Browse by UCL people
Group by: Type | Date
Jump to: Proceedings paper | Working / discussion paper
Number of items: 20.
Proceedings paper
Agrawal, N;
Kusner, MJ;
Shamsabadi, AS;
Gascón, A;
(2019)
QUOTIENT: Two-party secure neural network training and prediction.
In:
Proceedings of the ACM Conference on Computer and Communications Security.
(pp. pp. 1231-1247).
ACM: New York, NY, USA.
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Bradshaw, J;
Kusner, MJ;
Paige, B;
Segler, MHS;
Hernández-Lobato, JM;
(2019)
Generating molecules via chemical reactions.
In:
Proceedings of the Seventh International Conference on Learning Representations (ICLR 2019).
International Conference on Learning Representations (ICLR): New Orleans, LA, USA.
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Bradshaw, J;
Kusner, MJ;
Paige, B;
Segler, MHS;
Hernández-Lobato, JM;
(2019)
A generative model for electron paths.
In:
Proceedings of the Seventh International Conference on Learning Representations (ICLR 2019).
International Conference on Learning Representations (ICLR): New Orleans, LA, USA.
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Bradshaw, J;
Paige, B;
Kusner, MJ;
Segler, MHS;
Hernández-Lobato, JM;
(2019)
A Model to Search for Synthesizable Molecules.
In: Wallach, H and Larochelle, H and Beygelzimer, A and d'Alché-B, F, (eds.)
Proceedings of Advances in Neural Information Processing Systems 32 (NIPS 2019).
NIPS
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Gultchin, L;
Kusner, M;
Kanade, V;
Silva, R;
(2020)
Differentiable Causal Backdoor Discovery.
In: Chiappa, S and Calandra, R, (eds.)
Proceedings of the International Conference on Artificial Intelligence and Statistics.
PMLR: Online Conference.
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Janz, D;
Van Der Westhuizen, J;
Paige, B;
Kusner, MJ;
Hernández-Lobato, JM;
(2018)
Learning a generative model for validity in complex discrete structures.
In:
Proceedings of the Sixth International Conference on Learning Representations (ICLR 2018).
International Conference on Learning Representations (ICLR): Vancouver, Canada.
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Kilbertus, N;
Ball, P;
Kusner, M;
Weller, A;
Silva, R;
(2019)
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding.
In: Globerson, A and Silva, R, (eds.)
Proceedings of the 35th Uncertainty in Artificial Intelligence Conference (UAI 2019).
AUAI Press: Tel Aviv, Israel.
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Kilbertus, N;
Gascon, A;
Kusner, M;
Veale, M;
Gummadi, K;
Weller, A;
(2018)
Blind Justice: Fairness with Encrypted Sensitive Attributes.
In: Dy, J and Krause, A, (eds.)
Proceedings of the 35th International Conference on Machine Learning.
(pp. pp. 2635-2644).
International Machine Learning Society (IMLS).: Stockholm, Sweden.
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Kilbertus, N;
Kusner, M;
Silva, R;
(2020)
A class of algorithms for general instrumental variable models.
In:
Proceedings of the Advances in Neural Information Processing Systems (NeurIPS 2020).
Advances in Neural Information Processing Systems (NeurIPS 2020): Virtual conference.
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Kusner, M;
Russell, C;
Loftus, J;
Silva, R;
(2019)
Making Decisions that Reduce Discriminatory Impacts.
In: Xing, E, (ed.)
Proceedings of the 36th International Conference on Machine Learning (IML 2019).
PMLR (Proceedings of Machine Learning Research): Long Beach, CA, USA.
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Kusner, MJ;
Paige, B;
Hemández-Lobato, JM;
(2017)
Grammar variational autoencoder.
In:
Proceedings of Machine Learning Research.
(pp. pp. 1945-1954).
PMLR: Sydney, Australia.
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Kusner, MJ;
Russell, C;
Loftus, J;
Silva, R;
(2017)
Counterfactual Fairness.
In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 30 (NIPS 2017).
NIPS Proceedings: Long Beach, CA, USA.
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Russell, C;
Kusner, M;
Loftus, C;
Silva, R;
(2017)
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.
In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 30 (NIPS 2017).
NIPS Proceedings: Long Beach, CA, USA.
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Sanyal, A;
Kusner, MJ;
Gascón, A;
Kanade, V;
(2018)
TAPAS: Tricks to accelerate (encrypted) prediction as a service.
In:
Proceedings of the Thirty-fifth International Conference on Machine Learning.
(pp. pp. 4490-4499).
PMLR: Stockholm, Sweden.
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Wang, H;
Liu, Q;
Yue, X;
Lasenby, J;
Kusner, MJ;
(2021)
Unsupervised Point Cloud Pre-training via Occlusion Completion.
In:
Proceedings of the IEEE International Conference on Computer Vision.
(pp. pp. 9762-9772).
Institute of Electrical and Electronics Engineers (IEEE)
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Zantedeschi, V;
Falasca, F;
Douglas, A;
Strange, R;
Kusner, MJ;
Watson-Parris, D;
(2019)
Cumulo: A Dataset for Learning Cloud Classes.
In:
Proceedings of the NeurIPS 2019 Workshop: Tackling Climate Change with Machine Learning.
(pp. pp. 1-11).
NeurIPS: Vancouver, Canada.
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Zantedeschi, Valentina;
Kusner, Matt J;
Niculae, Vlad;
(2021)
Learning Binary Decision Trees by Argmin Differentiation.
In: Meila, M and Zhang, T, (eds.)
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 12298-12309).
PMLR
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Working / discussion paper
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Gardner, JR;
Upchurch, P;
Kusner, MJ;
Li, Y;
Weinberger, KQ;
Bala, K;
Hopcroft, JE;
(2016)
Deep Manifold Traversal: Changing Labels with Convolutional Features.
ArXiv: Ithaca, NY, USA.
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Kusner, MJ;
Hernández-Lobato, JM;
(2016)
GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution.
arXiv.org: Ithaca (NY), USA.
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Loftus, JR;
Russell, C;
Kusner, MJ;
Silva, R;
(2018)
Causal Reasoning for Algorithmic Fairness.
ArXiv: Ithaca, NY, USA.
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