| Large-scale log-determinant computation through stochastic Chebyshev expansions I Han, D Malioutov, J Shin International Conference on Machine Learning, 908-917, 2015 | 112 | 2015 |
| Approximating spectral sums of large-scale matrices using stochastic Chebyshev approximations I Han, D Malioutov, H Avron, J Shin SIAM Journal on Scientific Computing 39 (4), A1558-A1585, 2017 | 87 | 2017 |
| Kdeformer: Accelerating transformers via kernel density estimation A Zandieh, I Han, M Daliri, A Karbasi International Conference on Machine Learning, 40605-40623, 2023 | 45 | 2023 |
| Hyperattention: Long-context attention in near-linear time I Han, R Jayaram, A Karbasi, V Mirrokni, DP Woodruff, A Zandieh arXiv preprint arXiv:2310.05869, 2023 | 42 | 2023 |
| Scaling neural tangent kernels via sketching and random features A Zandieh, I Han, H Avron, N Shoham, C Kim, J Shin Advances in Neural Information Processing Systems 34, 1062-1073, 2021 | 30 | 2021 |
| Faster greedy MAP inference for determinantal point processes I Han, P Kambadur, K Park, J Shin International Conference on Machine Learning, 1384-1393, 2017 | 20 | 2017 |
| Stochastic chebyshev gradient descent for spectral optimization I Han, H Avron, J Shin Advances in Neural Information Processing Systems 31, 2018 | 18 | 2018 |
| BrainLM: A foundation model for brain activity recordings J Ortega Caro, AH Oliveira Fonseca, C Averill, SA Rizvi, M Rosati, ... bioRxiv, 2023.09. 12.557460, 2023 | 15 | 2023 |
| Fast neural kernel embeddings for general activations I Han, A Zandieh, J Lee, R Novak, L Xiao, A Karbasi Advances in neural information processing systems 35, 35657-35671, 2022 | 14 | 2022 |
| Scalable learning and MAP inference for nonsymmetric determinantal point processes M Gartrell, I Han, E Dohmatob, J Gillenwater, VE Brunel arXiv preprint arXiv:2006.09862, 2020 | 14 | 2020 |
| Polynomial tensor sketch for element-wise function of low-rank matrix I Han, H Avron, J Shin International Conference on Machine Learning, 3984-3993, 2020 | 12 | 2020 |
| MAP inference for customized determinantal point processes via maximum inner product search I Han, J Gillenwater International Conference on Artificial Intelligence and Statistics, 2797-2807, 2020 | 12 | 2020 |
| Random features for the neural tangent kernel I Han, H Avron, N Shoham, C Kim, J Shin arXiv preprint arXiv:2104.01351, 2021 | 10 | 2021 |
| Cell2sentence: Teaching large language models the language of biology D Levine, S Lévy, SA Rizvi, N Pallikkavaliyaveetil, X Chen, D Zhang, ... bioRxiv, 2023.09. 11.557287, 2023 | 6 | 2023 |
| Scalable sampling for nonsymmetric determinantal point processes I Han, M Gartrell, J Gillenwater, E Dohmatob, A Karbasi arXiv preprint arXiv:2201.08417, 2022 | 4 | 2022 |
| Scalable mcmc sampling for nonsymmetric determinantal point processes I Han, M Gartrell, E Dohmatob, A Karbasi International Conference on Machine Learning, 8213-8229, 2022 | 3 | 2022 |
| Random gegenbauer features for scalable kernel methods I Han, A Zandieh, H Avron International Conference on Machine Learning, 8330-8358, 2022 | 3 | 2022 |
| QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead A Zandieh, M Daliri, I Han arXiv preprint arXiv:2406.03482, 2024 | 2 | 2024 |
| SubGen: Token Generation in Sublinear Time and Memory A Zandieh, I Han, V Mirrokni, A Karbasi arXiv preprint arXiv:2402.06082, 2024 | 2 | 2024 |
| Stochastic gradient descent SL Team | 2 | 2016 |
Jinwoo ShinKAIST Endowed Chair ProfessorVerified email at kaist.ac.kr
