Kashif Rasul - RankMe

40 papers A* 7A 2Journal 29Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Margin-Aware Preference Optimization for Aligning Diffusion Models Without Reference. AAAI Jiwoo Hong, Sayak Paul, Noah Lee, Kashif Rasul, James Thorne, Jongheon Jeong
2026	A*	`conf`	Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations. AAAI Larkin Liu, Kashif Rasul, Yutong Chao, Jalal Etesami
2025	J	`jnl`	Accelerating Nash Learning from Human Feedback via Mirror Prox. CoRR Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard
2025	J	`jnl`	Chart-RVR: Reinforcement Learning with Verifiable Rewards for Explainable Chart Reasoning. CoRR Sanchit Sinha, Oana Frunza, Kashif Rasul, Yuriy Nevmyvaka, Aidong Zhang
2025	J	`jnl`	Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization. CoRR Kevin Rojas, Jiahe Lin, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Molei Tao, Wei Deng
2025	J	`jnl`	Random Initialization Can't Catch Up: The Advantage of Language Model Transfer for Time Series Forecasting. CoRR Roland Riachi, Kashif Rasul, Arjun Ashok, Prateek Humane, Alexis Roger, Andrew Robert Williams, Yuriy Nevmyvaka, Irina Rish
2025	J	`jnl`	Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations. CoRR Larkin Liu, Kashif Rasul, Yutong Chao, Jalal Etesami
2025	J	`jnl`	Small Vocabularies, Big Gains: Pretraining and Tokenization in Time Series Models. CoRR Alexis Roger, Gwen Legate, Kashif Rasul, Yuriy Nevmyvaka, Irina Rish
2025	J	`jnl`	TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster. CoRR Kanghui Ning, Zijie Pan, Yu Liu, Yushan Jiang, James Y. Zhang, Kashif Rasul, Anderson Schneider, Lintao Ma, Yuriy Nevmyvaka, Dongjin Song
2025	J	`jnl`	Triple Preference Optimization: Achieving Better Alignment using a Single Step Optimization. Trans. Mach. Learn. Res. Amir Saeidi, Shivanshu Verma, Kashif Rasul, Aswin RRV, Chitta Baral
2025	J	`jnl`	Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel. CoRR Chuanyang Zheng, Jiankai Sun, Yihang Gao, Enze Xie, Yuehao Wang, Peihao Wang, Ting Xu, Matthew Chang, Liliang Ren, Jingyao Li, Jing Xiong, Kashif Rasul, Mac Schwager, Anderson Schneider, Zhangyang Wang, Yuriy Nevmyvaka
2024	J	`jnl`	Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI. CoRR Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo A. Cecchi, Irina Rish
2024	J	`jnl`	Forecasting with Hyper-Trees. CoRR Alexander März, Kashif Rasul
2024	J	`jnl`	Margin-aware Preference Optimization for Aligning Diffusion Models without Reference. CoRR Jiwoo Hong, Sayak Paul, Noah Lee, Kashif Rasul, James Thorne, Jongheon Jeong
2024	J	`jnl`	Recurrent Interpolants for Probabilistic Time Series Prediction. CoRR Yu Chen, Marin Bilos, Sarthak Mittal, Wei Deng, Kashif Rasul, Anderson Schneider
2024	J	`jnl`	Structural Knowledge Informed Continual Multivariate Time Series Forecasting. CoRR Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka
2024	J	`jnl`	The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization. CoRR Shengyi Huang, Michael Noukhovitch, Arian Hosseini, Kashif Rasul, Weixun Wang, Lewis Tunstall
2024	A*	`conf`	VQ-TR: Vector Quantized Attention for Time Series Forecasting. ICLR Kashif Rasul, Andrew Bennett, Pablo Vicente, Umang Gupta, Hena Ghonia, Anderson Schneider, Yuriy Nevmyvaka
2023	J	`jnl`	Deep Learning based Forecasting: a case study from the online fashion industry. CoRR 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
2023	J	`jnl`	Lag-Llama: Towards Foundation Models for Time Series Forecasting. CoRR 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
2023	A*	`conf`	Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
2023	A*	`conf`	Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. ICML Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka
2023	J	`jnl`	Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation. CoRR Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka
2023	A	`conf`	Risk Bounds on Aleatoric Uncertainty Recovery. AISTATS Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka
2023	J	`jnl`	Zephyr: Direct Distillation of LM Alignment. CoRR 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
2022	J	`jnl`	Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
2022	J	`jnl`	Modeling Temporal Data as Continuous Functions with Process Diffusion. CoRR Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
2022	J	`jnl`	VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting. CoRR Kashif Rasul, Young-Jin Park, Max Nihlén Ramström, Kyung-Min Kim
2021	—	`conf`	AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) 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 Sanjay Sukthanker, Frank Hutter, Euxhen Hasanaj, Tien-Dung Le, Mikhail Khodak, Yuriy Nevmyvaka, Kashif Rasul, Frederic Sala, Anderson Schneider, Junhong Shen, Evan Randall Sparks
2021	A*	`conf`	Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. ICML Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf
2021	J	`jnl`	Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting. CoRR Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf
2021	A*	`conf`	Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. ICLR Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs M. Bergmann, Roland Vollgraf
2021	J	`jnl`	Probabilistic Time Series Forecasting with Implicit Quantile Networks. CoRR Adèle Gouttes, Kashif Rasul, Mateusz Koren, Johannes Stephan, Tofigh Naghibi
2020	J	`jnl`	Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows. CoRR Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, Roland Vollgraf
2019	J	`jnl`	A Bandit Framework for Optimal Selection of Reinforcement Learning Agents. CoRR Andreas Merentitis, Kashif Rasul, Roland Vollgraf, Abdul-Saboor Sheikh, Urs Bergmann
2019	—	`conf`	FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP. NAACL-HLT (Demonstrations) Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, Roland Vollgraf
2019	J	`jnl`	Set Flow: A Permutation Invariant Normalizing Flow. CoRR Kashif Rasul, Ingmar Schuster, Roland Vollgraf, Urs Bergmann
2017	J	`jnl`	Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms. CoRR Han Xiao, Kashif Rasul, Roland Vollgraf
2017	J	`jnl`	Stochastic Maximum Likelihood Optimization via Hypernetworks. CoRR Abdul-Saboor Sheikh, Kashif Rasul, Andreas Merentitis, Urs Bergmann
2002	A	`conf`	The GridLab Grid Application Toolkit. HPDC Gabrielle Allen, Kelly Davis, Thomas Dramlitsch, Tom Goodale, Ian Kelley, Gerd Lanfermann, Jason Novotny, Thomas Radke, Kashif Rasul, Michael Russell, Edward Seidel, Oliver Wehrens