| Stanford alpaca: An instruction-following llama model R Taori*, I Gulrajani*, T Zhang*, Y Dubois*, X Li*, C Guestrin, P Liang, ... | 2016* | 2023 |
| AlpacaEval: An automatic evaluator of instruction-following models X Li*, T Zhang*, Y Dubois*, R Taori*, I Gulrajani, C Guestrin, P Liang, ... | 277 | 2023 |
| AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback Y Dubois*, X Li*, R Taori*, T Zhang*, I Gulrajani, J Ba, C Guestrin, P Liang, ... NeurIPS, 2023 | 250 | 2023 |
| Convolutional Conditional Neural Processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner ICLR, 2020 | 161 | 2020 |
| Lossy Compression for Lossless Prediction Y Dubois, B Bloem-Reddy, K Ullrich, CJ Maddison NeurIPS, 2021 | 69 | 2021 |
| Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes AYK Foong, WP Bruinsma, J Gordon, Y Dubois, J Requeima, RE Turner NeurIPS, 2020 | 66 | 2020 |
| Optimal Representations for Covariate Shifts Y Ruan*, Y Dubois*, CJ Maddison ICLR, 2021 | 64 | 2021 |
| Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators Y Dubois, B Galambosi, P Liang, TB Hashimoto COLM, 2024 | 48 | 2024 |
| Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning S Santurkar, Y Dubois, R Taori, P Liang, T Hashimoto ICLR, 2022 | 45* | 2022 |
| Learning Optimal Representations with the Decodable Information Bottleneck Y Dubois, D Kiela, DJ Schwab, R Vedantam NeurIPS, 2020 | 40 | 2020 |
| Improving Self-Supervised Learning by Characterizing Idealized Representations Y Dubois, T Hashimoto, S Ermon, P Liang NeurIPS, 2022 | 35 | 2022 |
| Location Attention for Extrapolation to Longer Sequences Y Dubois, G Dagan, D Hupkes, E Bruni ACL, 2019 | 32 | 2019 |
| Identifying the Risks of LM Agents with an LM-Emulated Sandbox Y Ruan, H Dong, A Wang, S Pitis, Y Zhou, J Ba, Y Dubois, CJ Maddison, ... ICLR, 2023 | 28 | 2023 |
| Learning Instance-Specific Augmentations by Capturing Local Invariances N Miao, T Rainforth, E Mathieu, Y Dubois, YW Teh, A Foster, H Kim ICML, 2022 | 11* | 2022 |
| Evaluating Self-Supervised Learning via Risk Decomposition Y Dubois, T Hashimoto, P Liang ICML, 2023 | 6 | 2023 |
| Learning to (Learn at Test Time): RNNs with Expressive Hidden States Y Sun, X Li, K Dalal, J Xu, A Vikram, G Zhang, Y Dubois, X Chen, X Wang, ... arXiv preprint arXiv:2407.04620, 2024 | 1 | 2024 |
| Neural process family Y Dubois, J Gordon, AY Foong yanndubs.github.io/Neural-Process-Family, 2020 | | 2020 |
| Revisiting Associative Compression: I Can’t Believe It’s Not Better W Xu, MJ Muckley, Y Dubois, K Ullrich | | |
| Conditional Neural Processes for Semi-Supervised Learning Y Dubois, RE Turner | | |
| Understanding Disentangling in VAE Y Dubois, A Kastanos, D Lines, B Melman | | |
Tatsunori HashimotoAssistant Professor, StanfordVerified email at stanford.edu
