Publications

Below, you'll find select publications organised by year. For questions on any specific publication, feel free to message me.

Selected Publications [Full list in Google Scholar]

C. Rodriguez-Opazo, E. Abbasnejad, D. Teney, H. Damirchi, E. Marrese-Taylor, A. van den Hengel, Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling, in The International Conference on Learning Representations (ICLR), 2025.
P. Albert, F.Z. Zhang, C. Rodriguez-Opazo, H. Saratchandran, A. van den Hengel, E. Abbasnejad, RandLoRA: Full rank parameter-efficient fine-tuning of large models, in The International Conference on Learning Representations (ICLR), 2025.
A. Sonthalia, A. Rubinstein, E. Abbasnejad, S. Joon Oh, Do Deep Neural Network Solutions Form a Star Domain?, in The International Conference on Learning Representations (ICLR), 2025.
A. Sonthalia, A. Rubinstein, E. Abbasnejad, S. Joon Oh, Do Deep Neural Network Solutions Form a Star Domain?, in The Thirteenth International Conference on Learning Representations (ICLR), 2025.
B. Doan, A. Shamsi, X. Guo, A. Mohammadi, H. Alinejad-Rokny, D. Sejdinovic, D. Ranasinghe, E. Abbasnejad, Bayesian Low-rank Learning (Bella): A Practical Approach to Bayesian Neural Networks, in The 39th Annual AAAI Conference on Artificial Intelligence, 2025.
F. Zhang, P. Albert, C. Rodriguez-Opazo, A. van den Hengel, E. Abbasnejad, Knowledge Composition using Task Vectors with Learned Anisotropic Scaling, in Conference on Neural Information Processing Systems (NeurIPS), 2024.
B. Repasky, E. Abbasnejad, A. Dick, BLURD: Benchmarking and Learning using a Unified Rendering and Diffusion Model, in Conference on Neural Information Processing Systems (NeurIPS), 2024.
D. Teney, J. Wang, E Abbasnejad, Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup, International Conference on Machine Learning (ICML) 2024. [Link]
D. Teney, A. Nicolicioiu, V. Hartmann, E Abbasnejad, Neural Redshift: Random Networks are not Random Functions, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Oral] 2024. [Link]
V. Vo, E. Abbasnejad, D. Ranasinghe, BRUSLEATTACK: Query-efficient Score-based Sparse Adversarial Attack, The Twelfth International Conference on Learning Representations (ICLR) 2024. [Link]
D. Teney, Y. Lin, S. J. Oh, E. Abbasnejad, ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets, Conference on Neural Information Processing Systems (NeurIPS) 2023. [Link]
M. McDonnell, D. Gong, A. Parveneh, E. Abbasnejad, A. van den Hengel, RanPAC: Random Projections and Pre-trained Models for Continual Learning, Conference on Neural Information Processing Systems (NeurIPS) 2023. [Link]
S. Herath, B. Fernando, E. Abbasnejad, M. Hayat, S. Khadivi, M. Harandi, H. Rezatofighi, G. Haffari, Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2023. [Link]
I. Nassar, M. Hayat, E. Abbasnejad, H. Rezatofighi, G. Haffari, ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Highlight], 2023. [Link]
B. G. Doan, S. Yang, P. Montague, O. De Vel, T. Abraham, E. Abbasnejad, D. Ranasinghe, Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness, Association for the Advancement of Artificial Intelligence (AAAI) 2023. [Link]
Z. Zhang, I. Ng, D. Gong, Y. Liu, E. Abbasnejad, M. Gong, K. Zhang, J. Shi, Truncated Matrix Power Iteration for Differentiable DAG Learning, Conference on Neural Information Processing Systems (NeurIPS) 2022. [Link]
D. Teney, M. Peyrard, E. Abbasnejad, Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning, European Conference on Computer Vision (ECCV) 2022. [Link]
B. G. Doan, E. Abbasnejad, D. Ranasinghe, Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense, International Conference on Machine Learning (ICML) 2022. [Link]
D. Teney, E. Abbasnejad, S. Lucey, A. van den Hengel, Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022. [Link]
A. Parvaneh, E. Abbasnejad, D. Teney, R. Haffari, A. van den Hengel, J. Shi, Active Learning by Feature Mixing, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022. [Link]
V. Q. Vo, E. Abbasnejad, D. Ranasinghe, Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models, International Conference on Representation Learning (ICLR) 2022. [Link]
V. Q. Vo, E. Abbasnejad, D. Ranasinghe, RamBoAttack: A Robust and Query Efficient Deep Neural Network Decision Exploit, The Network and Distributed System Security Symposium (NDSS) 2022. Link]
D. Teney, E. Abbasnejad, A. van den Hengel, Unshuffling data for improved generalization in visual question answering, International Conference on Computer Vision (ICCV), 2021 [Link]
M. Zhang, S. Su, S. Pan, X. Chang, E. Abbasnejad, G. Haffari, iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients, International Conference on Machine Learning (ICML), 2021.
I. Nassar, S. Herath, E. Abbasnejad, W. Buntine, G. Haffari, All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), [Oral], 2021. [Link]
A. Parvaneh, E. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead, IEEE Transactions on Multimedia, 2021. [Link]
M. Neshata, M. Nezhad, E. Abbasnejad, S. Mirjalilide, D. Groppi, A. Heydari, L. Tjernberg,D. Garcia, B. Alexandera, Q. Shi, M. Wagner, Wind turbine power output prediction using a new hybrid neuro-evolutionary method, Energy, Volume 229, 2021. [Link]
M. Neshata, M. Nezhad, E. Abbasnejad, S. Mirjalilide, L. Tjernberg,D. Garcia, B. Alexandera, M. Wagner, A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm, Energy Conversion and Management, 2021. [Link]
A. Parvaneh, E. Abbasnejad, D. Teney, Q. Shi, A. van den Hengel, Counterfactual Vision-and-Language Navigation: Unraveling the Unseen, Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. [Spotlight] [Link]
D. Teney, K. Kafle, R. Shrestha, E. Abbasnejad, C. Kanan, A. van den Hengel, On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law, Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. [Link]
D. Teney, E. Abbasnejad, A. van den Hengel, Learning what makes a difference from counterfactual examples and gradient supervision, European Conference on Computer Vision (ECCV), 2020 [Link]
E. Abbasnejad, D. Teney, A. Parvaneh, Q. Shi, A. van den Hengel, Counterfactual Vision and Language Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Oral][Link]
E. Abbasnejad, I. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Language Reasoning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Link]
B. Doan, E. Abbasnejad, D. Ranasinghe, Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network, 36th Annual Computer Security Applications Conference (ACSAC), 2020. [Link]
M. Kazemi, Q. Wu, E. Abbasnejad, Q. Shi, Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021. [Link]
E. Abbasnejad, Q. Shi, A. van den Hengel, L. Liu, GADE: A Generative Adversarial Approach to Density Estimation and its Applications, International Journal of Computer Vision 128 (10), 2731-2743, 2020. [Link]
E. Abbasnejad, Q. Shi, A. van den Hengel, L. Liu, A Generative Adversarial Density Estimator, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019. [Oral] [Link]
E. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, L. Liu, What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019. [Link]
E. Abbasnejad, A. Dick, Q. Shi, A. van den Hengel, Active Learning from Noisy Tagged Images, In Proceedings of British Machine Vision Conference (BMVC), Newcastle, UK, 2018. [Link]
M. Abdi, E. Abbasnejad, C. P. Lim, S. Nahavandi, 3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space, In Proceedings of British Machine Vision Conference (BMVC). Newcastle, UK, 2018. [Oral] [Link]
A. Abedin, E. Abbasnejad, Q. Shi, D. Ranasinghe, H. Rezatofighi, Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables , In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems (MobiQuitous), New York, NY, USA, 2018. [Link]
E. Abbasnejad, A. Dick, A. van den Hengel, Infinite Variational Autoencoder for Semi-Supervised Learning , The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017. [link] [supplement]
A. Niculescu-Mizil, E. Abbasnejad, Label Filters for Large Scale Multilabel Classification , In Proceedings of The 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, USA, 2017. [Link]
H. Rezatofighi, V. Kumar, A. Milan, E. Abbasnejad, A. Dick, I. Reid, DeepSetNet: Predicting Sets with Deep Neural Networks, The IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017. [link]
A. Khoshkbarforoushha, R. Ranjan, R. Gaire, E. Abbasnejad, L. Wang, A. Zomaya, Distribution Based Workload Modelling of Continuous Queries in Clouds , In IEEE Transactions on Emerging Topics in Computing, 2016. [link]
E. Abbasnejad, J. Domke, S. Sanner, Loss-calibrated Monte Carlo Action Selection , In Proceedings of the 26th Conference on Artificial Intelligence (AAAI), Austin, USA, 2015. [link]
H. Afshar, S. Sanner, E. Abbasnejad, Linear-time Gibbs Sampling in Piecewise Graphical Models , In Proceedings of the 26th Conference on Artificial Intelligence (AAAI), Austin, USA, 2015. [link]
E. Abbasnejad, E. V. Bonilla, S. Sanner, Decision-theoretic Sparsification for Gaussian Process Preference Learning , Proceedings of the Machine Learning and Knowledge Discovery in Databases - European Conference (ECML PKDD). Prague, Czech Republic, 2013. [link]
E. Abbasnejad, S. Sanner, E. V. Bonilla, P. Poupart, Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes , In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013. [link] [dataset]

Publications

Below, you'll find select publications organised by year. For questions on any specific publication, feel free to message me.

Selected Publications [Full list in Google Scholar]

C. Rodriguez-Opazo, E. Abbasnejad, D. Teney, H. Damirchi, E. Marrese-Taylor, A. van den Hengel, Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling, in The International Conference on Learning Representations (ICLR), 2025.

P. Albert, F.Z. Zhang, C. Rodriguez-Opazo, H. Saratchandran, A. van den Hengel, E. Abbasnejad, RandLoRA: Full rank parameter-efficient fine-tuning of large models, in The International Conference on Learning Representations (ICLR), 2025.

A. Sonthalia, A. Rubinstein, E. Abbasnejad, S. Joon Oh, Do Deep Neural Network Solutions Form a Star Domain?, in The International Conference on Learning Representations (ICLR), 2025.

A. Sonthalia, A. Rubinstein, E. Abbasnejad, S. Joon Oh, Do Deep Neural Network Solutions Form a Star Domain?, in The Thirteenth International Conference on Learning Representations (ICLR), 2025.

B. Doan, A. Shamsi, X. Guo, A. Mohammadi, H. Alinejad-Rokny, D. Sejdinovic, D. Ranasinghe, E. Abbasnejad, Bayesian Low-rank Learning (Bella): A Practical Approach to Bayesian Neural Networks, in The 39th Annual AAAI Conference on Artificial Intelligence, 2025.

F. Zhang, P. Albert, C. Rodriguez-Opazo, A. van den Hengel, E. Abbasnejad, Knowledge Composition using Task Vectors with Learned Anisotropic Scaling, in Conference on Neural Information Processing Systems (NeurIPS), 2024.

B. Repasky, E. Abbasnejad, A. Dick, BLURD: Benchmarking and Learning using a Unified Rendering and Diffusion Model, in Conference on Neural Information Processing Systems (NeurIPS), 2024.

D. Teney, J. Wang, E Abbasnejad, Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup, International Conference on Machine Learning (ICML) 2024. [Link]

D. Teney, A. Nicolicioiu, V. Hartmann, E Abbasnejad, Neural Redshift: Random Networks are not Random Functions, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Oral] 2024. [Link]

V. Vo, E. Abbasnejad, D. Ranasinghe, BRUSLEATTACK: Query-efficient Score-based Sparse Adversarial Attack, The Twelfth International Conference on Learning Representations (ICLR) 2024. [Link]

D. Teney, Y. Lin, S. J. Oh, E. Abbasnejad, ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets, Conference on Neural Information Processing Systems (NeurIPS) 2023. [Link]

M. McDonnell, D. Gong, A. Parveneh, E. Abbasnejad, A. van den Hengel, RanPAC: Random Projections and Pre-trained Models for Continual Learning, Conference on Neural Information Processing Systems (NeurIPS) 2023. [Link]

S. Herath, B. Fernando, E. Abbasnejad, M. Hayat, S. Khadivi, M. Harandi, H. Rezatofighi, G. Haffari, Energy-based Self-Training and Normalization for Unsupervised Domain Adaptation, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2023. [Link]

I. Nassar, M. Hayat, E. Abbasnejad, H. Rezatofighi, G. Haffari, ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) [Highlight], 2023. [Link]

B. G. Doan, S. Yang, P. Montague, O. De Vel, T. Abraham, E. Abbasnejad, D. Ranasinghe, Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness, Association for the Advancement of Artificial Intelligence (AAAI) 2023. [Link]

Z. Zhang, I. Ng, D. Gong, Y. Liu, E. Abbasnejad, M. Gong, K. Zhang, J. Shi, Truncated Matrix Power Iteration for Differentiable DAG Learning, Conference on Neural Information Processing Systems (NeurIPS) 2022. [Link]

D. Teney, M. Peyrard, E. Abbasnejad, Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning, European Conference on Computer Vision (ECCV) 2022. [Link]

B. G. Doan, E. Abbasnejad, D. Ranasinghe, Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense, International Conference on Machine Learning (ICML) 2022. [Link]

D. Teney, E. Abbasnejad, S. Lucey, A. van den Hengel, Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022. [Link]

A. Parvaneh, E. Abbasnejad, D. Teney, R. Haffari, A. van den Hengel, J. Shi, Active Learning by Feature Mixing, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022. [Link]

V. Q. Vo, E. Abbasnejad, D. Ranasinghe, Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models, International Conference on Representation Learning (ICLR) 2022. [Link]

V. Q. Vo, E. Abbasnejad, D. Ranasinghe, RamBoAttack: A Robust and Query Efficient Deep Neural Network Decision Exploit, The Network and Distributed System Security Symposium (NDSS) 2022. Link]

D. Teney, E. Abbasnejad, A. van den Hengel, Unshuffling data for improved generalization in visual question answering, International Conference on Computer Vision (ICCV), 2021 [Link]

M. Zhang, S. Su, S. Pan, X. Chang, E. Abbasnejad, G. Haffari, iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients, International Conference on Machine Learning (ICML), 2021.

I. Nassar, S. Herath, E. Abbasnejad, W. Buntine, G. Haffari, All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), [Oral], 2021. [Link]

A. Parvaneh, E. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead, IEEE Transactions on Multimedia, 2021. [Link]

M. Neshata, M. Nezhad, E. Abbasnejad, S. Mirjalilide, D. Groppi, A. Heydari, L. Tjernberg,D. Garcia, B. Alexandera, Q. Shi, M. Wagner, Wind turbine power output prediction using a new hybrid neuro-evolutionary method, Energy, Volume 229, 2021. [Link]

M. Neshata, M. Nezhad, E. Abbasnejad, S. Mirjalilide, L. Tjernberg,D. Garcia, B. Alexandera, M. Wagner, A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm, Energy Conversion and Management, 2021. [Link]

A. Parvaneh, E. Abbasnejad, D. Teney, Q. Shi, A. van den Hengel, Counterfactual Vision-and-Language Navigation: Unraveling the Unseen, Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. [Spotlight] [Link]

D. Teney, K. Kafle, R. Shrestha, E. Abbasnejad, C. Kanan, A. van den Hengel, On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law, Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), 2020. [Link]

D. Teney, E. Abbasnejad, A. van den Hengel, Learning what makes a difference from counterfactual examples and gradient supervision, European Conference on Computer Vision (ECCV), 2020 [Link]

E. Abbasnejad, D. Teney, A. Parvaneh, Q. Shi, A. van den Hengel, Counterfactual Vision and Language Learning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Oral][Link]

E. Abbasnejad, I. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Language Reasoning, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [Link]

B. Doan, E. Abbasnejad, D. Ranasinghe, Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network, 36th Annual Computer Security Applications Conference (ACSAC), 2020. [Link]

M. Kazemi, Q. Wu, E. Abbasnejad, Q. Shi, Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021. [Link]

E. Abbasnejad, Q. Shi, A. van den Hengel, L. Liu, GADE: A Generative Adversarial Approach to Density Estimation and its Applications, International Journal of Computer Vision 128 (10), 2731-2743, 2020. [Link]

E. Abbasnejad, Q. Shi, A. van den Hengel, L. Liu, A Generative Adversarial Density Estimator, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019. [Oral] [Link]

E. Abbasnejad, Q. Wu, Q. Shi, A. van den Hengel, L. Liu, What's to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019. [Link]

E. Abbasnejad, A. Dick, Q. Shi, A. van den Hengel, Active Learning from Noisy Tagged Images, In Proceedings of British Machine Vision Conference (BMVC), Newcastle, UK, 2018. [Link]

M. Abdi, E. Abbasnejad, C. P. Lim, S. Nahavandi, 3D Hand Pose Estimation using Simulation and Partial-Supervision with a Shared Latent Space, In Proceedings of British Machine Vision Conference (BMVC). Newcastle, UK, 2018. [Oral] [Link]

A. Abedin, E. Abbasnejad, Q. Shi, D. Ranasinghe, H. Rezatofighi, Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables , In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems (MobiQuitous), New York, NY, USA, 2018. [Link]

E. Abbasnejad, A. Dick, A. van den Hengel, Infinite Variational Autoencoder for Semi-Supervised Learning , The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017. [link] [supplement]

A. Niculescu-Mizil, E. Abbasnejad, Label Filters for Large Scale Multilabel Classification , In Proceedings of The 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, USA, 2017. [Link]

H. Rezatofighi, V. Kumar, A. Milan, E. Abbasnejad, A. Dick, I. Reid, DeepSetNet: Predicting Sets with Deep Neural Networks, The IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017. [link]

A. Khoshkbarforoushha, R. Ranjan, R. Gaire, E. Abbasnejad, L. Wang, A. Zomaya, Distribution Based Workload Modelling of Continuous Queries in Clouds , In IEEE Transactions on Emerging Topics in Computing, 2016. [link]

E. Abbasnejad, J. Domke, S. Sanner, Loss-calibrated Monte Carlo Action Selection , In Proceedings of the 26th Conference on Artificial Intelligence (AAAI), Austin, USA, 2015. [link]

H. Afshar, S. Sanner, E. Abbasnejad, Linear-time Gibbs Sampling in Piecewise Graphical Models , In Proceedings of the 26th Conference on Artificial Intelligence (AAAI), Austin, USA, 2015. [link]

E. Abbasnejad, E. V. Bonilla, S. Sanner, Decision-theoretic Sparsification for Gaussian Process Preference Learning , Proceedings of the Machine Learning and Knowledge Discovery in Databases - European Conference (ECML PKDD). Prague, Czech Republic, 2013. [link]

E. Abbasnejad, S. Sanner, E. V. Bonilla, P. Poupart, Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes , In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, 2013. [link] [dataset]