12 papers
Linear Regression with Unknown Truncation Beyond Gaussian Features
Alexandros Kouridakis, Anay Mehrotra, Alkis Kalavasis +1
In truncated linear regression, samples are shown only when the outcome falls inside a certain survival set and the goal is to estimate the unknown -dimens…
Learning Mixture Models via Efficient High-dimensional Sparse Fourier Transforms
Alkis Kalavasis, Pravesh K. Kothari, Shuchen Li +1
In this work, we give a time and sample algorithm for efficiently learning the parameters of a mixture of spherical distributions in dimensions. Unlike al…
Efficient Parameter Estimation of Truncated Boolean Product Distributions
Dimitris Fotakis, Alkis Kalavasis, Christos Tzamos
We study the problem of estimating the parameters of a Boolean product distribution in dimensions, when the samples are truncated by a set accessible thr…
On the Learning Curves of Revenue Maximization
Steve Hanneke, Alkis Kalavasis, Shay Moran +1
Learning curves are a fundamental primitive in supervised learning, describing how an algorithm's performance improves with more data and providing a quantitative measure of its ge…
Does Generation Require Memorization? Creative Diffusion Models using Ambient Diffusion
Kulin Shah, Alkis Kalavasis, Adam R. Klivans +1
There is strong empirical evidence that the state-of-the-art diffusion modeling paradigm leads to models that memorize the training set, especially when the training set is small.…
Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms
Alkis Kalavasis, Anay Mehrotra, Manolis Zampetakis +2
Coarse data arise when learners observe only partial information about samples; namely, a set containing the sample rather than its exact value. This occurs naturally through measu…