4 citations · 5 across the 9 of their papers we have counts for
9 papers
Learning Orthonormal Bases for Function Spaces
Hamidreza Kamkari, Mohammad Sina Nabizadeh, Justin Solomon
Infinite-dimensional orthonormal basis expansions play a central role in representing and computing with function spaces due to their favorable linear algebraic properties. However…
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
Vahid Balazadeh, Hamidreza Kamkari, Medha Barath +2
The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification impossible. Existing approac…
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas +4
Causal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands…
A Geometric Framework for Understanding Memorization in Generative Models
Brendan Leigh Ross, Hamidreza Kamkari, Tongzi Wu +5
As deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into questi…
TabDPT: Scaling Tabular Foundation Models on Real Data
Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh +7
Tabular data is one of the most ubiquitous sources of information worldwide, spanning a wide variety of domains. This inherent heterogeneity has slowed the development of Tabular F…
A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models
Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh +2
High-dimensional data commonly lies on low-dimensional submanifolds, and estimating the local intrinsic dimension (LID) of a datum -- i.e. the dimension of the submanifold it belon…
A Geometric Explanation of the Likelihood OOD Detection Paradox
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell +3
Likelihood-based deep generative models (DGMs) commonly exhibit a puzzling behaviour: when trained on a relatively complex dataset, they assign higher likelihood values to out-of-d…
Order-based Structure Learning with Normalizing Flows
Hamidreza Kamkari, Vahid Balazadeh, Vahid Zehtab +1
Estimating the causal structure of observational data is a challenging combinatorial search problem that scales super-exponentially with graph size. Existing methods use continuous…
Physarum Inspired Dynamics to Solve Semi-Definite Programs
Yuan Gao, Hamidreza Kamkari, Andreas Karrenbauer +2
Physarum Polycephalum is a slime mold that can solve shortest path problems. A mathematical model based on Physarum's behavior, known as the Physarum Directed Dynamics, can solve p…