15 papers
Surprises in Proper Positive-Only Learning
Shai Ben-David, Farnam Mansouri, Anay Mehrotra +1
Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.i.d. samples from the positive region of an unknown target concept, bu…
Restoring Incentive Compatibility in Two-Stage Energy Markets with Prosumers
Nikolas Koumpis, Koushik Kar, Leandros Tassiulas +1
A central challenge in modern energy market design is the formulation of a strategy-proof imbalance settlement layer that secures both the economic efficiency of the institution an…
Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion
Anay Mehrotra, Phuc Tran, Van H. Vu +1
A central goal of modern causal inference is estimating heterogeneous treatment effects to answer questions like "how does an intervention affect each unit," rather than only on av…
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…
What is Learnable in Valiant's Theory of the Learnable?
Steve Hanneke, Anay Mehrotra, Grigoris Velegkas +1
Valiant's 1984 paper is widely credited with introducing the PAC learning model, but it, in fact, introduced a different model: unlike PAC learning, the learner receives only posit…
Smoothed Analysis of Learning from Positive Samples
Jane H. Lee, Anay Mehrotra, Manolis Zampetakis
Binary classification from positive-only samples is a variant of PAC learning where the learner receives i.i.d. positive samples and aims to learn a classifier with low error. Prev…