5 citations · 7 across the 13 of their papers we have counts for
14 papers
Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation
Emre Acartürk, Pranamya Kulkarni, Puranjay Datta +3
Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free inference method, CRL is par…
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)…
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…
Interventional Causal Discovery in a Mixture of DAGs
Burak Varıcı, Dmitriy Katz-Rogozhnikov, Dennis Wei +2
Causal interactions among a group of variables are often modeled by a single causal graph. In some domains, however, these interactions are best described by multiple co-existing c…
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…
Improved Bound for Robust Causal Bandits with Linear Models
Zirui Yan, Arpan Mukherjee, Burak Varıcı +1
This paper investigates the robustness of causal bandits (CBs) in the face of temporal model fluctuations. This setting deviates from the existing literature's widely-adopted assum…
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…
Robust Causal Bandits for Linear Models
Zirui Yan, Arpan Mukherjee, Burak Varıcı +1
Sequential design of experiments for optimizing a reward function in causal systems can be effectively modeled by the sequential design of interventions in causal bandits (CBs). In…
General Identifiability and Achievability for Causal Representation Learning
Burak Varıcı, Emre Acartürk, Karthikeyan Shanmugam +1
This paper focuses on causal representation learning (CRL) under a general nonparametric latent causal model and a general transformation model that maps the latent data to the obs…
Score-based Causal Representation Learning with Interventions
Burak Varici, Emre Acarturk, Karthikeyan Shanmugam +2
This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation. The objectives are:…
Causal Bandits for Linear Structural Equation Models
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri +1
This paper studies the problem of designing an optimal sequence of interventions in a causal graphical model to minimize cumulative regret with respect to the best intervention in…
Scalable Intervention Target Estimation in Linear Models
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri +1
This paper considers the problem of estimating the unknown intervention targets in a causal directed acyclic graph from observational and interventional data. The focus is on soft…