Lag-llama: Towards foundation models for time series forecasting K Rasul, A Ashok, AR Williams, A Khorasani, G Adamopoulos, ... arXiv preprint arXiv:2310.08278, 2023 | 18 | 2023 |
Modeling temporal data as continuous functions with process diffusion M Biloš, K Rasul, A Schneider, Y Nevmyvaka, S Günnemann | 14 | 2022 |
Modeling temporal data as continuous functions with stochastic process diffusion M Biloš, K Rasul, A Schneider, Y Nevmyvaka, S Günnemann International Conference on Machine Learning, 2452-2470, 2023 | 12 | 2023 |
Provably convergent Schrödinger bridge with applications to probabilistic time series imputation Y Chen, W Deng, S Fang, F Li, NT Yang, Y Zhang, K Rasul, S Zhe, ... International Conference on Machine Learning, 4485-4513, 2023 | 12 | 2023 |
Heterogeneous labor skills, the median voter and labor taxes F Piguillem, AL Schneider Review of Economic Dynamics 16 (2), 332-349, 2013 | 6 | 2013 |
Coordination, Efficiency and Policy Discretion F Piguillem, A Schneider EIEF Working Papers Series, 2013 | 4 | 2013 |
A note on redistribution, optimal fiscal policy and corner solutions F Piguillem, AL Schneider Mimeo, at https://sites. google. com/site/welcometofacundoswebsite/Home/research, 2007 | 4 | 2007 |
Empowering Time Series Analysis with Large Language Models: A Survey Y Jiang, Z Pan, X Zhang, S Garg, A Schneider, Y Nevmyvaka, D Song arXiv preprint arXiv:2402.03182, 2024 | 3 | 2024 |
In-or out-of-distribution detection via dual divergence estimation S Garg, S Dutta, M Dalirrooyfard, A Schneider, Y Nevmyvaka Uncertainty in Artificial Intelligence, 635-646, 2023 | 3 | 2023 |
Risk Bounds on Aleatoric Uncertainty Recovery Y Zhang, J Lin, F Li, Y Adler, K Rasul, A Schneider, Y Nevmyvaka International Conference on Artificial Intelligence and Statistics, 6015-6036, 2023 | 3 | 2023 |
Inference and sampling of point processes from diffusion excursions A Hasan, Y Chen, Y Ng, M Abdelghani, A Schneider, V Tarokh Uncertainty in Artificial Intelligence, 839-848, 2023 | 2 | 2023 |
Information theoretic clustering via divergence maximization among clusters S Garg, M Dalirrooyfard, A Schneider, Y Adler, Y Nevmyvaka, Y Chen, ... Uncertainty in Artificial Intelligence, 624-634, 2023 | 1 | 2023 |
Automl decathlon: Diverse tasks, modern methods, and efficiency at scale N Roberts, S Guo, C Xu, A Talwalkar, D Lander, L Tao, L Cai, S Niu, ... NeurIPS 2022 Competition Track, 151-170, 2022 | 1 | 2022 |
Heterogeneous Labor Skills, The Median Voter and Labor Taxes F Piguillem, AL Schneider EIEF Working Papers Series, 2009 | 1 | 2009 |
Heterogeneous Beliefs and Optimal Taxation F Piguillem, AL Schneider mimeo, 2008 | 1 | 2008 |
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI S Garg, A Schneider, A Raj, K Rasul, Y Nevmyvaka, S Gopal, ... arXiv preprint arXiv:2404.07377, 2024 | | 2024 |
IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting Z Pan, Y Jiang, S Garg, A Schneider, Y Nevmyvaka, D Song arXiv preprint arXiv:2403.05798, 2024 | | 2024 |
Structural Knowledge Informed Continual Multivariate Time Series Forecasting Z Pan, Y Jiang, D Song, S Garg, K Rasul, A Schneider, Y Nevmyvaka arXiv preprint arXiv:2402.12722, 2024 | | 2024 |
VQ-TR: Vector Quantized Attention for Time Series Forecasting K Rasul, A Bennett, P Vicente, U Gupta, H Ghonia, A Schneider, ... The Twelfth International Conference on Learning Representations, 2023 | | 2023 |
Learning to Abstain From Uninformative Data Y Zhang, S Zheng, M Dalirrooyfard, P Wu, A Schneider, A Raj, ... arXiv preprint arXiv:2309.14240, 2023 | | 2023 |