Kashif Rasul

According to our database, Kashif Rasul authored at least 27 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Recurrent Interpolants for Probabilistic Time Series Prediction.

Yu Chen, Marin Bilos, Sarthak Mittal, Wei Deng, Kashif Rasul, Anderson Schneider

CoRR, 2024

Margin-aware Preference Optimization for Aligning Diffusion Models without Reference.

Jiwoo Hong, Sayak Paul, Noah Lee, Kashif Rasul, James Thorne, Jongheon Jeong

CoRR, 2024

Forecasting with Hyper-Trees.

Alexander März, Kashif Rasul

CoRR, 2024

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI.

Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo A. Cecchi, Irina Rish

CoRR, 2024

The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization.

Shengyi Huang, Michael Noukhovitch, Arian Hosseini, Kashif Rasul, Weixun Wang, Lewis Tunstall

CoRR, 2024

Structural Knowledge Informed Continual Multivariate Time Series Forecasting.

Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka

CoRR, 2024

VQ-TR: Vector Quantized Attention for Time Series Forecasting.

Kashif Rasul, Andrew Bennett, Pablo Vicente, Umang Gupta, Hena Ghonia, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Zephyr: Direct Distillation of LM Alignment.

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

CoRR, 2023

Lag-Llama: Towards Foundation Models for Time Series Forecasting.

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

CoRR, 2023

Deep Learning based Forecasting: a case study from the online fashion industry.

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

CoRR, 2023

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation.

Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the International Conference on Machine Learning, 2023

Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion.

Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

Proceedings of the International Conference on Machine Learning, 2023

Risk Bounds on Aleatoric Uncertainty Recovery.

Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Modeling Temporal Data as Continuous Functions with Process Diffusion.

Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann

CoRR, 2022

Intrinsic Anomaly Detection for Multi-Variate Time Series.

Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert

CoRR, 2022

VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting.

Kashif Rasul, Young-Jin Park, Max Nihlén Ramström, Kyung-Min Kim

CoRR, 2022

2021

Probabilistic Time Series Forecasting with Implicit Quantile Networks.

Adèle Gouttes, Kashif Rasul, Mateusz Koren, Johannes Stephan, Tofigh Naghibi

CoRR, 2021

AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale.

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

Proceedings of the NeurIPS 2022 Competition Track, 2021

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting.

Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf

Proceedings of the 38th International Conference on Machine Learning, 2021

Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows.

Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs M. Bergmann, Roland Vollgraf

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows.

Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf

CoRR, 2020

2019

Set Flow: A Permutation Invariant Normalizing Flow.

Kashif Rasul, Ingmar Schuster, Roland Vollgraf, Urs Bergmann

CoRR, 2019

A Bandit Framework for Optimal Selection of Reinforcement Learning Agents.

Andreas Merentitis, Kashif Rasul, Roland Vollgraf, Abdul-Saboor Sheikh, Urs Bergmann

CoRR, 2019

FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP.

Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, Roland Vollgraf

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

2017

Stochastic Maximum Likelihood Optimization via Hypernetworks.

Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis, Urs Bergmann

CoRR, 2017

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Han Xiao, Kashif Rasul, Roland Vollgraf

CoRR, 2017

2002

The GridLab Grid Application Toolkit.

Gabrielle Allen, Kelly Davis, Thomas Dramlitsch, Tom Goodale, Ian Kelley, Gerd Lanfermann, Jason Novotny, Thomas Radke, Kashif Rasul, Michael Russell, Edward Seidel, Oliver Wehrens

Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing (HPDC-11 2002), 2002