Galstyan, Aram

55 publications

NeurIPS 2025 Compress, Gather, and Recompute: REFORMing Long-Context Processing in Transformers Woomin Song, Sai Muralidhar Jayanthi, Srikanth Ronanki, Kanthashree Mysore Sathyendra, Jinwoo Shin, Aram Galstyan, Shubham Katiyar, Sravan Babu Bodapati

ICLR 2025 SeRA: Self-Reviewing and Alignment of LLMs Using Implicit Reward Margins Jongwoo Ko, Saket Dingliwal, Bhavana Ganesh, Sailik Sengupta, Sravan Babu Bodapati, Aram Galstyan

ICLRW 2025 Wanda++: Pruning Large Language Models via Regional Gradients Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan, Goeric Huybrechts, Markus Müller, Jonas M. Kübler, Rupak Vignesh Swaminathan, Athanasios Mouchtaris, Sravan Babu Bodapati, Nathan Susanj, Zheng Zhang, Jack FitzGerald, Abhishek Kumar

CLeaR 2024 Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding Myrl G. Marmarelis, Greg Steeg, Aram Galstyan, Fred Morstatter

NeurIPSW 2024 Learning Morphisms with Gauss-Newton Approximation for Growing Networks Neal Gregory Lawton, Aram Galstyan, Greg Ver Steeg

AISTATS 2024 Policy Learning for Localized Interventions from Observational Data Myrl G. Marmarelis, Fred Morstatter, Aram Galstyan, Greg Ver Steeg

IJCAI 2023 Cognitively Inspired Learning of Incremental Drifting Concepts Mohammad Rostami, Aram Galstyan

NeurIPSW 2023 JAB: Joint Adversarial Prompting and Belief Augmentation Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna, Jwala Dhamala, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta

AAAI 2023 Overcoming Concept Shift in Domain-Aware Settings Through Consolidated Internal Distributions Mohammad Rostami, Aram Galstyan

UAI 2023 Partial Identification of Dose Responses with Hidden Confounders Myrl G. Marmarelis, Elizabeth Haddad, Andrew Jesson, Neda Jahanshad, Aram Galstyan, Greg Ver Steeg

JAIR 2022 A Metric Space for Point Process Excitations Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan

NeurIPSW 2022 Bounding the Effects of Continuous Treatments for Hidden Confounders Myrl Marmarelis, Greg Ver Steeg, Neda Jahanshad, Aram Galstyan

CVPR 2022 Failure Modes of Domain Generalization Algorithms Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan

AISTATS 2021 Influence Decompositions for Neural Network Attribution Kyle Reing, Greg Ver Steeg, Aram Galstyan

AAAI 2021 Exacerbating Algorithmic Bias Through Fairness Attacks Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, Aram Galstyan

ICLRW 2021 Fast Graph Learning with Unique Optimal Solutions Sami Abu-El-Haija, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

ICLR 2021 Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning Elan Sopher Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, Aram Galstyan

NeurIPS 2021 Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling Greg Ver Steeg, Aram Galstyan

NeurIPS 2021 Implicit SVD for Graph Representation Learning Sami Abu-El-Haija, Hesham Mostafa, Marcel Nassar, Valentino Crespi, Greg Ver Steeg, Aram Galstyan

NeurIPS 2021 Information-Theoretic Generalization Bounds for Black-Box Learning Algorithms Hrayr Harutyunyan, Maxim Raginsky, Greg Ver Steeg, Aram Galstyan

ACML 2021 Layer-Wise Neural Network Compression via Layer Fusion James O’Neill, Greg V. Steeg, Aram Galstyan

WACV 2021 MUSCLE: Strengthening Semi-Supervised Learning via Concurrent Unsupervised Learning Using Mutual Information Maximization Hanchen Xie, Mohamed E. Hussein, Aram Galstyan, Wael Abd-Almageed

ICCV 2021 Partner-Assisted Learning for Few-Shot Image Classification Jiawei Ma, Hanchen Xie, Guangxing Han, Shih-Fu Chang, Aram Galstyan, Wael Abd-Almageed

UAI 2021 Q-Paths: Generalizing the Geometric Annealing Path Using Power Means Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood

ICML 2020 All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan

NeurIPSW 2020 Annealed Importance Sampling with Q-Paths Rob Brekelmans, Vaden Masrani, Thang D Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen

ICML 2020 Improving Generalization by Controlling Label-Noise Information in Neural Network Weights Hrayr Harutyunyan, Kyle Reing, Greg Ver Steeg, Aram Galstyan

NeurIPSW 2020 Likelihood Ratio Exponential Families Rob Brekelmans, Frank Nielsen, Alireza Makhzani, Aram Galstyan, Greg Ver Steeg

AAAI 2020 Modeling Dialogues with Hashcode Representations: A Nonparametric Approach Sahil Garg, Irina Rish, Guillermo A. Cecchi, Palash Goyal, Sarik Ghazarian, Shuyang Gao, Greg Ver Steeg, Aram Galstyan

AAAI 2020 Predictive Engagement: An Efficient Metric for Automatic Evaluation of Open-Domain Dialogue Systems Sarik Ghazarian, Ralph M. Weischedel, Aram Galstyan, Nanyun Peng

AISTATS 2019 Auto-Encoding Total Correlation Explanation Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan

NeurIPS 2019 Exact Rate-Distortion in Autoencoders via Echo Noise Rob Brekelmans, Daniel Moyer, Aram Galstyan, Greg Ver Steeg

NeurIPS 2019 Fast Structure Learning with Modular Regularization Greg Ver Steeg, Hrayr Harutyunyan, Daniel Moyer, Aram Galstyan

AAAI 2019 Kernelized Hashcode Representations for Relation Extraction Sahil Garg, Aram Galstyan, Greg Ver Steeg, Irina Rish, Guillermo A. Cecchi, Shuyang Gao

ICML 2019 MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

IJCAI 2019 SAGE: A Hybrid Geopolitical Event Forecasting System Fred Morstatter, Aram Galstyan, Gleb Satyukov, Daniel Benjamin, Andrés Abeliuk, Mehrnoosh Mirtaheri, K. S. M. Tozammel Hossain, Pedro A. Szekely, Emilio Ferrara, Akira Matsui, Mark Steyvers, Stephen Bennett, David V. Budescu, Mark Himmelstein, Michael D. Ward, Andreas Beger, Michele Catasta, Rok Sosic, Jure Leskovec, Pavel Atanasov, Regina Joseph, Rajiv Sethi, Ali E. Abbas

UAI 2018 A Forest Mixture Bound for Block-Free Parallel Inference Neal Lawton, Greg Ver Steeg, Aram Galstyan

NeurIPS 2018 Invariant Representations Without Adversarial Training Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, Greg Ver Steeg

IJCAI 2017 Sifting Common Information from Many Variables Greg Ver Steeg, Shuyang Gao, Kyle Reing, Aram Galstyan

AAAI 2016 Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text Sahil Garg, Aram Galstyan, Ulf Hermjakob, Daniel Marcu

ICML 2016 The Information Sieve Greg Ver Steeg, Aram Galstyan

NeurIPS 2016 Variational Information Maximization for Feature Selection Shuyang Gao, Greg Ver Steeg, Aram Galstyan

AISTATS 2015 Efficient Estimation of Mutual Information for Strongly Dependent Variables Shuyang Gao, Greg Ver Steeg, Aram Galstyan

UAI 2015 Estimating Mutual Information by Local Gaussian Approximation Shuyang Gao, Greg Ver Steeg, Aram Galstyan

AISTATS 2015 Maximally Informative Hierarchical Representations of High-Dimensional Data Greg Ver Steeg, Aram Galstyan

ICML 2014 Demystifying Information-Theoretic Clustering Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo

NeurIPS 2014 Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg, Aram Galstyan

AAAI 2014 Where and Why Users "Check In" Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan

AISTATS 2013 Statistical Tests for Contagion in Observational Social Network Studies Greg Ver Steeg, Aram Galstyan

UAI 2011 A Sequence of Relaxation Constraining Hidden Variable Models Greg Ver Steeg, Aram Galstyan

AAAI 2011 Co-Evolution of Selection and Influence in Social Networks Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan

NeurIPS 2011 Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs Armen Allahverdyan, Aram Galstyan

UAI 2011 Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs (Abstract) Greg Ver Steeg, Aram Galstyan, Armen E. Allahverdyan

UAI 2009 On Maximum a Posteriori Estimation of Hidden Markov Processes Armen E. Allahverdyan, Aram Galstyan

IJCAI 2005 Inferring Useful Heuristics from the Dynamics of Iterative Relational Classifiers Aram Galstyan, Paul R. Cohen