2 citations · 3 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
Danial Hosseintabar, Fan Chen, Giannis Daras +2
Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains…
Ambient Diffusion Omni: Training Good Models with Bad Data
Giannis Daras, Adrian Rodriguez-Munoz, Adam Klivans +2
We show how to use low-quality, synthetic, and out-of-distribution images to improve the quality of a diffusion model. Typically, diffusion models are trained on curated datasets t…
Learning High-dimensional Gaussians from Censored Data
Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis +1
We provide efficient algorithms for the problem of distribution learning from high-dimensional Gaussian data where in each sample, some of the variable values are missing. We suppo…
Fixed Point Computation: Beating Brute Force with Smoothed Analysis
Idan Attias, Yuval Dagan, Constantinos Daskalakis +2
We propose a new algorithm that finds an -approximate fixed point of a smooth function from the -dimensional unit ball to itself. We use the general framew…