24 citations · 51 across the 32 of their papers we have counts for
11 papers · 1 filter
Optimal Rates for Learning with Monotone Adversaries
Anay Mehrotra
A monotone adversary observes an i.i.d. labeled sample and appends a finite number of further examples of its choice, every one of them labeled correctly by the target hypothesis.…
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…
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.…
Differentially Private Language Generation and Identification in the Limit
Anay Mehrotra, Grigoris Velegkas, Xifan Yu +1
We initiate the study of language generation in the limit, a model recently introduced by Kleinberg and Mullainathan [KM24], under the constraint of differential privacy. We consid…
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…
Language Generation with Infinite Contamination
Anay Mehrotra, Grigoris Velegkas, Xifan Yu +1
We study language generation in the limit, where an algorithm observes an adversarial enumeration of strings from an unknown target language and must eventually generate new, u…
Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases
Alkis Kalavasis, Anay Mehrotra, Felix Zhou
We revisit the problem of estimating linear regressors with self-selection bias in dimensions with the maximum selection criterion, as introduced by Cherapanamjeri, Daskala…
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…
DDPM Score Matching and Distribution Learning
Sinho Chewi, Alkis Kalavasis, Anay Mehrotra +1
Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key result in this area shows that with acc…