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ORCIDKashif Rasul
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2020 – today
- 2024
[c9]Kashif Rasul, Andrew Bennett, Pablo Vicente, Umang Gupta, Hena Ghonia, Anderson Schneider, Yuriy Nevmyvaka:
VQ-TR: Vector Quantized Attention for Time Series Forecasting. ICLR 2024- [i20]Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka:
Structural Knowledge Informed Continual Multivariate Time Series Forecasting. CoRR abs/2402.12722 (2024) - [i19]Shengyi Huang, Michael Noukhovitch, Arian Hosseini, Kashif Rasul, Weixun Wang, Lewis Tunstall:
The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization. CoRR abs/2403.17031 (2024) - [i18]Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo A. Cecchi, Irina Rish:
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. CoRR abs/2404.07377 (2024) - [i17]Alexander März, Kashif Rasul:
Forecasting with Hyper-Trees. CoRR abs/2405.07836 (2024) - [i16]Jiwoo Hong, Sayak Paul, Noah Lee, Kashif Rasul, James Thorne, Jongheon Jeong:
Margin-aware Preference Optimization for Aligning Diffusion Models without Reference. CoRR abs/2406.06424 (2024) - [i15]Yu Chen, Marin Bilos, Sarthak Mittal, Wei Deng, Kashif Rasul, Anderson Schneider:
Recurrent Interpolants for Probabilistic Time Series Prediction. CoRR abs/2409.11684 (2024) - 2023
- [c8]Yikai Zhang, Jiahe Lin
, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka:
Risk Bounds on Aleatoric Uncertainty Recovery. AISTATS 2023: 6015-6036 - [c7]Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML 2023: 2452-2470 - [c6]Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka:
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. ICML 2023: 4485-4513 - [i14]Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka:
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. CoRR abs/2305.07247 (2023) - [i13]Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adèle Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Kekic, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski:
Deep Learning based Forecasting: a case study from the online fashion industry. CoRR abs/2305.14406 (2023) - [i12]Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilos, Hena Ghonia, Nadhir Vincent Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish:
Lag-Llama: Towards Foundation Models for Time Series Forecasting. CoRR abs/2310.08278 (2023) - [i11]Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, Thomas Wolf:
Zephyr: Direct Distillation of LM Alignment. CoRR abs/2310.16944 (2023) - 2022
- [i10]Kashif Rasul, Young-Jin Park, Max Nihlén Ramström, Kyung-Min Kim:
VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting. CoRR abs/2205.15894 (2022) - [i9]Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert:
Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR abs/2206.14342 (2022) - [i8]Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann:
Modeling Temporal Data as Continuous Functions with Process Diffusion. CoRR abs/2211.02590 (2022) - 2021
- [c5]Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs M. Bergmann, Roland Vollgraf:
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. ICLR 2021 - [c4]Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf:
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. ICML 2021: 8857-8868 - [c3]Nicholas Roberts, Samuel Guo, Cong Xu, Ameet Talwalkar, David Lander, Lvfang Tao, Linhang Cai, Shuaicheng Niu, Jianyu Heng, Hongyang Qin, Minwen Deng, Johannes Hog, Alexander Pfefferle, Sushil Ammanaghatta Shivakumar, Arjun Krishnakumar, Yubo Wang, Rhea Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks:
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) 2021: 151-170 - [i7]Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf:
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. CoRR abs/2101.12072 (2021) - [i6]Adèle Gouttes, Kashif Rasul, Mateusz Koren, Johannes Stephan, Tofigh Naghibi:
Probabilistic Time Series Forecasting with Implicit Quantile Networks. CoRR abs/2107.03743 (2021) - 2020
- [i5]Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf:
Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. CoRR abs/2002.06103 (2020)
2010 – 2019
- 2019
- [c2]Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, Roland Vollgraf:
FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. NAACL-HLT (Demonstrations) 2019: 54-59 - [i4]Andreas Merentitis, Kashif Rasul, Roland Vollgraf, Abdul-Saboor Sheikh, Urs Bergmann:
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents. CoRR abs/1902.03657 (2019) - [i3]Kashif Rasul, Ingmar Schuster, Roland Vollgraf, Urs Bergmann:
Set Flow: A Permutation Invariant Normalizing Flow. CoRR abs/1909.02775 (2019) - 2017
- [i2]Han Xiao, Kashif Rasul, Roland Vollgraf:
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. CoRR abs/1708.07747 (2017) - [i1]Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis, Urs Bergmann:
Stochastic Maximum Likelihood Optimization via Hypernetworks. CoRR abs/1712.01141 (2017)
2000 – 2009
- 2002
- [c1]Gabrielle Allen, Kelly Davis, Thomas Dramlitsch, Tom Goodale, Ian Kelley, Gerd Lanfermann, Jason Novotny, Thomas Radke, Kashif Rasul, Michael Russell, Edward Seidel, Oliver Wehrens:
The GridLab Grid Application Toolkit. HPDC 2002: 411
Coauthor Index
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last updated on 2024-11-04 20:44 CET by the dblp team
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