6 papers · 1 filter
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…
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…
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…
A Note on Non-Negative -Approximating Polynomials
Jane H. Lee, Anay Mehrotra, Manolis Zampetakis
-Approximating polynomials, i.e., polynomials that approximate indicator functions in -norm under certain distributions, are widely used in computational learning theory.…
Prediction-Augmented Trees for Reliable Statistical Inference
Vikram Kher, Argyris Oikonomou, Manolis Zampetakis
The remarkable success of machine learning (ML) in predictive tasks has led scientists to incorporate ML predictions as a core component of the scientific discovery pipeline. This…