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2020 – today
- 2024
[j9]Wenjie Li
, Haoze Li, Qifan Song, Jean Honorio:
PyXAB - A Python Library for \mathcal{X}-Armed Bandit and Online Blackbox Optimization Algorithms. J. Open Source Softw. 9(102): 6507 (2024)- [j8]Site Bai, Chuyang Ke, Jean Honorio:
On the Dual Problem of Convexified Convolutional Neural Networks. Trans. Mach. Learn. Res. 2024 (2024) - [j7]Adarsh Barik, Jean Honorio:
Recovering Exact Support in Federated lasso without Optimization. Trans. Mach. Learn. Res. 2024 (2024) - [j6]Chuyang Ke, Jean Honorio:
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns. Trans. Mach. Learn. Res. 2024 (2024) - [c67]Wenjie Li, Qifan Song, Jean Honorio, Guang Lin:
Federated X-armed Bandit. AAAI 2024: 13628-13636 - [c66]Wenjie Li, Qifan Song, Jean Honorio:
Personalized Federated X-armed Bandit. AISTATS 2024: 37-45 - 2023
- [c65]Qiuling Xu, Guanhong Tao, Jean Honorio, Yingqi Liu, Shengwei An, Guangyu Shen, Siyuan Cheng, Xiangyu Zhang:
MEDIC: Remove Model Backdoors via Importance Driven Cloning. CVPR 2023: 20485-20494 - [c64]Adarsh Barik, Jean Honorio:
Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games. ICASSP 2023: 1-5 - [c63]Chuyang Ke, Jean Honorio:
Exact Inference in High-order Structured Prediction. ICML 2023: 16152-16167 - [i75]Shixiong Wang, Haowei Wang, Jean Honorio:
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity. CoRR abs/2301.13565 (2023) - [i74]Hanbyul Lee, Qifan Song, Jean Honorio:
Support Recovery in Sparse PCA with Non-Random Missing Data. CoRR abs/2302.01535 (2023) - [i73]Chuyang Ke, Jean Honorio:
Exact Inference in High-order Structured Prediction. CoRR abs/2302.03236 (2023) - [i72]Wenjie Li, Haoze Li, Jean Honorio, Qifan Song:
PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms. CoRR abs/2303.04030 (2023) - [i71]Hanbyul Lee, Rahul Mazumder, Qifan Song, Jean Honorio:
Matrix Completion from General Deterministic Sampling Patterns. CoRR abs/2306.02283 (2023) - [i70]Chuyang Ke, Jean Honorio:
Partial Inference in Structured Prediction. CoRR abs/2306.03949 (2023) - [i69]Adarsh Barik, Jean Honorio:
Outlier-robust Estimation of a Sparse Linear Model Using Invexity. CoRR abs/2306.12678 (2023) - [i68]Adarsh Barik, Suvrit Sra, Jean Honorio:
Invex Programs: First Order Algorithms and Their Convergence. CoRR abs/2307.04456 (2023) - [i67]Wenjie Li, Qifan Song, Jean Honorio:
Personalized Federated X -armed Bandit. CoRR abs/2310.16323 (2023) - 2022
- [j5]Chuyang Ke, Jean Honorio:
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation. J. Mach. Learn. Res. 23: 284:1-284:28 (2022) - [c62]Kevin Bello, Chuyang Ke, Jean Honorio:
A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. AISTATS 2022: 640-654 - [c61]Chuyang Ke, Jean Honorio:
Federated Myopic Community Detection with One-shot Communication. AISTATS 2022: 4937-4954 - [c60]Chuyang Ke, Jean Honorio:
Exact Partitioning of High-Order Planted Models with A Tensor Nuclear Norm Constraint. ICASSP 2022: 4428-4432 - [c59]Adarsh Barik, Jean Honorio:
Provable Sample Complexity Guarantees For Learning Of Continuous-Action Graphical Games With Nonparametric Utilities. ICASSP 2022: 4443-4447 - [c58]Adarsh Barik, Jean Honorio:
Information Theoretic Limits For Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity. ICASSP 2022: 5173-5177 - [c57]Adarsh Barik, Jean Honorio:
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation. ICML 2022: 1627-1646 - [c56]Wenjie Li, Adarsh Barik, Jean Honorio:
A Simple Unified Framework for High Dimensional Bandit Problems. ICML 2022: 12619-12655 - [c55]Hanbyul Lee, Kevin Bello, Jean Honorio:
On the Fundamental Limits of Exact Inference in Structured Prediction. ISIT 2022: 3174-3179 - [c54]Hanbyul Lee, Qifan Song, Jean Honorio:
Support Recovery in Sparse PCA with Incomplete Data. NeurIPS 2022 - [i66]Site Bai, Chuyang Ke, Jean Honorio:
Dual Convexified Convolutional Neural Networks. CoRR abs/2205.14056 (2022) - [i65]Hanbyul Lee, Qifan Song, Jean Honorio:
Support Recovery in Sparse PCA with Incomplete Data. CoRR abs/2205.15215 (2022) - [i64]Wenjie Li, Qifan Song, Jean Honorio, Guang Lin:
Federated X-Armed Bandit. CoRR abs/2205.15268 (2022) - [i63]Adarsh Barik, Jean Honorio:
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation. CoRR abs/2206.01167 (2022) - [i62]Chuyang Ke, Jean Honorio:
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns. CoRR abs/2206.04893 (2022) - [i61]Imon Banerjee, Jean Honorio:
Meta Sparse Principal Component Analysis. CoRR abs/2208.08938 (2022) - [i60]Deepak Maurya, Adarsh Barik, Jean Honorio:
A Novel Plug-and-Play Approach for Adversarially Robust Generalization. CoRR abs/2208.09449 (2022) - [i59]Huiming Xie, Jean Honorio:
Meta Learning for High-dimensional Ising Model Selection Using $\ell_1$-regularized Logistic Regression. CoRR abs/2208.09539 (2022) - [i58]Shixiong Wang, Haowei Wang, Jean Honorio:
Distributional Robustness Bounds Generalization Errors. CoRR abs/2212.09962 (2022) - [i57]Deepak Maurya, Jean Honorio:
A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression. CoRR abs/2212.11209 (2022) - 2021
- [c53]Yuki Ohnishi, Jean Honorio:
Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation. AISTATS 2021: 1711-1719 - [c52]Zhanyu Wang, Jean Honorio:
The Sample Complexity of Meta Sparse Regression. AISTATS 2021: 2323-2331 - [c51]Manuel Widmoser, Maria Leonor Pacheco, Jean Honorio, Dan Goldwasser:
Randomized Deep Structured Prediction for Discourse-Level Processing. EACL 2021: 1174-1184 - [c50]Abi Komanduru, Jean Honorio:
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning. ICML 2021: 5676-5685 - [c49]Qian Zhang, Yilin Zheng, Jean Honorio:
Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation. ICML 2021: 12642-12652 - [c48]Donald Q. Adams, Adarsh Barik, Jean Honorio:
Information-Theoretic Bounds for Integral Estimation. ISIT 2021: 742-747 - [c47]Qiuling Xu, Kevin Bello, Jean Honorio:
A Le Cam Type Bound for Adversarial Learning and Applications. ISIT 2021: 1164-1169 - [c46]Zitao Li, Jean Honorio:
Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability. ISIT 2021: 1314-1319 - [c45]Abdulrahman Alabdulkareem, Jean Honorio:
Information-theoretic lower bounds for zero-order stochastic gradient estimation. ISIT 2021: 2316-2321 - [c44]Krishna Reddy Kesari, Jean Honorio:
First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions. ISIT 2021: 2322-2327 - [c43]Jiajun Liang, Chuyang Ke, Jean Honorio:
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection. ISIT 2021: 2578-2583 - [c42]Gregory Dexter, Kevin Bello, Jean Honorio:
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees. NeurIPS 2021: 6972-6982 - [c41]Adarsh Barik, Jean Honorio:
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. NeurIPS 2021: 23245-23257 - [c40]Zhikun Zhang, Tianhao Wang, Ninghui Li, Jean Honorio, Michael Backes, Shibo He, Jiming Chen, Yang Zhang:
PrivSyn: Differentially Private Data Synthesis. USENIX Security Symposium 2021: 929-946 - [i56]Manuel Widmoser, Maria Leonor Pacheco, Jean Honorio, Dan Goldwasser:
Randomized Deep Structured Prediction for Discourse-Level Processing. CoRR abs/2101.10435 (2021) - [i55]Jiajun Liang, Chuyang Ke, Jean Honorio:
Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection. CoRR abs/2101.12369 (2021) - [i54]Gregory Dexter, Kevin Bello, Jean Honorio:
Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees. CoRR abs/2102.07937 (2021) - [i53]Kevin Bello, Chuyang Ke, Jean Honorio:
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. CoRR abs/2102.08019 (2021) - [i52]Hanbyul Lee, Kevin Bello, Jean Honorio:
On the Fundamental Limits of Exact Inference in Structured Prediction. CoRR abs/2102.08895 (2021) - [i51]Wenjie Li, Adarsh Barik, Jean Honorio:
A Simple Unified Framework for High Dimensional Bandit Problems. CoRR abs/2102.09626 (2021) - [i50]Adarsh Barik, Jean Honorio:
Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. CoRR abs/2102.09704 (2021) - [i49]Donald Q. Adams, Adarsh Barik, Jean Honorio:
Information-Theoretic Bounds for Integral Estimation. CoRR abs/2102.10199 (2021) - [i48]Abi Komanduru, Jean Honorio:
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning. CoRR abs/2103.04446 (2021) - [i47]Chuyang Ke, Jean Honorio:
Federated Myopic Community Detection with One-shot Communication. CoRR abs/2106.07255 (2021) - 2020
- [c39]Kevin Bello, Asish Ghoshal, Jean Honorio:
Minimax Bounds for Structured Prediction Based on Factor Graphs. AISTATS 2020: 213-222 - [c38]Adarsh Bank, Jean Honorio:
Provable Efficient Skeleton Learning of Encodable Discrete Bayes Nets in Poly-Time and Sample Complexity. ISIT 2020: 2486-2491 - [c37]Kevin Bello, Jean Honorio:
Fairness constraints can help exact inference in structured prediction. NeurIPS 2020 - [i46]Zhanyu Wang, Jean Honorio:
The Sample Complexity of Meta Sparse Regression. CoRR abs/2002.09587 (2020) - [i45]Yuki Ohnishi, Jean Honorio:
Novel Change of Measure Inequalities and PAC-Bayesian Bounds. CoRR abs/2002.10678 (2020) - [i44]Krishna Reddy Kesari, Jean Honorio:
First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions. CoRR abs/2002.12911 (2020) - [i43]Abdulrahman Alabdulkareem, Jean Honorio:
Information-Theoretic Lower Bounds for Zero-Order Stochastic Gradient Estimation. CoRR abs/2003.13881 (2020) - [i42]Adarsh Barik, Jean Honorio:
Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities. CoRR abs/2004.01022 (2020) - [i41]Chuyang Ke, Jean Honorio:
Exact Partitioning of High-order Planted Models with a Tensor Nuclear Norm Constraint. CoRR abs/2006.11666 (2020) - [i40]Adarsh Barik, Jean Honorio:
Exact Support Recovery in Federated Regression with One-shot Communication. CoRR abs/2006.12583 (2020) - [i39]Qian Zhang, Yilin Zheng, Jean Honorio:
Support Union Recovery in Meta Learning of Gaussian Graphical Models. CoRR abs/2006.12598 (2020) - [i38]Kevin Bello, Jean Honorio:
Fairness constraints can help exact inference in structured prediction. CoRR abs/2007.00218 (2020) - [i37]Kevin Bello, Qiuling Xu, Jean Honorio:
Fundamental Limits of Adversarial Learning. CoRR abs/2007.00289 (2020) - [i36]Xiaochen Yang, Jean Honorio:
Information Theoretic Sample Complexity Lower Bound for Feed-Forward Fully-Connected Deep Networks. CoRR abs/2007.00796 (2020) - [i35]Zhikun Zhang, Tianhao Wang, Ninghui Li, Jean Honorio, Michael Backes, Shibo He, Jiming Chen, Yang Zhang:
PrivSyn: Differentially Private Data Synthesis. CoRR abs/2012.15128 (2020)
2010 – 2019
- 2019
- [c36]Zhaosen Wang, Jean Honorio:
Reconstructing a Bounded-Degree Directed Tree Using Path Queries. Allerton 2019: 506-513 - [c35]Raphael A. Meyer, Jean Honorio:
Optimality Implies Kernel Sum Classifiers are Statistically Efficient. ICML 2019: 4566-4574 - [c34]Longyun Guo, Jean Honorio, John Morgan:
Cost-Aware Learning for Improved Identifiability with Multiple Experiments. ISIT 2019: 1802-1806 - [c33]Kevin Bello, Jean Honorio:
Exact inference in structured prediction. NeurIPS 2019: 3693-3702 - [c32]Abi Komanduru, Jean Honorio:
On the Correctness and Sample Complexity of Inverse Reinforcement Learning. NeurIPS 2019: 7110-7119 - [c31]Adarsh Barik, Jean Honorio:
Learning Bayesian Networks with Low Rank Conditional Probability Tables. NeurIPS 2019: 8962-8971 - [i34]Raphael Arkady Meyer, Jean Honorio:
On the Statistical Efficiency of Optimal Kernel Sum Classifiers. CoRR abs/1901.09087 (2019) - [i33]Chuyang Ke, Jean Honorio:
Exact Recovery in the Latent Space Model. CoRR abs/1902.03099 (2019) - [i32]Adarsh Barik, Jean Honorio:
Learning Bayesian Networks with Low Rank Conditional Probability Tables. CoRR abs/1905.12552 (2019) - [i31]Abi Komanduru, Jean Honorio:
On the Correctness and Sample Complexity of Inverse Reinforcement Learning. CoRR abs/1906.00422 (2019) - [i30]Kevin Bello, Asish Ghoshal, Jean Honorio:
Minimax bounds for structured prediction. CoRR abs/1906.00449 (2019) - [i29]Kevin Bello, Jean Honorio:
Exact inference in structured prediction. CoRR abs/1906.00451 (2019) - [i28]Chuyang Ke, Jean Honorio:
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation. CoRR abs/1911.02161 (2019) - [i27]Adarsh Barik, Jean Honorio:
Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games. CoRR abs/1911.04225 (2019) - 2018
- [c30]Asish Ghoshal, Jean Honorio:
Learning linear structural equation models in polynomial time and sample complexity. AISTATS 2018: 1466-1475 - [c29]Asish Ghoshal, Jean Honorio:
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity. AISTATS 2018: 1486-1494 - [c28]Yixi Xu, Jean Honorio, Xiao Wang:
On the Statistical Efficiency of Compositional Nonparametric Prediction. AISTATS 2018: 1531-1539 - [c27]Meimei Liu, Jean Honorio, Guang Cheng:
Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. Allerton 2018: 1005-1011 - [c26]Asish Ghoshal, Jean Honorio:
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time. ICML 2018: 1749-1757 - [c25]Kevin Bello, Jean Honorio:
Learning latent variable structured prediction models with Gaussian perturbations. NeurIPS 2018: 3149-3159 - [c24]Chuyang Ke, Jean Honorio:
Information-theoretic Limits for Community Detection in Network Models. NeurIPS 2018: 8334-8343 - [c23]Kevin Bello, Jean Honorio:
Computationally and statistically efficient learning of causal Bayes nets using path queries. NeurIPS 2018: 10954-10964 - [i26]Longyun Guo, Jean Honorio, John Morgan:
On the Sample Complexity of Learning from a Sequence of Experiments. CoRR abs/1802.04350 (2018) - [i25]Chuyang Ke, Jean Honorio:
Information-theoretic Limits for Community Detection in Network Models. CoRR abs/1802.06104 (2018) - [i24]Adarsh Barik, Jean Honorio:
Learning Binary Bayesian Networks in Polynomial Time and Sample Complexity. CoRR abs/1803.04087 (2018) - [i23]Zitao Li, Jean Honorio:
Regularized Loss Minimizers with Local Data Obfuscation. CoRR abs/1805.07645 (2018) - [i22]Asish Ghoshal, Jean Honorio:
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time. CoRR abs/1805.08196 (2018) - [i21]Kevin Bello, Jean Honorio:
Learning latent variable structured prediction models with Gaussian perturbations. CoRR abs/1805.09213 (2018) - [i20]Meimei Liu, Jean Honorio, Guang Cheng:
Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression. CoRR abs/1809.06019 (2018) - [i19]Adarsh Barik, Jean Honorio:
Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity. CoRR abs/1811.06635 (2018) - 2017
- [c22]Asish Ghoshal, Jean Honorio:
Information-theoretic limits of Bayesian network structure learning. AISTATS 2017: 767-775 - [c21]Asish Ghoshal, Jean Honorio:
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions. AISTATS 2017: 1532-1540 - [c20]Jean Honorio:
On the sample complexity of learning graphical games. Allerton 2017: 830-836 - [c19]Adarsh Barik, Jean Honorio, Mohit Tawarmalani:
Information theoretic limits for linear prediction with graph-structured sparsity. ISIT 2017: 2348-2352 - [c18]Asish Ghoshal, Jean Honorio:
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity. NIPS 2017: 6457-6466 - [i18]Adarsh Barik, Jean Honorio, Mohit Tawarmalani:
Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity. CoRR abs/1701.07895 (2017) - [i17]Asish Ghoshal, Jean Honorio:
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity. CoRR abs/1703.01196 (2017) - [i16]Asish Ghoshal, Jean Honorio:
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions. CoRR abs/1703.01218 (2017) - [i15]Yixi Xu, Jean Honorio, Xiao Wang:
Statistical Efficiency of Compositional Nonparametric Prediction. CoRR abs/1704.01896 (2017) - [i14]Kevin Bello, Jean Honorio:
Learning Bayes networks using interventional path queries in polynomial time and sample complexity. CoRR abs/1706.00754 (2017) - [i13]Asish Ghoshal, Jean Honorio:
Learning Sparse Potential Games in Polynomial Time and Sample Complexity. CoRR abs/1706.05648 (2017) - [i12]Asish Ghoshal, Jean Honorio:
Learning linear structural equation models in polynomial time and sample complexity. CoRR abs/1707.04673 (2017) - 2016
- [c17]Asish Ghoshal, Jean Honorio:
From behavior to sparse graphical games: Efficient recovery of equilibria. Allerton 2016: 1220-1227 - [c16]Keehwan Park, Jean Honorio:
Information-theoretic lower bounds for recovery of diffusion network structures. ISIT 2016: 1346-1350 - [c15]Jean Honorio, Tommi S. Jaakkola:
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. UAI 2016 - [i11]Jean Honorio:
On the Sample Complexity of Learning Sparse Graphical Games. CoRR abs/1601.07243 (2016) - [i10]Asish Ghoshal, Jean Honorio:
Information-theoretic lower bounds on learning the structure of Bayesian networks. CoRR abs/1601.07460 (2016) - [i9]Keehwan Park, Jean Honorio:
Information-Theoretic Lower Bounds for Recovery of Diffusion Network Structures. CoRR abs/1601.07932 (2016) - [i8]Zhaosen Wang, Jean Honorio:
Reconstructing a Bounded-Degree Directed Tree Using Path Queries. CoRR abs/1606.05183 (2016) - [i7]Asish Ghoshal, Jean Honorio:
From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria. CoRR abs/1607.02959 (2016) - 2015
- [j4]Eugene Belilovsky, Katerina Gkirtzou, Michail Misyrlis, Anna B. Konova, Jean Honorio, Nelly Alia-Klein, Rita Z. Goldstein, Dimitris Samaras, Matthew B. Blaschko:
Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm. Comput. Medical Imaging Graph. 46: 40-46 (2015) - [j3]Jean Honorio, Luis E. Ortiz:
Learning the structure and parameters of large-population graphical games from behavioral data. J. Mach. Learn. Res. 16: 1157-1210 (2015) - [i6]Jean Honorio, Tommi S. Jaakkola:
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms. CoRR abs/1508.00945 (2015) - 2014
- [c14]Jean Honorio, Tommi S. Jaakkola:
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees. AISTATS 2014: 384-392 - [c13]Jean Honorio, Tommi S. Jaakkola:
A Unified Framework for Consistency of Regularized Loss Minimizers. ICML 2014: 136-144 - 2013
- [c12]Jean Honorio, Tommi S. Jaakkola:
Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy. ICML (3) 2013: 459-467 - [c11]Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Z. Goldstein, Matthew B. Blaschko:
FMRI analysis of cocaine addiction using k-support sparsity. ISBI 2013: 1078-1081 - [c10]Katerina Gkirtzou, Jean Honorio, Dimitris Samaras, Rita Z. Goldstein, Matthew B. Blaschko:
fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics. MLMI 2013: 90-97 - [c9]Jean Honorio, Tommi S. Jaakkola:
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. UAI 2013 - [i5]Jean Honorio, Tommi S. Jaakkola:
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models. CoRR abs/1309.6838 (2013) - 2012
- [j2]Jean Honorio, Dardo Tomasi, Rita Z. Goldstein, Hoi-Chung Leung, Dimitris Samaras:
Can a Single Brain Region Predict a Disorder? IEEE Trans. Medical Imaging 31(11): 2062-2072 (2012) - [c8]Kiwon Yun, Jean Honorio, Debaleena Chattopadhyay, Tamara L. Berg, Dimitris Samaras:
Two-person interaction detection using body-pose features and multiple instance learning. CVPR Workshops 2012: 28-35 - [c7]Jean Honorio:
Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models. ICML 2012 - [c6]Jean Honorio, Dimitris Samaras, Irina Rish, Guillermo A. Cecchi:
Variable Selection for Gaussian Graphical Models. AISTATS 2012: 538-546 - [i4]Jean Honorio:
Lipschitz Parametrization of Probabilistic Graphical Models. CoRR abs/1202.3733 (2012) - [i3]Jean Honorio, Luis E. Ortiz:
Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data. CoRR abs/1206.3713 (2012) - [i2]Jean Honorio:
Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models. CoRR abs/1206.4627 (2012) - [i1]Jean Honorio, Dimitris Samaras:
Simultaneous and Group-Sparse Multi-Task Learning of Gaussian Graphical Models. CoRR abs/1207.4255 (2012) - 2011
- [j1]Can Kirmizibayrak, Jean Honorio, Xiaolong Jiang, Mark Russell, James K. Hahn:
Digital Analysis and Visualization of Swimming Motion. Int. J. Virtual Real. 10(3): 9-16 (2011) - [c5]Jean Honorio:
Lipschitz Parametrization of Probabilistic Graphical Models. UAI 2011: 347-354 - 2010
- [c4]Jean Honorio, Dimitris Samaras:
Multi-Task Learning of Gaussian Graphical Models. ICML 2010: 447-454 - [c3]Jean Honorio, Dimitris Samaras, Dardo Tomasi, Rita Z. Goldstein:
Simple fully automated group classification on brain FMRI. ISBI 2010: 1145-1148
2000 – 2009
- 2009
- [c2]Jean Honorio, Luis E. Ortiz, Dimitris Samaras, Nikos Paragios, Rita Z. Goldstein:
Sparse and Locally Constant Gaussian Graphical Models. NIPS 2009: 745-753 - 2008
- [c1]Georg Langs, Dimitris Samaras, Nikos Paragios, Jean Honorio, Nelly Alia-Klein, Dardo Tomasi, Nora D. Volkow, Rita Z. Goldstein:
Task-Specific Functional Brain Geometry from Model Maps. MICCAI (1) 2008: 925-933
Coauthor Index
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