Search Results for author: Yuriy Nevmyvaka

Found 12 papers, 4 papers with code

Variational Schrödinger Diffusion Models

no code implementations8 May 2024 Wei Deng, Weijian Luo, Yixin Tan, Marin Biloš, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen

To improve the scalability while preserving efficient transportation plans, we leverage variational inference to linearize the forward score functions (variational scores) of SB and restore simulation-free properties in training backward scores.

Variational Inference

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

no code implementations10 Apr 2024 Sahil Garg, Anderson Schneider, Anant Raj, Kashif Rasul, Yuriy Nevmyvaka, Sneihil Gopal, Amit Dhurandhar, Guillermo Cecchi, Irina Rish

In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution.

Denoising

$\textbf{S}^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting

1 code implementation9 Mar 2024 Zijie Pan, Yushan Jiang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song

To this end, we propose Semantic Space Informed Prompt learning with LLM ($S^2$IP-LLM) to align the pre-trained semantic space with time series embeddings space and perform time series forecasting based on learned prompts from the joint space.

Time Series Time Series Forecasting

Structural Knowledge Informed Continual Multivariate Time Series Forecasting

no code implementations20 Feb 2024 Zijie Pan, Yushan Jiang, Dongjin Song, Sahil Garg, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka

To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay.

Continual Learning Graph structure learning +2

Empowering Time Series Analysis with Large Language Models: A Survey

no code implementations5 Feb 2024 Yushan Jiang, Zijie Pan, Xikun Zhang, Sahil Garg, Anderson Schneider, Yuriy Nevmyvaka, Dongjin Song

Specifically, we first state the challenges and motivations of applying language models in the context of time series as well as brief preliminaries of LLMs.

Survey Time Series +1

Learning to Abstain From Uninformative Data

no code implementations25 Sep 2023 Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen

Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high.

Decision Making Learning Theory

Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes

1 code implementation28 May 2023 Yu Chen, Fengpei Li, Anderson Schneider, Yuriy Nevmyvaka, Asohan Amarasingham, Henry Lam

Then we proposed a robust and computationally-efficient method modified from MLE that does not rely on the prior estimation of the heterogeneous intensity and is thus applicable in a data-limited regime (e. g., few-shot, no repeated observations).

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation

1 code implementation12 May 2023 Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka

We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.

Imputation Missing Values +1

Do price trajectory data increase the efficiency of market impact estimation?

no code implementations26 May 2022 Fengpei Li, Vitalii Ihnatiuk, Ryan Kinnear, Anderson Schneider, Yuriy Nevmyvaka

Market impact is an important problem faced by large institutional investor and active market participant.

Learning to Abstain in the Presence of Uninformative Data

no code implementations29 Sep 2021 Yikai Zhang, Songzhu Zheng, Pengxiang Wu, Yuriy Nevmyvaka, Chao Chen

Learning and decision making in domains with naturally high noise-to-signal ratios – such as Finance or Public Health – can be challenging and yet extremely important.

Decision Making Learning Theory

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