7 papers
Tabular Numeric Stretch Transformation
Zihao Ye, Juyong Kim, Johnna Sundberg +2
Tabular data presents unique challenges for deep learning due to its heterogeneous nature, where numeric features exhibit diverse distributions, scales, and statistical properties.…
Eigenfunction Extraction for Ordered Representation Learning
Burak Varıcı, Che-Ping Tsai, Ritabrata Ray +2
Recent advances in representation learning reveal that widely used objectives, such as contrastive and non-contrastive, implicitly perform spectral decomposition of a contextual ke…
ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning
Pranamya Kulkarni, Puranjay Datta, Burak Varıcı +3
Causal representation learning (CRL) has emerged as a powerful unsupervised framework that (i) disentangles the latent generative factors underlying high-dimensional data, and (ii)…
Score-based Causal Representation Learning: Linear and General Transformations
Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam +2
This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent va…
Contextures: Representations from Contexts
Runtian Zhai, Kai Yang, Che-Ping Tsai +3
Despite the empirical success of foundation models, we do not have a systematic characterization of the representations that these models learn. In this paper, we establish the con…
Linear Causal Representation Learning from Unknown Multi-node Interventions
Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam +1
Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This…