| Training independent subnetworks for robust prediction M Havasi, R Jenatton, S Fort, JZ Liu, J Snoek, B Lakshminarayanan, ... arXiv preprint arXiv:2010.06610, 2020 | 224 | 2020 |
| Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 110 | 2021 |
| Inference in deep Gaussian processes using stochastic gradient Hamiltonian Monte Carlo M Havasi, JM Hernández-Lobato, JJ Murillo-Fuentes Advances in neural information processing systems 31, 2018 | 110 | 2018 |
| Minimal random code learning: Getting bits back from compressed model parameters M Havasi, R Peharz, JM Hernández-Lobato arXiv preprint arXiv:1810.00440, 2018 | 76 | 2018 |
| Addressing leakage in concept bottleneck models M Havasi, S Parbhoo, F Doshi-Velez Advances in Neural Information Processing Systems 35, 23386-23397, 2022 | 58 | 2022 |
| Compressing images by encoding their latent representations with relative entropy coding G Flamich, M Havasi, JM Hernández-Lobato Advances in Neural Information Processing Systems 33, 16131-16141, 2020 | 46 | 2020 |
| Determining optimal coherency interface for many-accelerator socs using bayesian optimization K Bhardwaj, M Havasi, Y Yao, DM Brooks, JMH Lobato, GY Wei IEEE Computer Architecture Letters 18 (2), 119-123, 2019 | 13 | 2019 |
| What makes a good explanation?: A harmonized view of properties of explanations Z Chen, V Subhash, M Havasi, W Pan, F Doshi-Velez arXiv preprint arXiv:2211.05667, 2022 | 11 | 2022 |
| A comprehensive methodology to determine optimal coherence interfaces for many-accelerator SoCs K Bhardwaj, M Havasi, Y Yao, DM Brooks, JM Hernández-Lobato, GY Wei Proceedings of the ACM/IEEE International Symposium on Low Power Electronics …, 2020 | 10 | 2020 |
| Refining the variational posterior through iterative optimization M Havasi, J Snoek, D Tran, J Gordon, JM Hernández-Lobato | 8 | 2021 |
| Deep Gaussian processes with decoupled inducing inputs M Havasi, JM Hernández-Lobato, JJ Murillo-Fuentes arXiv preprint arXiv:1801.02939, 2018 | 8 | 2018 |
| Compression without quantization G Flamich, M Havasi, JM Hernández-Lobato Proc. Int. Conf. Learn. Representations, 2019 | 4 | 2019 |
| Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding, 2020 G Flamich, M Havasi, JM Hernández-Lobato Advances in Neural Information Processing Systems 34 (4), 12, 0 | 4 | |
| Learning optimal summaries of clinical time-series with concept bottleneck models C Wu, S Parbhoo, M Havasi, F Doshi-Velez Machine Learning for Healthcare Conference, 648-672, 2022 | 3 | 2022 |
| Advances in compression using probabilistic models M Havasi | 3 | 2021 |
| Does the explanation satisfy your needs?: A unified view of properties of explanations Z Chen, V Subhash, M Havasi, W Pan, F Doshi-Velez arXiv preprint arXiv:2211.05667, 2022 | 2 | 2022 |
| What Makes a Good Explanation?: A Harmonized View of Properties of Explanations V Subhash, Z Chen, M Havasi, W Pan, F Doshi-Velez Progress and Challenges in Building Trustworthy Embodied AI, 2022 | 1 | 2022 |
| Sampling the variational posterior with local refinement M Havasi, J Snoek, D Tran, J Gordon, JM Hernández-Lobato Entropy 23 (11), 1475, 2021 | 1 | 2021 |
| Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles B Phan, B Amos, I Gat, M Havasi, M Muckley, K Ullrich arXiv preprint arXiv:2410.09303, 2024 | | 2024 |
| Understanding and Mitigating Tokenization Bias in Language Models B Phan, M Havasi, M Muckley, K Ullrich arXiv preprint arXiv:2406.16829, 2024 | | 2024 |
