai, Gustavo Angarita, Edward Gaiser, Robert Malison, Deepak Ganesan, and Benjamin Marlin. "Domain Adaptation Methods for Improving Lab-to-field Generalization of Cocaine Detection using Wearable ECG." 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2016. Abstractnatarajan-ubicomp16.pdf

Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects. In this paper, we present domain adaptation methods for assessing and mitigating potential sources of performance loss in lab-to-field generalization and apply them to the problem of cocaine use detection from wearable electrocardiogram sensor data.

Marlin, Benjamin M., Mark W. Schmidt, and Kevin P. Murphy. "Group Sparse Priors for Covariance Estimation." UAI. 2009. 383-392. Abstract

Recently it has become popular to learn sparse Gaussian graphical models (GGMs) by imposing l1 or group l1,2 penalties on the elements of the precision matrix. This penalized likelihood approach results in a tractable convex optimization problem. In this paper, we reinterpret these results as performing MAP estimation under a novel prior which we call the group l1 and l1,2 positive definite matrix distributions. This enables us to build a hierarchical model in which the l1 regularization terms vary depending on which group the entries are assigned to, which in turn allows us to learn block structured sparse GGMs with unknown group assignments. Exact inference in this hierarchical model is intractable, due to the need to compute the normalization constant of these matrix distributions. However, we derive upper bounds on the partition functions, which lets us use fast variational inference (optimizing a lower bound on the joint posterior). We show that on two real world data sets (motion capture and financial data), our method which infers the block structure outperforms a method that uses a fixed block structure, which in turn outperforms baseline methods that ignore block structure.

Adams, Roy J., Edison Thomaz, and Benjamin M. Marlin. "Hierarchical Nested CRFs for Segmentation and Labeling of Physiological Time Series." NIPS Workshop on Machine Learning in Healthcare. 2015. Abstractadams-nips-heath2015.pdf

In this paper, we address the problem of nested hierarchical segmentation
and labeling of time series data. We present a hierarchical
span-based conditional random field framework for this problem that
leverages higher-order factors to enforce the nesting constraints. The framework can
incorporate a variety of additional factors including higher order cardinality
factors. This research is motivated by hierarchical activity recognition problems
in the field of mobile Health (mHealth). We show that the specific model of interest in the mHealth setting supports exact MAP inference in quadratic time. Learning is accomplished in the structured support vector machine framework. We show positive results on real and synthetic data sets.

Adams, Roy, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, and Benjamin Marlin. "Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams." International Conference on Machine Learning. 2016. Abstracticml2016_hns.pdf

The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance at the p=0.005 level relative to a hierarchical pairwise CRF.

Hiatt, Laura, Roy Adams, and Benjamin Marlin. "An Improved Data Representation for Smoking Detection with Wearable Respiration Sensors." IEEE Wireless Health. 2016. hiatt-wh2016.pdf

Late breaking extended abstract.

Marlin, Benjamin M., Kevin Swersky, Bo Chen, and Nando de Freitas. "Inductive Principles for Restricted Boltzmann Machine Learning." AISTATS. 2010. 509-516. Abstract

Recent research has seen the proposal of several new inductive principles designed specifically to avoid the problems associated with maximum likelihood learning in models with intractable partition functions. In this paper, we study learning methods for binary restricted Boltzmann machines (RBMs) based on ratio matching and generalized score matching. We compare these new RBM learning methods to a range of existing learning methods including stochastic maximum likelihood, contrastive divergence, and pseudo-likelihood. We perform an extensive empirical evaluation across multiple tasks and data sets.

Iyengar, Srinivasan, Sandeep Kalra, Anushree Ghosh, David Irwin, Prashant Shenoy, and Benjamin Marlin. "iProgram: Inferring Smart Schedules for Dumb Thermostats." 10th Annual Women in Machine Learning Workshop. 2015. Abstract

Heating, ventilation, and air conditioning (HVAC) accounts for over 50% of a typical home's energy usage. A thermostat generally controls HVAC usage in a home to ensure user comfort. In this paper, we focus on making existing "dumb" programmable thermostats smart by applying energy analytics on smart meter data to infer home occupancy patterns and compute an optimized thermostat schedule. Utilities with smart meter deployments are capable of immediately applying our approach, called iProgram, to homes across their customer base. iProgram addresses new challenges in inferring home occupancy from smart meter data where i) training data is not available and ii) the thermostat schedule may be misaligned with occupancy, frequently resulting in high power usage during unoccupied periods. iProgram translates occupancy patterns inferred from opaque smart meter data into a custom schedule for existing types of programmable thermostats, e.g., 1-day, 7-day, etc. We implement iProgram as a web service and show that it reduces the mismatch time between the occupancy pattern and the thermostat schedule by a median value of 44.28 minutes (out of 100 homes) when compared to a default 8am-6pm weekday schedule, with a median deviation of 30.76 minutes off the optimal schedule. Further, iProgram yields a daily energy saving of 0.42kWh on average across the 100 homes. Utilities may use iProgram to recommend thermostat schedules to customers and provide them estimates of potential energy savings in their energy bills.

Iyengar, Srinivasan, Sandeep Kalra, Anushree Ghosh, David Irwin, Prashant Shenoy, and Benjamin Marlin. "iProgram: Inferring Smart Schedules for Dumb Thermostats." Proceedings of the 2Nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments. BuildSys '15. New York, NY, USA: ACM, 2015. 211-220. Abstractp211-iyengar.pdf

Heating, ventilation, and air conditioning (HVAC) accounts for over 50% of a typical home's energy usage. A thermostat generally controls HVAC usage in a home to ensure user comfort. In this paper, we focus on making existing "dumb" programmable thermostats smart by applying energy analytics on smart meter data to infer home occupancy patterns and compute an optimized thermostat schedule. Utilities with smart meter deployments are capable of immediately applying our approach, called iProgram, to homes across their customer base. iProgram addresses new challenges in inferring home occupancy from smart meter data where i) training data is not available and ii) the thermostat schedule may be misaligned with occupancy, frequently resulting in high power usage during unoccupied periods. iProgram translates occupancy patterns inferred from opaque smart meter data into a custom schedule for existing types of programmable thermostats, e.g., 1-day, 7-day, etc. We implement iProgram as a web service and show that it reduces the mismatch time between the occupancy pattern and the thermostat schedule by a median value of 44.28 minutes (out of 100 homes) when compared to a default 8am-6pm weekday schedule, with a median deviation of 30.76 minutes off the optimal schedule. Further, iProgram yields a daily energy saving of 0.42kWh on average across the 100 homes. Utilities may use iProgram to recommend thermostat schedules to customers and provide them estimates of potential energy savings in their energy bills.

Dadkhahi, Hamid, Nazir Saleheen, Santosh Kumar, and Benjamin Marlin. "Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications." ICML On Device Intelligence Workshop. 2016. Abstractdadkhahi-icml-odi2017.pdf

The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud systems. We apply our approach to the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data using two and three stage cascades.

Adams, Roy J., and Benjamin M. Marlin. "Learning Time Series Detection Models from Temporally Imprecise Labels." The 20th International Conference on Artificial Intelligence and Statistics. 2017. Abstractadams17a.pdf

In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels commonly occur in areas like mobile health research where human annotators are tasked with labeling the occurrence of very short duration events. We propose a general learning framework for this problem that can accommodate different base classifiers and noise models. We present results on real mobile health data showing that the proposed framework significantly outperforms a number of alternatives including assuming that the label time stamps are noise-free, transforming the problem into the multiple instance learning framework, and learning on labels that were manually re-aligned.

Parate, Abhinav, Meng-Chieh Chiu, Deepak Ganesan, and Benjamin M. Marlin. "Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing." MobiSys. 2013. 83-96. Abstractsensing_mobisys13_paper.pdf

The proliferation of sensors on mobile phones and wearables has led to a plethora of context classifiers designed to sense the individual's context. We argue that a key missing piece in mobile inference is a layer that fuses the outputs of several classifiers to learn deeper insights into an individual's habitual patterns and associated correlations between contexts, thereby enabling new systems optimizations and opportunities. In this paper, we design CQue, a dynamic bayesian network that operates over classifiers for individual contexts, observes relations across these outputs across time, and identifies opportunities for improving energy-efficiency and accuracy by taking advantage of relations. In addition, such a layer provides insights into privacy leakage that might occur when seemingly innocuous user context revealed to different applications on a phone may be combined to reveal more information than originally intended. In terms of system architecture, our key contribution is a clean separation between the detection layer and the fusion layer, enabling classifiers to solely focus on detecting the context, and leverage temporal smoothing and fusion mechanisms to further boost performance by just connecting to our higher-level inference engine. To applications and users, CQue provides a query interface, allowing a) applications to obtain more accurate context results while remaining agnostic of what classifiers/sensors are used and when, and b) users to specify what contexts they wish to keep private, and only allow information that has low leakage with the private context to be revealed. We implemented CQue in Android, and our results show that CQue can i) improve activity classification accuracy up to 42%, ii) reduce energy consumption in classifying social, location and activity contexts with high accuracy(>90%) by reducing the number of required classifiers by at least 33%, and iii) effectively detect and suppress contexts that reveal private information.

Marlin, Benjamin M. "Modeling User Rating Profiles For Collaborative Filtering." NIPS. 2003. Abstract

In this paper we present a generative latent variable model for rating-based collaborative filtering called the User Rating Profile model (URP). The generative process which underlies URP is designed to produce complete user rating profiles, an assignment of one rating to each item for each user. Our model represents each user as a mixture of user attitudes, and the mixing proportions are distributed according to a Dirichlet random variable. The rating for each item is generated by selecting a user attitude for the item, and then selecting a rating according to the preference pattern associated with that attitude. URP is related to several models including a multinomial mixture model, the aspect model, and LDA, but has clear advantages over each.

Marlin, Benjamin M., and Richard S. Zemel. "The multiple multiplicative factor model for collaborative filtering." ICML. 2004. Abstract

We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of factors that have discrete, non-negative expression levels. Each factor proposes a distribution over the data vector. The distinguishing feature of MMFs is that they combine the factors' proposed distributions multiplicatively, taking into account factor expression levels. The product formulation of MMFs allow factors to specialize to a subset of the items, while the causal generative semantics mean MMFs can readily accommodate missing data. This makes MMFs distinct from both directed models with mixture semantics and undirected product models. In this paper we present empirical results from the collaborative filtering domain showing that a binary/multinomial MMF model matches the performance of the best existing models while learning an interesting latent space description of the users.

Duvenaud, David K., Benjamin M. Marlin, and Kevin P. Murphy. "Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification." CRV. 2011. 371-378. Abstractmultiscale_crv11_paper.pdf

Motivated by the abundance of images labeled only by their captions, we construct tree-structured multiscale conditional random fields capable of performing semi supervised learning. We show that such caption-only data can in fact increase pixel-level accuracy at test time. In addition, we compare two kinds of tree: the standard one with pair wise potentials, and one based on noisy-or potentials, which better matches the semantics of the recursive partitioning used to create the tree.