Anderson Schneider

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 20

Top Topics

Vector Quantized

Provably Convergent

Continuous Functions

Top Venues

Publications

Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting. CoRR (2024)
Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song
Empowering Time Series Analysis with Large Language Models: A Survey. CoRR (2024)
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 (2024)
Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen
Learning to Abstain From Uninformative Data. Trans. Mach. Learn. Res. 2024 (2024)
Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka
Structural Knowledge Informed Continual Multivariate Time Series Forecasting. CoRR (2024)
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)
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 (2023)
Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam
Detection of Short-Term Temporal Dependencies in Hawkes Processes with Heterogeneous Background Dynamics. UAI (2023)
Sahil Garg, Mina Dalirrooyfard, Anderson Schneider, Yeshaya Adler, Yuriy Nevmyvaka, Yu Chen, Fengpei Li, Guillermo A. Cecchi
Information theoretic clustering via divergence maximization among clusters. UAI (2023)
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)
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 (2023)
Ali Hasan, Yu Chen, Yuting Ng, Mohamed Abdelghani, Anderson Schneider, Vahid Tarokh
Inference and sampling of point processes from diffusion excursions. UAI (2023)
Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion. ICML (2023)
Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka
In- or out-of-distribution detection via dual divergence estimation. UAI (2023)
Yikai Zhang, Jiahe Lin, Fengpei Li, Yeshaya Adler, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka
Risk Bounds on Aleatoric Uncertainty Recovery. AISTATS (2023)
Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam
Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes. CoRR (2023)
Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen
Learning to Abstain From Uninformative Data. CoRR (2023)
Sahil Garg, Umang Gupta, Yu Chen, Syamantak Datta Gupta, Yeshaya Adler, Anderson Schneider, Yuriy Nevmyvaka
Estimating transfer entropy under long ranged dependencies. UAI (2022)
Marin Bilos, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
Modeling Temporal Data as Continuous Functions with Process Diffusion. CoRR (2022)
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 R. Sparks
AutoML Decathlon: Diverse Tasks, Modern Methods, and Efficiency at Scale. NeurIPS (Competition and Demos) (2021)