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2020 – today
- 2025
[i84]Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Making Sense Of Distributed Representations With Activation Spectroscopy. CoRR abs/2501.15435 (2025)- 2024
- [j11]Qiang Li
, Greg Ver Steeg, Jesus Malo:
Functional connectivity via total correlation: Analytical results in visual areas. Neurocomputing 571: 127143 (2024) - [j10]Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil J. Dhinagar, Tanmay Ghai, Chrysovalantis Anastasiou, Rafael Sanchez, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, José Luis Ambite:
A federated learning architecture for secure and private neuroimaging analysis. Patterns 5(8): 101031 (2024) - [c76]Haz Sameen Shahgir, Xianghao Kong, Greg Ver Steeg, Yue Dong:
Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks. ACL (Findings) 2024: 5779-5796 - [c75]Myrl G. Marmarelis, Fred Morstatter, Aram Galstyan, Greg Ver Steeg:
Policy Learning for Localized Interventions from Observational Data. AISTATS 2024: 4456-4464 - [c74]Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan, Fred Morstatter:
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding. CLeaR 2024: 18-40 - [c73]Soroosh Safari Loaliyan, Greg Ver Steeg:
Comparative Analysis of Generalization and Harmonization Methods for 3D Brain fMRI Images: A Case Study on OpenBHB Dataset. CVPR Workshops 2024: 4915-4923 - [c72]Alessandro Achille, Greg Ver Steeg, Tian Yu Liu, Matthew Trager, Carson Klingenberg, Stefano Soatto:
Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding. CVPR 2024: 11062-11071 - [c71]Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi, Greg Ver Steeg, Anoop Kumar, Anna Rumshisky, Aram Galstyan:
Prompt Perturbation Consistency Learning for Robust Language Models. EACL (Findings) 2024: 1357-1370 - [c70]Xianghao Kong, Ollie Liu, Han Li, Dani Yogatama, Greg Ver Steeg:
Interpretable Diffusion via Information Decomposition. ICLR 2024 - [c69]Yunshu Wu, Yingtao Luo, Xianghao Kong, Vagelis Papalexakis, Greg Ver Steeg:
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training. NeurIPS 2024 - [c68]Keshav Balasubramanian, Abdulla Alshabanah, Elan Markowitz, Greg Ver Steeg, Murali Annavaram:
Biased User History Synthesis for Personalized Long-Tail Item Recommendation. RecSys 2024: 189-199 - [c67]Tamoghna Chattopadhyay, Neha Ann Joshy, Chirag Jagad, Emma J. Gleave, Sophia I. Thomopoulos, Yixue Feng, Julio E. Villalon-Reina, Emily Laltoo, Himanshu Joshi, Ganesan Venkatasubramanian, John P. John, Greg Ver Steeg, José Luis Ambite, Paul M. Thompson:
Comparison of Explainable AI Models for MRI-Based Alzheimer's Disease Classification. SIPAIM 2024: 1-4 - [i83]Alessandro Achille, Greg Ver Steeg, Tian Yu Liu, Matthew Trager, Carson Klingenberg, Stefano Soatto:
Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding. CoRR abs/2402.08919 (2024) - [i82]Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi, Greg Ver Steeg, Anoop Kumar, Anna Rumshisky, Aram Galstyan:
Prompt Perturbation Consistency Learning for Robust Language Models. CoRR abs/2402.15833 (2024) - [i81]Yunshu Wu, Yingtao Luo, Xianghao Kong, Evangelos E. Papalexakis, Greg Ver Steeg:
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training. CoRR abs/2407.08946 (2024) - [i80]Neal Lawton, Aishwarya Padmakumar, Judith Gaspers, Jack FitzGerald, Anoop Kumar, Greg Ver Steeg, Aram Galstyan:
QuAILoRA: Quantization-Aware Initialization for LoRA. CoRR abs/2410.14713 (2024) - [i79]Shaorong Zhang, Yuanbin Cheng, Xianghao Kong, Greg Ver Steeg:
Exploring the Design Space of Diffusion Bridge Models via Stochasticity Control. CoRR abs/2410.21553 (2024) - [i78]Neal Lawton, Aram Galstyan, Greg Ver Steeg:
Learning Morphisms with Gauss-Newton Approximation for Growing Networks. CoRR abs/2411.05855 (2024) - 2023
- [c66]Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg, He Xie, Anoop Kumar, Aram Galstyan:
Measuring and Mitigating Local Instability in Deep Neural Networks. ACL (Findings) 2023: 2810-2823 - [c65]Neal Lawton, Anoop Kumar, Govind Thattai, Aram Galstyan, Greg Ver Steeg:
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models. ACL (Findings) 2023: 8506-8515 - [c64]Umang Gupta, Aram Galstyan, Greg Ver Steeg:
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning. ACL (Findings) 2023: 12612-12629 - [c63]Xianghao Kong, Rob Brekelmans, Greg Ver Steeg:
Information-Theoretic Diffusion. ICLR 2023 - [c62]Umang Gupta, Tamoghna Chattopadhyay, Nikhil J. Dhinagar, Paul M. Thompson, Greg Ver Steeg:
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models. ISBI 2023: 1-5 - [c61]Elizabeth Haddad, Myrl G. Marmarelis, Talia M. Nir, Aram Galstyan, Greg Ver Steeg, Neda Jahanshad:
Causal Sensitivity Analysis for Hidden Confounding: Modeling the Sex-Specific Role of Diet on the Aging Brain. MLCN@MICCAI 2023: 91-101 - [c60]Dheeraj Komandur, Umang Gupta, Tamoghna Chattopadhyay, Nikhil J. Dhinagar, Sophia I. Thomopoulos, Jiu-Chiuan Chen, Dan Beavers, Greg Ver Steeg, Paul M. Thompson:
Unsupervised harmonization of brain MRI using 3D CycleGANs and its effect on brain age prediction. SIPAIM 2023: 1-5 - [c59]Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg:
Partial identification of dose responses with hidden confounders. UAI 2023: 1368-1379 - [i77]Xianghao Kong, Rob Brekelmans, Greg Ver Steeg:
Information-Theoretic Diffusion. CoRR abs/2302.03792 (2023) - [i76]Umang Gupta, Tamoghna Chattopadhyay, Nikhil J. Dhinagar, Paul M. Thompson, Greg Ver Steeg, Alzheimer's Disease Neuroimaging Initiative:
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models. CoRR abs/2303.01491 (2023) - [i75]Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger B. Grosse, Alireza Makhzani:
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. CoRR abs/2303.06992 (2023) - [i74]Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg, He Xie, Anoop Kumar, Aram Galstyan:
Measuring and Mitigating Local Instability in Deep Neural Networks. CoRR abs/2305.10625 (2023) - [i73]Neal Lawton, Anoop Kumar, Govind Thattai, Aram Galstyan, Greg Ver Steeg:
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models. CoRR abs/2305.16597 (2023) - [i72]Umang Gupta, Aram Galstyan, Greg Ver Steeg:
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning. CoRR abs/2305.19264 (2023) - [i71]Elan Markowitz, Ziyan Jiang, Fan Yang, Xing Fan, Tony Chen, Greg Ver Steeg, Aram Galstyan:
Multi-Task Knowledge Enhancement for Zero-Shot and Multi-Domain Recommendation in an AI Assistant Application. CoRR abs/2306.06302 (2023) - [i70]Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan, Fred Morstatter:
Tighter Prediction Intervals for Causal Outcomes Under Hidden Confounding. CoRR abs/2306.09520 (2023) - [i69]Xianghao Kong, Ollie Liu, Han Li, Dani Yogatama, Greg Ver Steeg:
Interpretable Diffusion via Information Decomposition. CoRR abs/2310.07972 (2023) - [i68]Haz Sameen Shahgir, Xianghao Kong, Greg Ver Steeg, Yue Dong:
Asymmetric Bias in Text-to-Image Generation with Adversarial Attacks. CoRR abs/2312.14440 (2023) - 2022
- [j9]Qiang Li, Greg Ver Steeg, Shujian Yu, Jesus Malo:
Functional Connectome of the Human Brain with Total Correlation. Entropy 24(12): 1725 (2022) - [j8]Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan:
A Metric Space for Point Process Excitations. J. Artif. Intell. Res. 73 (2022) - [c58]Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan:
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal. ACL (Findings) 2022: 658-678 - [c57]Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan:
Failure Modes of Domain Generalization Algorithms. CVPR 2022: 19055-19064 - [c56]Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi, Greg Ver Steeg, Roger Baker Grosse, Alireza Makhzani:
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds. ICLR 2022 - [c55]Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan:
Formal limitations of sample-wise information-theoretic generalization bounds. ITW 2022: 440-445 - [c54]Dimitris Stripelis, Umang Gupta, Nikhil J. Dhinagar, Greg Ver Steeg, Paul M. Thompson, José Luis Ambite:
Towards Sparsified Federated Neuroimaging Models via Weight Pruning. DeCaF/FAIR@MICCAI 2022: 141-151 - [c53]Judith Gaspers, Anoop Kumar, Greg Ver Steeg, Aram Galstyan:
Temporal Generalization for Spoken Language Understanding. NAACL-HLT (Industry Papers) 2022: 37-44 - [c52]Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Murali Annavaram, Aram Galstyan, Greg Ver Steeg:
StATIK: Structure and Text for Inductive Knowledge Graph Completion. NAACL-HLT (Findings) 2022: 604-615 - [i67]Marcin Abram, Keith Burghardt, Greg Ver Steeg, Aram Galstyan, Rémi Dingreville:
Inferring topological transitions in pattern-forming processes with self-supervised learning. CoRR abs/2203.10204 (2022) - [i66]Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan:
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal. CoRR abs/2203.12574 (2022) - [i65]Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan:
Bounding the Effects of Continuous Treatments for Hidden Confounders. CoRR abs/2204.11206 (2022) - [i64]Dimitris Stripelis, Umang Gupta, Greg Ver Steeg, José Luis Ambite:
Federated Progressive Sparsification (Purge, Merge, Tune)+. CoRR abs/2204.12430 (2022) - [i63]Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil J. Dhinagar, Tanmay Ghai, Rafael Sanchez, Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, José Luis Ambite:
Secure Federated Learning for Neuroimaging. CoRR abs/2205.05249 (2022) - [i62]Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan:
Formal limitations of sample-wise information-theoretic generalization bounds. CoRR abs/2205.06915 (2022) - [i61]Dimitris Stripelis, Umang Gupta, Nikhil J. Dhinagar, Greg Ver Steeg, Paul M. Thompson, José Luis Ambite:
Towards Sparsified Federated Neuroimaging Models via Weight Pruning. CoRR abs/2208.11669 (2022) - 2021
- [j7]Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Discovering Higher-Order Interactions Through Neural Information Decomposition. Entropy 23(1): 79 (2021) - [j6]Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg, Aram Galstyan:
Identifying and Analyzing Cryptocurrency Manipulations in Social Media. IEEE Trans. Comput. Soc. Syst. 8(3): 607-617 (2021) - [c51]Umang Gupta, Aaron M. Ferber, Bistra Dilkina, Greg Ver Steeg:
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation. AAAI 2021: 7610-7619 - [c50]James O'Neill, Greg Ver Steeg, Aram Galstyan:
Layer-Wise Neural Network Compression via Layer Fusion. ACML 2021: 1381-1396 - [c49]Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Influence Decompositions For Neural Network Attribution. AISTATS 2021: 2710-2718 - [c48]Elan Sopher Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan:
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. ICLR 2021 - [c47]Umang Gupta, Pradeep K. Lam, Greg Ver Steeg, Paul M. Thompson:
Improved Brain Age Estimation With Slice-Based Set Networks. ISBI 2021: 840-844 - [c46]Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg:
Membership Inference Attacks on Deep Regression Models for Neuroimaging. MIDL 2021: 228-251 - [c45]Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan:
Implicit SVD for Graph Representation Learning. NeurIPS 2021: 8419-8431 - [c44]Greg Ver Steeg, Aram Galstyan:
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling. NeurIPS 2021: 11012-11025 - [c43]Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan:
Information-theoretic generalization bounds for black-box learning algorithms. NeurIPS 2021: 24670-24682 - [c42]Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood:
q-Paths: Generalizing the geometric annealing path using power means. UAI 2021: 1938-1947 - [i60]Umang Gupta, Aaron M. Ferber, Bistra Dilkina, Greg Ver Steeg:
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation. CoRR abs/2101.04108 (2021) - [i59]Elan Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan:
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. CoRR abs/2102.04350 (2021) - [i58]Umang Gupta, Pradeep Lam, Greg Ver Steeg, Paul Thompson:
Improved Brain Age Estimation with Slice-based Set Networks. CoRR abs/2102.04438 (2021) - [i57]Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan:
Fast Graph Learning with Unique Optimal Solutions. CoRR abs/2102.08530 (2021) - [i56]Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg:
Membership Inference Attacks on Deep Regression Models for Neuroimaging. CoRR abs/2105.02866 (2021) - [i55]Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood:
q-Paths: Generalizing the Geometric Annealing Path using Power Means. CoRR abs/2107.00745 (2021) - [i54]Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil J. Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, José Luis Ambite:
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption. CoRR abs/2108.03437 (2021) - [i53]Ninareh Mehrabi, Umang Gupta, Fred Morstatter, Greg Ver Steeg, Aram Galstyan:
Attributing Fair Decisions with Attention Interventions. CoRR abs/2109.03952 (2021) - [i52]Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan:
Information-theoretic generalization bounds for black-box learning algorithms. CoRR abs/2110.01584 (2021) - [i51]Greg Ver Steeg, Aram Galstyan:
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling. CoRR abs/2111.02434 (2021) - [i50]Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan:
Implicit SVD for Graph Representation Learning. CoRR abs/2111.06312 (2021) - [i49]Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan:
Failure Modes of Domain Generalization Algorithms. CoRR abs/2111.13733 (2021) - 2020
- [j5]Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Maximizing Multivariate Information With Error-Correcting Codes. IEEE Trans. Inf. Theory 66(5): 2683-2695 (2020) - [c41]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach. AAAI 2020: 3970-3979 - [c40]Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg, Wael AbdAlmageed, Premkumar Natarajan:
Invariant Representations through Adversarial Forgetting. AAAI 2020: 4272-4279 - [c39]Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan:
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. ICML 2020: 1111-1122 - [c38]Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Improving generalization by controlling label-noise information in neural network weights. ICML 2020: 4071-4081 - [i48]Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Improving Generalization by Controlling Label-Noise Information in Neural Network Weights. CoRR abs/2002.07933 (2020) - [i47]Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan:
Event Cartography: Latent Point Process Embeddings. CoRR abs/2005.02515 (2020) - [i46]Daniel Moyer, Greg Ver Steeg, Paul M. Thompson:
Overview of Scanner Invariant Representations. CoRR abs/2006.00115 (2020) - [i45]Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan:
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference. CoRR abs/2007.00642 (2020) - [i44]Tigran Galstyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan:
Robust Classification under Class-Dependent Domain Shift. CoRR abs/2007.05335 (2020) - [i43]James O'Neill, Greg Ver Steeg, Aram Galstyan:
Compressing Deep Neural Networks via Layer Fusion. CoRR abs/2007.14917 (2020) - [i42]Rob Brekelmans, Vaden Masrani, Thang Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen:
Annealed Importance Sampling with q-Paths. CoRR abs/2012.07823 (2020) - [i41]Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Ver Steeg:
Likelihood Ratio Exponential Families. CoRR abs/2012.15480 (2020)
2010 – 2019
- 2019
- [c37]Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo A. Cecchi, Shuyang Gao:
Kernelized Hashcode Representations for Relation Extraction. AAAI 2019: 6431-6440 - [c36]Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan:
Auto-Encoding Total Correlation Explanation. AISTATS 2019: 1157-1166 - [c35]Sahil Garg, Aram Galstyan, Greg Ver Steeg, Guillermo A. Cecchi:
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction. EMNLP/IJCNLP (1) 2019: 4024-4034 - [c34]Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan:
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. ICML 2019: 21-29 - [c33]Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg:
Exact Rate-Distortion in Autoencoders via Echo Noise. NeurIPS 2019: 3884-3895 - [c32]Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan:
Fast structure learning with modular regularization. NeurIPS 2019: 15567-15577 - [i40]Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg, Aram Galstyan:
Identifying and Analyzing Cryptocurrency Manipulations in Social Media. CoRR abs/1902.03110 (2019) - [i39]Daniel Moyer, Greg Ver Steeg, Chantal M. W. Tax, Paul M. Thompson:
Scanner Invariant Representations for Diffusion MRI Harmonization. CoRR abs/1904.05375 (2019) - [i38]Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg:
Exact Rate-Distortion in Autoencoders via Echo Noise. CoRR abs/1904.07199 (2019) - [i37]Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Hrayr Harutyunyan, Nazanin Alipourfard, Kristina Lerman, Greg Ver Steeg, Aram Galstyan:
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. CoRR abs/1905.00067 (2019) - [i36]Hrayr Harutyunyan, Daniel Moyer, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan:
Efficient Covariance Estimation from Temporal Data. CoRR abs/1905.13276 (2019) - [i35]Sahil Garg, Aram Galstyan, Greg Ver Steeg, Guillermo A. Cecchi:
Nearly-Unsupervised Hashcode Representations for Relation Extraction. CoRR abs/1909.03881 (2019) - [i34]Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg, Wael AbdAlmageed, Premkumar Natarajan:
Invariant Representations through Adversarial Forgetting. CoRR abs/1911.04060 (2019) - [i33]Ayush Jaiswal, Rob Brekelmans, Daniel Moyer, Greg Ver Steeg, Wael AbdAlmageed, Premkumar Natarajan:
Discovery and Separation of Features for Invariant Representation Learning. CoRR abs/1912.00646 (2019) - 2018
- [c31]Sahil Garg, Guillermo A. Cecchi, Irina Rish, Shuyang Gao, Greg Ver Steeg, Sarik Ghazarian, Palash Goyal, Aram Galstyan:
Dialogue Modeling Via Hash Functions. LaCATODA@IJCAI 2018: 24-36 - [c30]Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg:
Invariant Representations without Adversarial Training. NeurIPS 2018: 9102-9111 - [c29]Neal Lawton, Greg Ver Steeg, Aram Galstyan:
A Forest Mixture Bound for Block-Free Parallel Inference. UAI 2018: 968-977 - [i32]Sahil Garg, Greg Ver Steeg, Aram Galstyan:
Stochastic Learning of Nonstationary Kernels for Natural Language Modeling. CoRR abs/1801.03911 (2018) - [i31]Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan:
Auto-Encoding Total Correlation Explanation. CoRR abs/1802.05822 (2018) - [i30]Neal Lawton, Aram Galstyan, Greg Ver Steeg:
A Forest Mixture Bound for Block-Free Parallel Inference. CoRR abs/1805.06951 (2018) - [i29]Daniel Moyer, Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan:
Evading the Adversary in Invariant Representation. CoRR abs/1805.09458 (2018) - [i28]Daniel Moyer, Paul M. Thompson, Greg Ver Steeg:
Measures of Tractography Convergence. CoRR abs/1806.04634 (2018) - [i27]Kyle Reing, Greg Ver Steeg, Aram Galstyan:
Maximizing Multivariate Information with Error-Correcting Codes. CoRR abs/1811.10839 (2018) - 2017
- [j4]Ryan J. Gallagher, Kyle Reing, David C. Kale, Greg Ver Steeg:
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. Trans. Assoc. Comput. Linguistics 5: 529-542 (2017) - [c28]Greg Ver Steeg, Rob Brekelmans, Hrayr Harutyunyan, Aram Galstyan:
Disentangled representations via synergy minimization. Allerton 2017: 180-187 - [c27]Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan:
Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (Extended Abstract). ICDE 2017: 57-58 - [c26]Greg Ver Steeg, Shuyang Gao, Kyle Reing, Aram Galstyan:
Sifting Common Information from Many Variables. IJCAI 2017: 2885-2892 - [c25]Greg Ver Steeg:
Unsupervised Learning via Total Correlation Explanation. IJCAI 2017: 5151-5155 - [c24]David Stück, Haraldur Tómas Hallgrímsson, Greg Ver Steeg, Alessandro Epasto, Luca Foschini:
The Spread of Physical Activity Through Social Networks. WWW 2017: 519-528 - [i26]Greg Ver Steeg, Aram Galstyan:
Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality. CoRR abs/1706.03353 (2017) - [i25]Wenzhe Li, Dong Guo, Greg Ver Steeg, Aram Galstyan:
Unifying Local and Global Change Detection in Dynamic Networks. CoRR abs/1710.03035 (2017) - [i24]Greg Ver Steeg, Rob Brekelmans, Hrayr Harutyunyan, Aram Galstyan:
Disentangled Representations via Synergy Minimization. CoRR abs/1710.03839 (2017) - [i23]Armen E. Allahverdyan, Greg Ver Steeg, Aram Galstyan:
Memory-induced mechanism for self-sustaining activity in networks. CoRR abs/1712.07844 (2017) - 2016
- [j3]Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan:
Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks. IEEE Trans. Knowl. Data Eng. 28(10): 2765-2777 (2016) - [c23]Greg Ver Steeg, Aram Galstyan:
The Information Sieve. ICML 2016: 164-172 - [c22]Sarah K. Madsen, Greg Ver Steeg, Madelaine Daianu, Adam Mezher, Neda Jahanshad, Talia M. Nir, Xue Hua, Boris A. Gutman, Aram Galstyan, Paul M. Thompson:
Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly. Image Processing 2016: 978411 - [c21]Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Variational Information Maximization for Feature Selection. NIPS 2016: 487-495 - [c20]Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, Aram Galstyan:
Latent Space Model for Multi-Modal Social Data. WWW 2016: 447-458 - [i22]Greg Ver Steeg, Shuyang Gao, Kyle Reing, Aram Galstyan:
Sifting Common Information from Many Variables. CoRR abs/1606.02307 (2016) - [i21]Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Variational Information Maximization for Feature Selection. CoRR abs/1606.02827 (2016) - [i20]Kyle Reing, David C. Kale, Greg Ver Steeg, Aram Galstyan:
Toward Interpretable Topic Discovery via Anchored Correlation Explanation. CoRR abs/1606.07043 (2016) - [i19]Ryan J. Gallagher, Kyle Reing, David C. Kale, Greg Ver Steeg:
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. CoRR abs/1611.10277 (2016) - 2015
- [c19]Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Efficient Estimation of Mutual Information for Strongly Dependent Variables. AISTATS 2015 - [c18]Greg Ver Steeg, Aram Galstyan:
Maximally Informative Hierarchical Representations of High-Dimensional Data. AISTATS 2015 - [c17]Sarah K. Madsen, Greg Ver Steeg, Adam Mezher, Neda Jahanshad, Talia M. Nir, Xue Hua, Boris A. Gutman, Aram Galstyan, Paul M. Thompson:
Information-theoretic characterization of blood panel predictors for brain atrophy and cognitive decline in the elderly. ISBI 2015: 980-984 - [c16]Madelaine Daianu, Greg Ver Steeg, Adam Mezher, Neda Jahanshad, Talia M. Nir, Xiaoran Yan, Gautam Prasad, Kristina Lerman, Aram Galstyan, Paul M. Thompson:
Information-Theoretic Clustering of Neuroimaging Metrics Related to Cognitive Decline in the Elderly. MCV@MICCAI 2015: 13-23 - [c15]Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Estimating Mutual Information by Local Gaussian Approximation. UAI 2015: 278-285 - [c14]Nathan O. Hodas, Greg Ver Steeg, Joshua J. Harrison, Satish Chikkagoudar, Eric Bell, Courtney D. Corley:
Disentangling the Lexicons of Disaster Response in Twitter. WWW (Companion Volume) 2015: 1201-1204 - [i18]Greg Ver Steeg, Aram Galstyan:
The Information Sieve. CoRR abs/1507.02284 (2015) - [i17]Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Estimating Mutual Information by Local Gaussian Approximation. CoRR abs/1508.00536 (2015) - [i16]Yoon-Sik Cho, Greg Ver Steeg, Emilio Ferrara, Aram Galstyan:
Latent Space Model for Multi-Modal Social Data. CoRR abs/1510.05318 (2015) - 2014
- [j2]Vasanthan Raghavan, Greg Ver Steeg, Aram Galstyan, Alexander G. Tartakovsky:
Modeling Temporal Activity Patterns in Dynamic Social Networks. IEEE Trans. Comput. Soc. Syst. 1(1): 89-107 (2014) - [c13]Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan:
Where and Why Users "Check In". AAAI 2014: 269-275 - [c12]Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo:
Demystifying Information-Theoretic Clustering. ICML 2014: 19-27 - [c11]Greg Ver Steeg, Aram Galstyan:
Discovering Structure in High-Dimensional Data Through Correlation Explanation. NIPS 2014: 577-585 - [r1]Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan:
Mixed Membership Blockmodels for Dynamic Networks with Feedback. Handbook of Mixed Membership Models and Their Applications 2014: 527-545 - [i15]Greg Ver Steeg, Aram Galstyan:
Discovering Structure in High-Dimensional Data Through Correlation Explanation. CoRR abs/1406.1222 (2014) - [i14]Greg Ver Steeg, Aram Galstyan:
Maximally Informative Hierarchical Representations of High-Dimensional Data. CoRR abs/1410.7404 (2014) - [i13]Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Efficient Estimation of Mutual Information for Strongly Dependent Variables. CoRR abs/1411.2003 (2014) - [i12]Linhong Zhu, Greg Ver Steeg, Aram Galstyan:
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization. CoRR abs/1411.3675 (2014) - [i11]Shuyang Gao, Greg Ver Steeg, Aram Galstyan:
Understanding confounding effects in linguistic coordination: an information-theoretic approach. CoRR abs/1412.0696 (2014) - 2013
- [c10]Greg Ver Steeg, Aram Galstyan:
Statistical Tests for Contagion in Observational Social Network Studies. AISTATS 2013: 563-571 - [c9]Vasanthan Raghavan, Greg Ver Steeg, Aram Galstyan, Alexander G. Tartakovsky:
Coupled hidden markov models for user activity in social networks. ICME Workshops 2013: 1-6 - [c8]Greg Ver Steeg, Aram Galstyan:
Information-theoretic measures of influence based on content dynamics. WSDM 2013: 3-12 - [i10]Vasanthan Raghavan, Greg Ver Steeg, Aram Galstyan, Alexander G. Tartakovsky:
Modeling Temporal Activity Patterns in Dynamic Social Networks. CoRR abs/1305.1980 (2013) - [i9]Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo:
Demystifying Information-Theoretic Clustering. CoRR abs/1310.4210 (2013) - [i8]Greg Ver Steeg, Cristopher Moore, Aram Galstyan, Armen E. Allahverdyan:
Phase Transitions in Community Detection: A Solvable Toy Model. CoRR abs/1312.0631 (2013) - 2012
- [c7]Greg Ver Steeg:
Information Theoretic Tools for Social Media. #MSM 2012: 1 - [c6]Greg Ver Steeg, Aram Galstyan:
Information transfer in social media. WWW 2012: 509-518 - [i7]Greg Ver Steeg, Aram Galstyan:
Inferring Predictive Links in Social Media Using Content Transfer. CoRR abs/1208.4475 (2012) - [i6]Greg Ver Steeg, Aram Galstyan:
Statistical Tests for Contagion in Observational Social Network Studies. CoRR abs/1211.4889 (2012) - 2011
- [c5]Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan:
Co-Evolution of Selection and Influence in Social Networks. AAAI 2011: 779-784 - [c4]Kristina Lerman, Aram Galstyan, Greg Ver Steeg, Tad Hogg:
Social Mechanics: An Empirically Grounded Science of Social Media. The Future of the Social Web 2011 - [c3]Greg Ver Steeg, Rumi Ghosh, Kristina Lerman:
What Stops Social Epidemics? ICWSM 2011 - [c2]Greg Ver Steeg, Aram Galstyan:
A Sequence of Relaxation Constraining Hidden Variable Models. UAI 2011: 717-726 - [c1]Greg Ver Steeg, Aram Galstyan, Armen E. Allahverdyan:
Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs (Abstract). UAI 2011: 853 - [i5]Aram Galstyan, Greg Ver Steeg, Armen E. Allahverdyan:
Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs. CoRR abs/1101.4227 (2011) - [i4]Greg Ver Steeg, Rumi Ghosh, Kristina Lerman:
What stops social epidemics? CoRR abs/1102.1985 (2011) - [i3]Greg Ver Steeg, Aram Galstyan:
A Sequence of Relaxations Constraining Hidden Variable Models. CoRR abs/1106.1636 (2011) - [i2]Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan:
Co-evolution of Selection and Influence in Social Networks. CoRR abs/1106.2788 (2011) - [i1]Greg Ver Steeg, Aram Galstyan:
Information Transfer in Social Media. CoRR abs/1110.2724 (2011)
2000 – 2009
- 2009
- [j1]Greg Ver Steeg, Stephanie Wehner:
Relaxed uncertainty relations and information processing. Quantum Inf. Comput. 9(9&10): 801-832 (2009)
Coauthor Index
[i84] [c75] [c74] [c71] [i82] [i80] [i78] [c66] [c65] [c64] [c61] [c59] [i74] [i73] [i72] [i71] [i70] [j8] [c58] [c57] [c55] [c53] [c52] [i67] [i66] [i65] [i62] [j7] [j6] [c50] [c49] [c48] [c45] [c44] [c43] [c42] [i59] [i57] [i55] [i53] [i52] [i51] [i50] [i49] [j5] [c41] [c39] [c38] [i48] [i47] [i45] [i44] [i43] [i42] [i41] [c37] [c36] [c35] [c34] [c33] [c32] [i40] [i38] [i37] [i36] [i35] [c31] [c30] [c29] [i32] [i31] [i30] [i29] [i27] [c28] [c27] [c26] [i26] [i25] [i24] [i23] [j3] [c23] [c22] [c21] [c20] [i22] [i21] [i20] [c19] [c18] [c17] [c16] [c15] [i18] [i17] [i16] [j2] [c13] [c12] [c11] [r1] [i15] [i14] [i13] [i12] [i11] [c10] [c9] [c8] [i10] [i9] [i8] [c6] [i7] [i6] [c5] [c4] [c2] [c1] [i5] [i3] [i2] [i1]
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