| Accelerating large-scale inference with anisotropic vector quantization R Guo, P Sun, E Lindgren, Q Geng, D Simcha, F Chern, S Kumar International Conference on Machine Learning, 3887-3896, 2020 | 447 | 2020 |
| Composing Normalizing Flows for Inverse Problems J Jay Whang, E Lindgren, A Dimakis International Conference on Machine Learning (ICML), 2021 | 75* | 2021 |
| Experimental design for cost-aware learning of causal graphs E Lindgren, M Kocaoglu, AG Dimakis, S Vishwanath Advances in Neural Information Processing Systems 31, 2018 | 51 | 2018 |
| Leveraging sparsity for efficient submodular data summarization E Lindgren, S Wu, AG Dimakis Advances in Neural Information Processing Systems 29, 2016 | 42* | 2016 |
| Efficient training of retrieval models using negative cache E Lindgren, S Reddi, R Guo, S Kumar Advances in Neural Information Processing Systems 34, 4134-4146, 2021 | 31 | 2021 |
| A rule-based design specification language for synthetic biology E Oberortner, S Bhatia, E Lindgren, D Densmore ACM Journal on Emerging Technologies in Computing Systems (JETC) 11 (3), 1-19, 2014 | 21 | 2014 |
| On robust learning of ising models EM Lindgren, V Shah, Y Shen, AG Dimakis, A Klivans NeurIPS Workshop on Relational Representation Learning, 2019 | 9 | 2019 |
| Exact map inference by avoiding fractional vertices EM Lindgren, AG Dimakis, A Klivans International Conference on Machine Learning, 2120-2129, 2017 | 5* | 2017 |
| Efficient Training of Embedding Models Using Negative Cache EM Lindgren, SJ Reddi, R Guo, S Kumar US Patent App. 17/983,130, 2023 | 1 | 2023 |
| Drowning in Documents: Consequences of Scaling Reranker Inference M Jacob, E Lindgren, M Zaharia, M Carbin, O Khattab, A Drozdov arXiv preprint arXiv:2411.11767, 2024 | | 2024 |
| Combinatorial Optimization for Graphical Structures in Machine Learning EM Lindgren The University of Texas at Austin, 2019 | | 2019 |
| Rescaled JL Embedding S Wu, E Lindgren | | |
