Bilmes, Jeff A.

59 publications

AAAI 2022 PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff A. Bilmes, Rishabh K. Iyer

NeurIPS 2022 Retrospective Adversarial Replay for Continual Learning Lilly Kumari, Shengjie Wang, Tianyi Zhou, Jeff A Bilmes

NeurIPS 2021 Constrained Robust Submodular Partitioning Shengjie Wang, Tianyi Zhou, Chandrashekhar Lavania, Jeff A Bilmes

AAAI 2021 Submodular Span, with Applications to Conditional Data Summarization Lilly Kumari, Jeff A. Bilmes

NeurIPS 2019 On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks Sunil Thulasidasan, Gopinath Chennupati, Jeff A. Bilmes, Tanmoy Bhattacharya, Sarah Michalak

NeurIPS 2018 Diverse Ensemble Evolution: Curriculum Data-Model Marriage Tianyi Zhou, Shengjie Wang, Jeff A. Bilmes

NeurIPS 2018 Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions Wenruo Bai, William Stafford Noble, Jeff A. Bilmes

AISTATS 2017 Scaling Submodular Maximization via Pruned Submodularity Graphs Tianyi Zhou, Hua Ouyang, Jeff A. Bilmes, Yi Chang, Carlos Guestrin

ICLR 2017 Training Compressed Fully-Connected Networks with a Density-Diversity Penalty Shengjie Wang, Haoran Cai, Jeff A. Bilmes, William S. Noble

NeurIPS 2016 Deep Submodular Functions: Definitions and Learning Brian W Dolhansky, Jeff A. Bilmes

NeurIPS 2015 Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications Kai Wei, Rishabh K Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes

AISTATS 2015 On Approximate Non-Submodular Minimization via Tree-Structured Supermodularity Yoshinobu Kawahara, Rishabh K. Iyer, Jeff A. Bilmes

NeurIPS 2015 Submodular Hamming Metrics Jennifer A Gillenwater, Rishabh K Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes

AISTATS 2015 Submodular Point Processes with Applications to Machine Learning Rishabh K. Iyer, Jeff A. Bilmes

NeurIPS 2014 Divide-and-Conquer Learning by Anchoring a Conical Hull Tianyi Zhou, Jeff A. Bilmes, Carlos Guestrin

NeurIPS 2014 Learning Mixtures of Submodular Functions for Image Collection Summarization Sebastian Tschiatschek, Rishabh K Iyer, Haochen Wei, Jeff A. Bilmes

UAI 2014 Learning Peptide-Spectrum Alignment Models for Tandem Mass Spectrometry John T. Halloran, Jeff A. Bilmes, William Stafford Noble

UAI 2014 Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes

NeurIPS 2013 Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K Iyer, Stefanie Jegelka, Jeff A. Bilmes

NeurIPS 2013 Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K Iyer, Jeff A. Bilmes

UAI 2013 The Lovasz-Bregman Divergence and Connections to Rank Aggregation, Clustering, and Web Ranking Rishabh K. Iyer, Jeff A. Bilmes

UAI 2012 Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications Rishabh K. Iyer, Jeff A. Bilmes

UAI 2012 Learning Mixtures of Submodular Shells with Application to Document Summarization Hui Lin, Jeff A. Bilmes

UAI 2012 Spectrum Identification Using a Dynamic Bayesian Network Model of Tandem Mass Spectra Ajit P. Singh, John T. Halloran, Jeff A. Bilmes, Katrin Kirchhoff, William Stafford Noble

NeurIPS 2012 Submodular-Bregman and the Lovász-Bregman Divergences with Applications Rishabh Iyer, Jeff A. Bilmes

UAI 2011 Active Semi-Supervised Learning Using Submodular Functions Andrew Guillory, Jeff A. Bilmes

ICML 2011 Approximation Bounds for Inference Using Cooperative Cuts Stefanie Jegelka, Jeff A. Bilmes

MLJ 2011 Creating Non-Minimal Triangulations for Use in Inference in Mixed Stochastic/deterministic Graphical Models Chris D. Bartels, Jeff A. Bilmes

NeurIPS 2011 On Fast Approximate Submodular Minimization Stefanie Jegelka, Hui Lin, Jeff A. Bilmes

ICML 2011 Online Submodular Minimization for Combinatorial Structures Stefanie Jegelka, Jeff A. Bilmes

NeurIPS 2011 Online Submodular Set Cover, Ranking, and Repeated Active Learning Andrew Guillory, Jeff A. Bilmes

ICML 2011 Simultaneous Learning and Covering with Adversarial Noise Andrew Guillory, Jeff A. Bilmes

CVPR 2011 Submodularity Beyond Submodular Energies: Coupling Edges in Graph Cuts Stefanie Jegelka, Jeff A. Bilmes

AAAI 2010 Discovering Long Range Properties of Social Networks with Multi-Valued Time-Inhomogeneous Models Danny Wyatt, Tanzeem Choudhury, Jeff A. Bilmes

JMLR 2010 Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers Franz Pernkopf, Jeff A. Bilmes

ICML 2010 Interactive Submodular Set Cover Andrew Guillory, Jeff A. Bilmes

ALT 2009 Average-Case Active Learning with Costs Andrew Guillory, Jeff A. Bilmes

NeurIPS 2009 Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification Amarnag Subramanya, Jeff A. Bilmes

NeurIPS 2009 Label Selection on Graphs Andrew Guillory, Jeff A. Bilmes

NeurIPS 2009 Submodularity Cuts and Applications Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes

UAI 2009 UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009 Jeff A. Bilmes, Andrew Y. Ng

AAAI 2008 Learning Hidden Curved Exponential Family Models to Infer Face-to-Face Interaction Networks from Situated Speech Data Danny Wyatt, Tanzeem Choudhury, Jeff A. Bilmes

AAAI 2008 Structure Learning on Large Scale Common Sense Statistical Models of Human State William Pentney, Matthai Philipose, Jeff A. Bilmes

IJCAI 2007 A Privacy-Sensitive Approach to Modeling Multi-Person Conversations Danny Wyatt, Tanzeem Choudhury, Jeff A. Bilmes, Henry A. Kautz

UAI 2007 Consensus Ranking Under the Exponential Model Marina Meila, Kapil Phadnis, Arthur Patterson, Jeff A. Bilmes

AAAI 2007 Learning Large Scale Common Sense Models of Everyday Life William Pentney, Matthai Philipose, Jeff A. Bilmes, Henry A. Kautz

IJCAI 2007 Local Search for Balanced Submodular Clusterings Mukund Narasimhan, Jeff A. Bilmes

NeurIPS 2006 Multi-Dynamic Bayesian Networks Karim Filali, Jeff A. Bilmes

UAI 2006 Non-Minimal Triangulations for Mixed Stochastic/Deterministic Graphical Models Chris D. Bartels, Jeff A. Bilmes

UAI 2006 Recognizing Activities and Spatial Context Using Wearable Sensors Amar Subramanya, Alvin Raj, Jeff A. Bilmes, Dieter Fox

ICCV 2005 A Generative/Discriminative Learning Algorithm for Image Classification Yi Li, Linda G. Shapiro, Jeff A. Bilmes

UAI 2005 A Submodular-Supermodular Procedure with Applications to Discriminative Structure Learning Mukund Narasimhan, Jeff A. Bilmes

ICML 2005 Discriminative Versus Generative Parameter and Structure Learning of Bayesian Network Classifiers Franz Pernkopf, Jeff A. Bilmes

NeurIPS 2005 Q-Clustering Mukund Narasimhan, Nebojsa Jojic, Jeff A. Bilmes

NeurIPS 2004 Optimal Sub-Graphical Models Mukund Narasimhan, Jeff A. Bilmes

UAI 2004 PAC-Learning Bounded Tree-Width Graphical Models Mukund Narasimhan, Jeff A. Bilmes

NeurIPS 2003 Necessary Intransitive Likelihood-Ratio Classifiers Gang Ji, Jeff A. Bilmes

UAI 2003 On Triangulating Dynamic Graphical Models Jeff A. Bilmes, Chris D. Bartels

UAI 2000 Dynamic Bayesian Multinets Jeff A. Bilmes