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Benjamin M. Marlin
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- affiliation: Department of Computer Science, University of Massachusetts Amherst
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2020 – today
- 2024
- [c75]Maxwell A. Xu, Alexander Moreno, Hui Wei, Benjamin M. Marlin, James Matthew Rehg:
REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning. ICLR 2024 - [i32]Ho Lyun Jeong, Ziqi Wang, Colin Samplawski, Jason Wu, Shiwei Fang, Lance M. Kaplan, Deepak Ganesan, Benjamin M. Marlin, Mani B. Srivastava:
GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors. CoRR abs/2402.14136 (2024) - [i31]Jason Wu, Ziqi Wang, Xiaomin Ouyang, Ho Lyun Jeong, Colin Samplawski, Lance M. Kaplan, Benjamin M. Marlin, Mani B. Srivastava:
FlexLoc: Conditional Neural Networks for Zero-Shot Sensor Perspective Invariance in Object Localization with Distributed Multimodal Sensors. CoRR abs/2406.06796 (2024) - [i30]Hui Wei, Maxwell A. Xu, Colin Samplawski, James M. Rehg, Santosh Kumar, Benjamin M. Marlin:
Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation. CoRR abs/2406.18848 (2024) - 2023
- [c74]Benjamin M. Marlin, Niranjan Suri, Shiwei Fang, Mani B. Srivastava, Colin Samplawski, Ziqi Wang, Maggie B. Wigness:
IoBT-MAX: a Multimodal Analytics eXperimentation Testbed for IoBT Research. MILCOM 2023: 127-132 - [c73]Karine Karine, Predrag V. Klasnja, Susan A. Murphy, Benjamin M. Marlin:
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions. UAI 2023: 1047-1057 - [c72]Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani B. Srivastava, Benjamin M. Marlin:
Heteroskedastic Geospatial Tracking with Distributed Camera Networks. UAI 2023: 1805-1814 - [i29]Karine Karine, Predrag V. Klasnja, Susan A. Murphy, Benjamin M. Marlin:
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions. CoRR abs/2305.09913 (2023) - [i28]Colin Samplawski, Shiwei Fang, Ziqi Wang, Deepak Ganesan, Mani B. Srivastava, Benjamin M. Marlin:
Heteroskedastic Geospatial Tracking with Distributed Camera Networks. CoRR abs/2306.02407 (2023) - [i27]Maxwell A. Xu, Alexander Moreno, Hui Wei, Benjamin M. Marlin, James M. Rehg:
Retrieval-Based Reconstruction For Time-series Contrastive Learning. CoRR abs/2311.00519 (2023) - 2022
- [c71]Karine Tung, Steven De La Torre, Mohamed El Mistiri, Rebecca Braga De Braganca, Eric B. Hekler, Misha Pavel, Daniel E. Rivera, Pedja Klasnja, Donna Spruijt-Metz, Benjamin M. Marlin:
BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data. CHASE 2022: 78-90 - [c70]Meet P. Vadera, Colin Samplawski, Benjamin M. Marlin:
Uncertainty Quantification Using Query-Based Object Detectors. ECCV Workshops (8) 2022: 78-93 - [c69]Satya Narayan Shukla, Benjamin M. Marlin:
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series. ICLR 2022 - [c68]Meet P. Vadera, Jinyang Li, Adam D. Cobb, Brian Jalaian, Tarek F. Abdelzaher, Benjamin M. Marlin:
URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods. MLSys 2022 - [c67]Shiwei Fang, Ankur Sarker, Ziqi Wang, Mani B. Srivastava, Benjamin M. Marlin, Deepak Ganesan:
Design and Deployment of a Multi-Modal Multi-Node Sensor Data Collection Platform. SenSys 2022: 1041-1046 - [i26]Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin:
Impact of Parameter Sparsity on Stochastic Gradient MCMC Methods for Bayesian Deep Learning. CoRR abs/2202.03770 (2022) - [i25]Karine Tung, Steven De La Torre, Mohamed El Mistiri, Rebecca Braga De Braganca, Eric B. Hekler, Misha Pavel, Daniel E. Rivera, Pedja Klasnja, Donna Spruijt-Metz, Benjamin M. Marlin:
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data. CoRR abs/2209.05581 (2022) - 2021
- [c66]Meet P. Vadera, Benjamin M. Marlin:
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems. CogMI 2021: 252-261 - [c65]Satya Narayan Shukla, Benjamin M. Marlin:
Multi-Time Attention Networks for Irregularly Sampled Time Series. ICLR 2021 - [c64]Shiwei Fang, Jin Huang, Colin Samplawski, Deepak Ganesan, Benjamin M. Marlin, Tarek F. Abdelzaher, Maggie B. Wigness:
Optimizing Intelligent Edge-clouds with Partitioning, Compression and Speculative Inference. MILCOM 2021: 892-896 - [c63]Colin Samplawski, Benjamin M. Marlin:
Towards Transformer-Based Real-Time Object Detection at the Edge: A Benchmarking Study. MILCOM 2021: 898-903 - [c62]Jinyang Li, Runyu Ma, Vikram Sharma Mailthody, Colin Samplawski, Benjamin M. Marlin, Songqing Chen, Shuochao Yao, Tarek F. Abdelzaher:
Towards an Accurate Latency Model for Convolutional Neural Network Layers on GPUs. MILCOM 2021: 904-909 - [c61]Meet P. Vadera, Soumya Ghosh, Kenney Ng, Benjamin M. Marlin:
Post-hoc loss-calibration for Bayesian neural networks. UAI 2021: 1403-1412 - [i24]Satya Narayan Shukla, Benjamin M. Marlin:
Multi-Time Attention Networks for Irregularly Sampled Time Series. CoRR abs/2101.10318 (2021) - [i23]Meet P. Vadera, Soumya Ghosh, Kenney Ng, Benjamin M. Marlin:
Post-hoc loss-calibration for Bayesian neural networks. CoRR abs/2106.06997 (2021) - [i22]Satya Narayan Shukla, Benjamin M. Marlin:
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series. CoRR abs/2107.11350 (2021) - [i21]Meet P. Vadera, Benjamin M. Marlin:
Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems. CoRR abs/2112.01675 (2021) - 2020
- [j11]Omer T. Inan, P. Tenaerts, Sheila A. Prindiville, H. R. Reynolds, D. S. Dizon, K. Cooper-Arnold, Mintu P. Turakhia, Mark J. Pletcher, Kenzie L. Preston, Harlan M. Krumholz, Benjamin M. Marlin, Kenneth D. Mandl, Predrag V. Klasnja, Bonnie Spring, Erin Iturriaga, R. Campo, P. Desvigne-Nickens, Y. Rosenberg, Steven R. Steinhubl, Robert M. Califf:
Digitizing clinical trials. npj Digit. Medicine 3 (2020) - [c60]Benjamin M. Marlin, Tarek F. Abdelzaher, Gabriela F. Ciocarlie, Adam D. Cobb, Mark Dennison, Brian Jalaian, Lance M. Kaplan, Tiffany Raber, Adrienne Raglin, Piyush K. Sharma, Mani B. Srivastava, Theron Trout, Meet P. Vadera, Maggie B. Wigness:
On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems. CogMI 2020: 82-91 - [c59]Colin Samplawski, Erik G. Learned-Miller, Heesung Kwon, Benjamin M. Marlin:
Zero-Shot Learning in the Presence of Hierarchically Coarsened Labels. CVPR Workshops 2020: 4015-4019 - [c58]Steven Cheng-Xian Li, Benjamin M. Marlin:
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective. ICML 2020: 5937-5946 - [c57]Jin Huang, Colin Samplawski, Deepak Ganesan, Benjamin M. Marlin, Heesung Kwon:
CLIO: enabling automatic compilation of deep learning pipelines across IoT and cloud. MobiCom 2020: 58:1-58:12 - [c56]Colin Samplawski, Jin Huang, Deepak Ganesan, Benjamin M. Marlin:
Towards Objection Detection Under IoT Resource Constraints: Combining Partitioning, Slicing and Compression. AIChallengeIoT@SenSys 2020: 14-20 - [c55]Meet P. Vadera, Brian Jalaian, Benjamin M. Marlin:
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks. UAI 2020: 719-728 - [i20]Meet P. Vadera, Satya Narayan Shukla, Brian Jalaian, Benjamin M. Marlin:
Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification. CoRR abs/2002.02842 (2020) - [i19]Satya Narayan Shukla, Benjamin M. Marlin:
Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction. CoRR abs/2003.11059 (2020) - [i18]Meet P. Vadera, Brian Jalaian, Benjamin M. Marlin:
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks. CoRR abs/2005.08110 (2020) - [i17]Meet P. Vadera, Adam D. Cobb, Brian Jalaian, Benjamin M. Marlin:
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks. CoRR abs/2007.04466 (2020) - [i16]Steven Cheng-Xian Li, Benjamin M. Marlin:
Learning from Irregularly-Sampled Time Series: A Missing Data Perspective. CoRR abs/2008.07599 (2020) - [i15]Satya Narayan Shukla, Benjamin M. Marlin:
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series: From Discretization to Attention and Invariance. CoRR abs/2012.00168 (2020)
2010 – 2019
- 2019
- [j10]Srinivasan Iyengar, Sandeep Kalra, Anushree Ghosh, David E. Irwin, Prashant J. Shenoy, Benjamin M. Marlin:
Inferring Smart Schedules for Dumb Thermostats. ACM Trans. Cyber Phys. Syst. 3(2): 17:1-17:29 (2019) - [j9]Nicholas Jacek, Meng-Chieh Chiu, Benjamin M. Marlin, J. Eliot B. Moss:
Optimal Choice of When to Garbage Collect. ACM Trans. Program. Lang. Syst. 41(1): 3:1-3:35 (2019) - [c54]Annamalai Natarajan, Deepak Ganesan, Benjamin M. Marlin:
Hierarchical Active Learning for Model Personalization in the Presence of Label Scarcity. BSN 2019: 1-4 - [c53]Benjamin M. Marlin, Meet P. Vadera:
Poster Abstract: Investigating Fusion-Based Deep Learning Architectures for Smoking Puff Detection. CHASE 2019: 11-12 - [c52]Soha Rostaminia, Addison Mayberry, Deepak Ganesan, Benjamin M. Marlin, Jeremy Gummeson:
iLid: eyewear solution for low-power fatigue and drowsiness monitoring. ETRA 2019: 62:1-62:3 - [c51]Steven Cheng-Xian Li, Bo Jiang, Benjamin M. Marlin:
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks. ICLR (Poster) 2019 - [c50]Satya Narayan Shukla, Benjamin M. Marlin:
Interpolation-Prediction Networks for Irregularly Sampled Time Series. ICLR (Poster) 2019 - [i14]Colin Samplawski, Heesung Kwon, Erik G. Learned-Miller, Benjamin M. Marlin:
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification. CoRR abs/1902.05492 (2019) - [i13]Steven Cheng-Xian Li, Bo Jiang, Benjamin M. Marlin:
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks. CoRR abs/1902.09599 (2019) - [i12]Meet P. Vadera, Benjamin M. Marlin:
Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty. CoRR abs/1906.01724 (2019) - [i11]Satya Narayan Shukla, Benjamin M. Marlin:
Interpolation-Prediction Networks for Irregularly Sampled Time Series. CoRR abs/1909.07782 (2019) - 2018
- [j8]Tarek F. Abdelzaher, Nora Ayanian, Tamer Basar, Suhas N. Diggavi, Jana Diesner, Deepak Ganesan, Ramesh Govindan, Susmit Jha, Tancrède Lepoint, Benjamin M. Marlin, Klara Nahrstedt, David M. Nicol, Raj Rajkumar, Stephen Russell, Sanjit A. Seshia, Fei Sha, Prashant J. Shenoy, Mani B. Srivastava, Gaurav S. Sukhatme, Ananthram Swami, Paulo Tabuada, Don Towsley, Nitin H. Vaidya, Venugopal V. Veeravalli:
Toward an Internet of Battlefield Things: A Resilience Perspective. Computer 51(11): 24-36 (2018) - [c49]Tarek F. Abdelzaher, Nora Ayanian, Tamer Basar, Suhas N. Diggavi, Jana Diesner, Deepak Ganesan, Ramesh Govindan, Susmit Jha, Tancrède Lepoint, Benjamin M. Marlin, Klara Nahrstedt, David M. Nicol, Raj Rajkumar, Stephen Russell, Sanjit A. Seshia, Fei Sha, Prashant J. Shenoy, Mani B. Srivastava, Gaurav S. Sukhatme, Ananthram Swami, Paulo Tabuada, Don Towsley, Nitin H. Vaidya, Venugopal V. Veeravalli:
Will Distributed Computing Revolutionize Peace? The Emergence of Battlefield IoT. ICDCS 2018: 1129-1138 - [c48]Ahmed Ali-Eldin, Deepak Ganesan, Heesung Kwon, Benjamin M. Marlin, Prashant J. Shenoy, Mani B. Srivastava, Don Towsley:
Executing Analytics and Fusion Workloads on Transient Computing Resources in Tactical Environments. MILCOM 2018: 1-9 - [c47]Roy Adams, Benjamin M. Marlin:
Learning Time Series Segmentation Models from Temporally Imprecise Labels. UAI 2018: 135-144 - [i10]Satya Narayan Shukla, Benjamin M. Marlin:
Modeling Irregularly Sampled Clinical Time Series. CoRR abs/1812.00531 (2018) - 2017
- [j7]Soha Rostaminia, Addison Mayberry, Deepak Ganesan, Benjamin M. Marlin, Jeremy Gummeson:
iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(2): 23:1-23:26 (2017) - [j6]Santosh Kumar, Gregory D. Abowd, William T. Abraham, Mustafa al'Absi, Duen Horng Chau, Emre Ertin, Deborah Estrin, Deepak Ganesan, Timothy Hnat, Syed Monowar Hossain, Zachary G. Ives, Jacqueline Kerr, Benjamin M. Marlin, Susan A. Murphy, James M. Rehg, Inbal Nahum-Shani, Vivek Shetty, Ida Sim, Bonnie Spring, Mani B. Srivastava, David W. Wetter:
Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K). IEEE Pervasive Comput. 16(2): 18-22 (2017) - [j5]Hamid Dadkhahi, Marco F. Duarte, Benjamin M. Marlin:
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series. IEEE Trans. Image Process. 26(11): 5435-5446 (2017) - [c46]Roy J. Adams, Benjamin M. Marlin:
Learning Time Series Detection Models from Temporally Imprecise Labels. AISTATS 2017: 157-165 - [c45]Hamid Dadkhahi, Benjamin M. Marlin:
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices. KDD 2017: 1773-1781 - 2016
- [j4]Addison Mayberry, Yamin Tun, Pan Hu, Duncan Smith-Freedman, Deepak Ganesan, Benjamin M. Marlin, Christopher D. Salthouse:
THE "I" IN THE EYE. GetMobile Mob. Comput. Commun. 20(2): 27-30 (2016) - [c44]Addison Mayberry, Yamin Tun, Pan Hu, Duncan Smith-Freedman, Benjamin M. Marlin, Christopher D. Salthouse, Deepak Ganesan:
CIDER: enhancing the performance of computational eyeglasses. ETRA 2016: 313-314 - [c43]Annamalai Natarajan, Gustavo Angarita, Edward Gaiser, Robert Malison, Deepak Ganesan, Benjamin M. Marlin:
Domain adaptation methods for improving lab-to-field generalization of cocaine detection using wearable ECG. UbiComp 2016: 875-885 - [c42]Laura Hiatt, Roy J. Adams, Benjamin M. Marlin:
An Improved Data Representation for Smoking Detection with Wearable Respiration Sensors. ICHI 2016: 409 - [c41]Roy J. Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, Benjamin M. Marlin:
Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams. ICML 2016: 334-343 - [c40]Steven Cheng-Xian Li, Benjamin M. Marlin:
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification. NIPS 2016: 1804-1812 - [c39]Nicholas Jacek, Meng-Chieh Chiu, Benjamin M. Marlin, Eliot Moss:
Assessing the limits of program-specific garbage collection performance. PLDI 2016: 584-598 - [c38]Meng-Chieh Chiu, Benjamin M. Marlin, Eliot Moss:
Real-Time Program-Specific Phase Change Detection for Java Programs. PPPJ 2016: 12:1-12:11 - [c37]Thai Nguyen, Roy J. Adams, Annamalai Natarajan, Benjamin M. Marlin:
Parsing wireless electrocardiogram signals with context free grammar conditional random fields. Wireless Health 2016: 149-156 - [i9]Steven Cheng-Xian Li, Benjamin M. Marlin:
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification. CoRR abs/1606.04443 (2016) - [i8]Hamid Dadkhahi, Marco F. Duarte, Benjamin M. Marlin:
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series. CoRR abs/1606.08282 (2016) - [i7]Hamid Dadkhahi, Nazir Saleheen, Santosh Kumar, Benjamin M. Marlin:
Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications. CoRR abs/1607.03730 (2016) - [i6]Hamid Dadkhahi, Benjamin M. Marlin:
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices. CoRR abs/1608.00159 (2016) - [i5]Roy J. Adams, Benjamin M. Marlin:
Learning Time Series Detection Models from Temporally Imprecise Labels. CoRR abs/1611.02258 (2016) - 2015
- [j3]Haibin Huang, Evangelos Kalogerakis, Benjamin M. Marlin:
Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces. Comput. Graph. Forum 34(5): 25-38 (2015) - [j2]Santosh Kumar, Gregory D. Abowd, William T. Abraham, Mustafa al'Absi, J. Gayle Beck, Duen Horng Chau, Tyson Condie, David E. Conroy, Emre Ertin, Deborah Estrin, Deepak Ganesan, Cho Lam, Benjamin M. Marlin, Clay B. Marsh, Susan A. Murphy, Inbal Nahum-Shani, Kevin Patrick, James M. Rehg, Moushumi Sharmin, Vivek Shetty, Ida Sim, Bonnie Spring, Mani B. Srivastava, David W. Wetter:
Center of excellence for mobile sensor data-to-knowledge (MD2K). J. Am. Medical Informatics Assoc. 22(6): 1137-1142 (2015) - [c36]Nazir Saleheen, Amin Ahsan Ali, Syed Monowar Hossain, Hillol Sarker, Soujanya Chatterjee, Benjamin M. Marlin, Emre Ertin, Mustafa al'Absi, Santosh Kumar:
puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. UbiComp 2015: 999-1010 - [c35]Hamid Dadkhahi, Marco F. Duarte, Benjamin M. Marlin:
Isomap out-of-sample extension for noisy time series data. MLSP 2015: 1-6 - [c34]Addison Mayberry, Yamin Tun, Pan Hu, Duncan Smith-Freedman, Deepak Ganesan, Benjamin M. Marlin, Christopher D. Salthouse:
CIDER: Enabling Robustness-Power Tradeoffs on a Computational Eyeglass. MobiCom 2015: 400-412 - [c33]Srinivasan Iyengar, Sandeep Kalra, Anushree Ghosh, David E. Irwin, Prashant J. Shenoy, Benjamin M. Marlin:
iProgram: Inferring Smart Schedules for Dumb Thermostats. BuildSys@SenSys 2015: 211-220 - [c32]Steven Cheng-Xian Li, Benjamin M. Marlin:
Classification of Sparse and Irregularly Sampled Time Series with Mixtures of Expected Gaussian Kernels and Random Features. UAI 2015: 484-493 - 2014
- [c31]Annamalai Natarajan, Edward Gaiser, Gustavo Angarita, Robert Malison, Deepak Ganesan, Benjamin M. Marlin:
Conditional random fields for morphological analysis of wireless ECG signals. BCB 2014: 370-379 - [c30]Andrew Kae, Benjamin M. Marlin, Erik G. Learned-Miller:
The Shape-Time Random Field for Semantic Video Labeling. CVPR 2014: 272-279 - [c29]Addison Mayberry, Pan Hu, Benjamin M. Marlin, Christopher D. Salthouse, Deepak Ganesan:
iShadow: the computational eyeglass system. ETRA 2014: 359-360 - [c28]Addison Mayberry, Pan Hu, Benjamin M. Marlin, Christopher D. Salthouse, Deepak Ganesan:
iShadow: design of a wearable, real-time mobile gaze tracker. MobiSys 2014: 82-94 - [c27]Roy J. Adams, Rajani S. Sadasivam, Kavitha Balakrishnan, Rebecca L. Kinney, Thomas K. Houston, Benjamin M. Marlin:
PERSPeCT: collaborative filtering for tailored health communications. RecSys 2014: 329-332 - 2013
- [c26]Benjamin M. Marlin, Roy J. Adams, Rajani S. Sadasivam, Thomas K. Houston:
Towards Collaborative Filtering Recommender Systems for Tailored Health Communications. AMIA 2013 - [c25]Annamalai Natarajan, Abhinav Parate, Edward Gaiser, Gustavo Angarita, Robert Malison, Benjamin M. Marlin, Deepak Ganesan:
Detecting Signatures of Cocaine Using On-Body Sensors. AMIA 2013 - [c24]Annamalai Natarajan, Abhinav Parate, Edward Gaiser, Gustavo Angarita, Robert Malison, Benjamin M. Marlin, Deepak Ganesan:
Detecting cocaine use with wearable electrocardiogram sensors. UbiComp 2013: 123-132 - [c23]Abhinav Parate, Matthias Böhmer, David Chu, Deepak Ganesan, Benjamin M. Marlin:
Practical prediction and prefetch for faster access to applications on mobile phones. UbiComp 2013: 275-284 - [c22]Abhinav Parate, Meng-Chieh Chiu, Deepak Ganesan, Benjamin M. Marlin:
Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing. MobiSys 2013: 83-96 - [c21]Sebastian Riedel, Limin Yao, Andrew McCallum, Benjamin M. Marlin:
Relation Extraction with Matrix Factorization and Universal Schemas. HLT-NAACL 2013: 74-84 - 2012
- [c20]Benjamin M. Marlin, David C. Kale, Robinder G. Khemani, Randall C. Wetzel:
Unsupervised pattern discovery in electronic health care data using probabilistic clustering models. IHI 2012: 389-398 - [c19]Mohammad Emtiyaz Khan, Shakir Mohamed, Benjamin M. Marlin, Kevin P. Murphy:
A Stick-Breaking Likelihood for Categorical Data Analysis with Latent Gaussian Models. AISTATS 2012: 610-618 - [i4]Benjamin M. Marlin, Nando de Freitas:
Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood. CoRR abs/1202.3746 (2012) - [i3]Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy:
Group Sparse Priors for Covariance Estimation. CoRR abs/1205.2626 (2012) - [i2]Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney:
Collaborative Filtering and the Missing at Random Assumption. CoRR abs/1206.5267 (2012) - [i1]Craig Boutilier, Richard S. Zemel, Benjamin M. Marlin:
Active Collaborative Filtering. CoRR abs/1212.2442 (2012) - 2011
- [c18]David Duvenaud, Benjamin M. Marlin, Kevin P. Murphy:
Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification. CRV 2011: 371-378 - [c17]Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy:
Piecewise Bounds for Estimating Bernoulli-Logistic Latent Gaussian Models. ICML 2011: 633-640 - [c16]Kevin Swersky, Marc'Aurelio Ranzato, David Buchman, Benjamin M. Marlin, Nando de Freitas:
On Autoencoders and Score Matching for Energy Based Models. ICML 2011: 1201-1208 - [c15]Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney:
Recommender Systems, Missing Data and Statistical Model Estimation. IJCAI 2011: 2686-2691 - [c14]Benjamin M. Marlin, Nando de Freitas:
Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood. UAI 2011: 497-505 - 2010
- [c13]Kevin Swersky, Bo Chen, Benjamin M. Marlin, Nando de Freitas:
A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets. ITA 2010: 80-89 - [c12]Mohammad Emtiyaz Khan, Benjamin M. Marlin, Guillaume Bouchard, Kevin P. Murphy:
Variational bounds for mixed-data factor analysis. NIPS 2010: 1108-1116 - [c11]Benjamin M. Marlin, Kevin Swersky, Bo Chen, Nando de Freitas:
Inductive Principles for Restricted Boltzmann Machine Learning. AISTATS 2010: 509-516
2000 – 2009
- 2009
- [c10]Benjamin M. Marlin, Kevin P. Murphy:
Sparse Gaussian graphical models with unknown block structure. ICML 2009: 705-712 - [c9]Baback Moghaddam, Benjamin M. Marlin, Mohammad Emtiyaz Khan, Kevin P. Murphy:
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models. NIPS 2009: 1285-1293 - [c8]Benjamin M. Marlin, Richard S. Zemel:
Collaborative prediction and ranking with non-random missing data. RecSys 2009: 5-12 - [c7]Benjamin M. Marlin, Mark Schmidt, Kevin P. Murphy:
Group Sparse Priors for Covariance Estimation. UAI 2009: 383-392 - 2008
- [b1]Benjamin M. Marlin:
Missing Data Problems in Machine Learning. University of Toronto, Canada, 2008 - 2007
- [c6]Benjamin M. Marlin, Richard S. Zemel, Sam T. Roweis, Malcolm Slaney:
Collaborative Filtering and the Missing at Random Assumption. UAI 2007: 267-275 - 2005
- [c5]