Rose Yu

PG-BIG: Personalized Guidance for Biomechanically Informed Generative Models in Exercise Science L4DC 2026 Adapting While Learning: Grounding LLMs for Scientific Problems with Tool Usage Adaptation ICML 2025 AtlasD: Automatic Local Symmetry Discovery ICML 2025 BIGE : Biomechanics-informed GenAI for Exercise Science L4DC 2025 Can LLMs Understand Time Series Anomalies? ICLR 2025 ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models ICLR 2025 ClimSim-Online: A Large Multi-Scale Dataset and Framework for Hybrid Physics-ML Climate Emulation JMLR 2025 Discovering Latent Causal Graphs from Spatiotemporal Data ICML 2025 MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning ICML 2025 Understanding Mode Connectivity via Parameter Space Symmetry ICML 2025 Copula Conformal prediction for multi-step time series prediction ICLR 2024 Data-driven simulator for mechanical circulatory support with domain adversarial neural process L4DC 2024 Discovering Mixtures of Structural Causal Models from Time Series Data ICML 2024 Improving Convergence and Generalization Using Parameter Symmetries ICLR 2024 Latent Space Symmetry Discovery ICML 2024 Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes AISTATS 2024 MORL-Prompt: An Empirical Analysis of Multi-Objective Reinforcement Learning for Discrete Prompt Optimization EMNLP 2024 Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling ICML 2024 On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers AISTATS 2024 Probablistic Emulation of a Global Climate Model with Spherical DYffusion NeurIPS 2024 Symmetry-Informed Governing Equation Discovery NeurIPS 2024 Understanding the difficulty of solving Cauchy problems with PINNs L4DC 2024 Automatic Integration for Fast and Interpretable Neural Point Processes L4DC 2023 Automatic Integration for Spatiotemporal Neural Point Processes NeurIPS 2023 ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation NeurIPS 2023 Disentangled Multi-Fidelity Deep Bayesian Active Learning ICML 2023 DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting NeurIPS 2023 Generative Adversarial Symmetry Discovery ICML 2023 Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts ICLR 2023 On the Connection Between MPNN and Graph Transformer ICML 2023 Probabilistic Symmetry for Multi-Agent Dynamics L4DC 2023 Symmetries, Flat Minima, and the Conserved Quantities of Gradient Flow ICLR 2023 Approximately Equivariant Networks for Imperfectly Symmetric Dynamics ICML 2022 LIMO: Latent Inceptionism for Targeted Molecule Generation ICML 2022 Meta-Learning Dynamics Forecasting Using Task Inference NeurIPS 2022 Neural Point Process for Learning Spatiotemporal Event Dynamics L4DC 2022 Symmetry Teleportation for Accelerated Optimization NeurIPS 2022 Automatic Symmetry Discovery with Lie Algebra Convolutional Network NeurIPS 2021 Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems L4DC 2021 Incorporating Symmetry into Deep Dynamics Models for Improved Generalization ICLR 2021 Traffic Forecasting using Vehicle-to-Vehicle Communication L4DC 2021 Trajectory Prediction using Equivariant Continuous Convolution ICLR 2021 Deep Imitation Learning for Bimanual Robotic Manipulation NeurIPS 2020 Learning Disentangled Representations of Videos with Missing Data NeurIPS 2020 Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis ICML 2020 NAOMI: Non-Autoregressive Multiresolution Sequence Imputation NeurIPS 2019 Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology NeurIPS 2019 Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting ICLR 2018 Tensor Regression Meets Gaussian Processes AISTATS 2018 Learning from Multiway Data: Simple and Efficient Tensor Regression ICML 2016 Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams ICML 2015