538 citations · 2.5k across the 90 of their papers we have counts for
93 papers
Stochastic global optimization of continuous functions via random walks on Grassmannians
Kartik Gupta, Stephen D. Miller, Pradeep Ravikumar +1
We introduce a stochastic global optimization method based on random walks on Grassmannian manifolds. To minimize a continuous objective , t…
Representer Point Selection for Explaining Regularized High-dimensional Models
Che-Ping Tsai, Jiong Zhang, Eli Chien +3
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model i…
Global Optimality in Bivariate Gradient-based DAG Learning
Chang Deng, Kevin Bello, Bryon Aragam +1
Recently, a new class of non-convex optimization problems motivated by the statistical problem of learning an acyclic directed graphical model from data has attracted significant i…
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Tianyu Chen, Kevin Bello, Bryon Aragam +1
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal st…
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Simon Buchholz, Goutham Rajendran, Elan Rosenfeld +3
We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function i…
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
Runtian Zhai, Bingbin Liu, Andrej Risteski +2
Data augmentation is critical to the empirical success of modern self-supervised representation learning, such as contrastive learning and masked language modeling. However, a theo…