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Matt J. KusnerDepartment of Computer Engineering and Software Engineering
Profile in the Directory of Experts of Polytechnique Montréal
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Alabdulmohsin, I., Chiou, N., D'Amour, A., Gretton, A., Koyejo, S., Kusner, M. J., Pfohl, S. R., Salaudeen, O., Schrouff, J., & Tsai, K. (2023, April). Adapting to latent subgroup shifts via concepts and proxies [Paper]. 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Palau de Congressos, Valencia, Spain. Published in Proceedings of Machine Learning Research, 206. External link
Agrawal, N., Bell, J., Gascón, A., & Kusner, M. J. (2021, November). MPC-friendly commitments for publicly verifiable covert security [Paper]. ACM SIGSAC Conference on Computer and Communications Security (CCS 2021). External link
Agrawal, N., Shamsabadi, A. S., Kusner, M. J., & Gascón, A. (2019, November). QUOTIENT: two-party secure neural network training and prediction [Paper]. ACM SIGSAC Conference on Computer and Communications Security (CCS 2019), London, United Kingdom. External link
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M. H. S., & Hernández-Lobato, J. M. (2020, December). Barking up the right tree: an approach to search over molecule synthesis DAGs [Paper]. 34th International Conference on Neural Information Processing Systems (NIPS 2020), Vancouver, British Columbia, Canada. External link
Bradshaw, J., Kusner, M. J., Paige, B., Segler, M. H. S., & Hernández-Lobato, J. M. (2019, May). A generative model for electron paths [Paper]. 7th International Conference on Learning Representations (ICLR 2019), New Orleans, Louisiana, USA (19 pages). External link
Bradshaw, J., Paige, B., Kusner, M. J., Segler, M. H. S., & Hernández-Lobato, J. M. (2019, December). A model to search for synthesizable molecules [Paper]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. External link
Gopakumar, V., Gray, A., Zanisi, L., Nunn, T., Giles, D., Kusner, M. J., Pamela, S., & Deisenroth, M. P. (2025, February). Calibrated Physics-Informed Uncertainty Quantification [Paper]. 42nd International Conference on Machine Learning (PMLR 2025), Vancouver, BC, Canada. External link
Gopakumar, V., Pamela, S., Zanisi, L., Li, Z., Gray, A., Brennand, D., Bhatia, N., Stathopoulos, G., Kusner, M. J., Deisenroth, M. P., & Anandkumar, A. (2024). Plasma surrogate modelling using Fourier neural operators. Nuclear Fusion, 64(5), 056025 (36 pages). External link
Gultchin, L., Watson, D. S., Kusner, M. J., & Silva, R. (2021, July). Operationalizing complex causes: a pragmatic view of mediation [Paper]. 38th International Conference on Machine Learning (ICML 2021). Published in Proceedings of Machine Learning Research, 139. External link
Gultchin, L., Kusner, M. J., Kanade, V., & Silva, R. (2020, August). Differentiable causal backdoor discovery [Paper]. 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020). Published in Proceedings of Machine Learning Research, 108. External link
Gardner, J. R., Kusner, M. J., Xu, Z., Weinberger, K. Q., & Cunningham, J. P. (2014, June). Bayesian optimization with inequality constraints [Paper]. 31st International Conference on Machine Learning (ICML 2014), Beijing, China. Published in Proceedings of Machine Learning Research, 32(2). External link
Huang, G., Guo, C., Kusner, M. J., Sun, Y., Weinberger, K. Q., & Sha, F. (2016, December). Supervised word mover's distance [Paper]. 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. External link
Janz, D., Westhuizen, J. , Paige, B., Kusner, M. J., & Hernández-Lobato, J. M. (2018, July). Learning a Generative Model for Validity in Complex Discrete Structures [Paper]. 35th International Conference on Machine Learning (ICLR 2018), Stockholm, Sweden (12 pages). External link
Kaddour, J., Lynch, A., Liu, Q., Kusner, M. J., & Silva, R. (2025). Causal Machine Learning: A Survey and Open Problems [Discussion or Letter]. Foundations and Trends® in Optimization, 9(1-2), 1-247. External link
Kaddour, J., Key, O., Nawrot, P., Minervini, P., & Kusner, M. J. (2023, December). No train no gain: revisiting efficient training algorithms for transformer-based language models [Paper]. 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, USA. External link
Kaddour, J., Liu, L., Silva, R., & Kusner, M. J. (2022, November). When Do Flat Minima Optimizers Work? [Paper]. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana. External link
Kaddour, J., Zhu, Y., Liu, Q., Kusner, M. J., & Silva, R. (2021, December). Causal effect inference for structured treatments [Paper]. 35th Annual Conference on Neural Information Processing Systems (NeurIPS 2021). External link
Kilbertus, N., Kusner, M. J., & Silva, R. (2020, December). A class of algorithms for general instrumental variable models [Paper]. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. External link
Kusner, M. J., & Loftus, J. R. (2020). The Long Road to Fairer Algorithms. Nature, 578(7793), 34-36. External link
Kusner, M. J., Russell, C., Loftus, J. R., & Silva, R. (2019, June). Making Decisions that Reduce Discriminatory Impact [Paper]. 36th International Conference on Machine Learning (ICML 2019), Long Beach, California, USA. Published in Proceedings of Machine Learning Research, 97. External link
Kilbertus, N., Ball, P. J., Kusner, M. J., Weller, A., & Silva, R. (2019, July). The Sensitivity of Counterfactual Fairness to Unmeasured Confounding [Paper]. 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel. Published in Proceedings of Machine Learning Research, 115. External link
Kilbertus, N., Gascón, A., Kusner, M. J., Veale, M., Gummadi, K. P., & Weller, A. (2018, July). Blind justice: fairness with encrypted sensitive attributes [Paper]. 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden. Published in Proceedings of Machine Learning Research, 80. External link
Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017, December). Counterfactual fairness [Paper]. 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. External link
Kusner, M. J., Paige, B., & Hernández-Lobato, J. M. (2017, August). Grammar Variational Autoencoder [Paper]. 34th International Conference on Machine Learning (ICML 2017), Sydney, Australia. Published in Proceedings of Machine Learning Research, 70. External link
Kusner, M. J. (2016). Learning in the real world: constraints on cost, space, and privacy [Ph.D. thesis, McKelvey School of Engineering]. External link
Kusner, M. J., Sun, Y., Sridharan, K., & Weinberger, K. Q. (2016, May). Private causal inference [Paper]. 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), Cadiz, Spain. External link
Kusner, M. J., Gardner, J. R., Garnett, R., & Weinberger, K. Q. (2015, July). Differentially private bayesian optimization [Paper]. 32nd International Conference on Machine Learning (ICML'15), Lile, France. External link
Kusner, M. J., Sun, Y., Kolkin, N. I., & Weinberger, K. Q. (2025, July). From word embeddings to document distances [Paper]. 32nd International Conference on Machine Learning (ICML 2015), Lile, France. External link
Kusner, M. J., Chen, W., Zhou, Q., Xu, Z., Weinberger, K. Q., & Chen, Y. (2014, July). Feature-cost sensitive learning with submodular trees of classifiers [Paper]. 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Québec, Québec, Canada. External link
Kusner, M. J., Tyree, S., Weinberger, K. Q., & Agrawal, K. (2014, June). Stochastic neighbor compression [Paper]. 31st International Conference on Machine Learning (ICML 2014), Beijing, China. External link
Liu, Q., Kusner, M. J., & Blunsom, P. (2021, June). Counterfactual Data Augmentation for Neural Machine Translation [Paper]. Conference of the North-American-Chapter of the Association-for-Computational-Linguistics - Human Language Technologies (NAACL-HLT 2021). External link
Maus, N. T., Jones, H. T., Moore, J. S., Kusner, M. J., Bradshaw, J., & Gardner, J. R. (2022, November). Local Latent Space Bayesian Optimization over Structured Inputs [Paper]. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, USA (14 pages). External link
Mastouri, A., Zhu, Y., Gultchin, L., Korba, A., Silva, R., Kusner, M. J., Gretton, A., & Muandet, K. (2021, July). Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction [Paper]. 38th International Conference on Machine Learning (ICML 2021). Published in Proceedings of Machine Learning Research, 139. External link
Malkomes, G., Kusner, M. J., Chen, W., Weinberger, K. Q., & Moseley, B. (2015, December). Fast distributed k-center clustering with outliers on massive data [Paper]. 29th International Conference on Neural Information Processing Systems (NIPS 2015), Montréal, Québec, Canada. External link
Narasiah, H., Kitouni, O., Scorsoglio, A., Sturdza, B. K., Hatcher, S., Katcher, K., Khalesi, J., Garcia, D., & Kusner, M. J. (2024). Machine learning discovery of cost-efficient dry cooler designs for concentrated solar power plants. Scientific Reports, 14(1), 19086. External link
Padh, K., Zeitler, J., Watson, D. S., Kusner, M. J., Silva, R., & Kilbertus, N. (2023, April). Stochastic Causal Programming for Bounding Treatment Effects [Paper]. 2nd Conference on Causal Learning and Reasoning (CCLR 2023), Tübingen, Germany (35 pages). Published in Proceedings of Machine Learning Research, 213. External link
Richter, L., He, X., Minervini, P., & Kusner, M. J. (2025, April). An auditing test to detect behavioral shift in language models [Paper]. 13th International Conference on Learning Representations (ICLR 2025), Singapore, Singapore. External link
Russell, C., Kusner, M. J., Loftus, J. R., & Silva, R. (2017, December). When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness [Paper]. 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Red Hook, New York. USA. External link
Sanyal, A., Kusner, M. J., Gascón, A., & Kanade, V. (2018, July). TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service [Paper]. 35th International Conference on Machine Learning (ICML 2018), Stockholm, Sweden. Published in Proceedings of Machine Learning Research, 80. External link
Tsai, K., Pfohl, S. R., Salaudeen, O., Chiou, N., Kusner, M. J., D'amour, A., Koyejo, S., & Gretton, A. (2024, May). Proxy Methods for Domain Adaptation [Paper]. 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), Valencia, Spain (29 pages). Published in Proceedings of Machine Learning Research. External link
Wang, H., Liu, Q., Yue, X., Lasenby, J., & Kusner, M. J. (2021, October). Unsupervised Point Cloud Pre-training via Occlusion Completion [Paper]. 18th IEEE/CVF International Conference on Computer Vision (ICCV 2021), Montreal, Quebec, Canada. External link
Xu, Z., Kusner, M. J., Weinberger, K. Q., Chen, M., & Chapelle, O. (2014). Classifier cascades and trees for minimizing feature evaluation cost. Journal of Machine Learning Research, 15(1), 2113-2144. External link
Xu, Z., Kusner, M. J., Huang, G., & Weinberger, K. Q. (2013, June). Anytime representation learning [Paper]. 30th International Conference on Machine Learning (ICML 2013), Atlanta, Georgia, USA. External link
Xu, Z., Kusner, M. J., Weinberger, K. Q., & Chen, M. (2013, June). Cost-sensitive tree of classifiers [Paper]. 30th International Conference on Machine Learning (ICML 2013), Atlanta, Georgia, USA. External link
Zhu, Y., De Souza, D. A., Shi, Z., Yang, M., Minervini, P., Kusner, M. J., & D'Amour, A. (2025, February). When Can Proxies Improve the Sample Complexity of Preference Learning? [Paper]. 42nd International Conference on Machine Learning (PMLR 2025), Vancouver, BC, Canada. External link
Zhu, Y., Gultchin, L., Gretton, A., Kusner, M. J., & Silva, R. (2022, August). Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach [Paper]. 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), Eindhoven, The Netherlands. Published in Proceedings of Machine Learning Research, 180. External link
Zantedeschi, V., Kaddour, J., Franceschi, L., Kusner, M. J., & Niculae, V. (2022, April). DAG Learning on the Permutahedron [Poster]. 10th International Conference on Learning Representations (ICLR 2023) (9 pages). External link
Zantedeschi, V., Kusner, M. J., & Niculae, V. (2021, July). Learning Binary Decision Trees by Argmin Differentiation [Paper]. Unspecified. External link
