1 citations · 4 across the 15 of their papers we have counts for
16 papers
On Parameters of Nonlinear Scalar Dynamics from Video: Invariants, Calibration, and Identifiability
Wenjie Wang, Yuanyuan Wang, Zixiang Jiang +2
Physical parameter estimation from video aims to recover the parameters of a known family of governing dynamical equations from pixel observations. Existing identifiability theory…
Physics from Video: Identifiability of Time-Invariant Second-Order ODEs under Minimal Trajectory Conditions
Yuanyuan Wang, Wenjie Wang, Kun Zhang +1
Bridging the gap between visual realism and physical understanding is a core challenge for video-based world models. We study the structural identifiability of continuous-time phys…
TimeLAVA: Learning-Agnostic Valuation for Time Series Data
Wenqin Liu, Weizhi Quan, Aoqi Zuo +5
Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains…
Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts
Yuanyuan Wang, Wenjie Wang, Haoxuan Li +2
Causal representation learning for time series has developed strong identifiability results in discrete-time latent causal models, but identifiability in continuous-time latent sto…
Analytic DAG Constraints for Differentiable DAG Learning
Zhen Zhang, Ignavier Ng, Dong Gong +6
Recovering the underlying Directed Acyclic Graph (DAG) structures from observational data presents a formidable challenge, partly due to the combinatorial nature of the DAG-constra…
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
Yuhang Liu, Dong Gong, Yichao Cai +6
Recent empirical evidence shows that LLM representations encode human-interpretable concepts. Nevertheless, the mechanisms by which these representations emerge remain largely unex…
Identifiability Analysis of Linear ODE Systems with Hidden Confounders
Yuanyuan Wang, Biwei Huang, Wei Huang +2
The identifiability analysis of linear Ordinary Differential Equation (ODE) systems is a necessary prerequisite for making reliable causal inferences about these systems. While ide…
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
Yingyu Lin, Yuxing Huang, Wenqin Liu +6
Real-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery. In this work, we expl…
Rethinking State Disentanglement in Causal Reinforcement Learning
Haiyao Cao, Zhen Zhang, Panpan Cai +7
One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical supp…
Optimal Kernel Choice for Score Function-based Causal Discovery
Wenjie Wang, Biwei Huang, Feng Liu +4
Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Rec…
On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
Shunxing Fan, Mingming Gong, Kun Zhang
We consider the effect of temporal aggregation on instantaneous (non-temporal) causal discovery in general setting. This is motivated by the observation that the true causal time l…
On the Identification of Temporally Causal Representation with Instantaneous Dependence
Zijian Li, Yifan Shen, Kaitao Zheng +5
Temporally causal representation learning aims to identify the latent causal process from time series observations, but most methods require the assumption that the latent causal p…
Towards Identifiable Latent Additive Noise Models
Yuhang Liu, Zhen Zhang, Dong Gong +6
Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing…
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
Yuhang Liu, Zhen Zhang, Dong Gong +6
Directed Acyclic Graphs (DAGs) are a standard tool in causal modeling, but their suitability for capturing the complexity of large-scale multimodal data is questionable. In practic…
Generator Identification for Linear SDEs with Additive and Multiplicative Noise
Yuanyuan Wang, Xi Geng, Wei Huang +2
In this paper, we present conditions for identifying the generator of a linear stochastic differential equation (SDE) from the distribution of its solution process with a given fix…
Identifiable Latent Polynomial Causal Models Through the Lens of Change
Yuhang Liu, Zhen Zhang, Dong Gong +5
Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of id…