1 citations · 1 across the 18 of their papers we have counts for
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Causal Inference for Sequential Settings under Interference and Latent Confounding
Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah
We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian…
High-accuracy sampling for diffusion models and log-concave distributions
Fan Chen, Sinho Chewi, Constantinos Daskalakis +1
We present algorithms for diffusion model sampling which obtain -error in steps, given access to -accurate score estimates in . Thi…
Ambient Dataloops: Generative Models for Dataset Refinement
Adrián Rodríguez-Muñoz, William Daspit, Adam Klivans +3
We propose Ambient Dataloops, an iterative framework for refining datasets that makes it easier for diffusion models to learn the underlying data distribution. Modern datasets cont…
Estimating Ising Models in Total Variation Distance
Constantinos Daskalakis, Vardis Kandiros, Rui Yao
We consider the problem of estimating Ising models over variables in Total Variation (TV) distance, given independent samples from the model. While the statistical complexi…
Learning Gaussian DAG Models without Condition Number Bounds
Constantinos Daskalakis, Vardis Kandiros, Rui Yao
We study the problem of learning the topology of a directed Gaussian Graphical Model under the equal-variance assumption, where the graph has nodes and maximum in-degree . P…
Learning Correlated Reward Models: Statistical Barriers and Opportunities
Yeshwanth Cherapanamjeri, Constantinos Daskalakis, Gabriele Farina +1
Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learning from Human Feedback (RLHF). H…