7 citations · 15 across the 8 of their papers we have counts for
10 papers
On efficient robust regression with subquadratic samples
Deeksha Adil, Jarosław Błasiok, Hongjie Chen +1
We revisit the problem of robust linear regression under Gaussian covariates with an unknown covariance matrix of condition number . For this fundamental problem, significant ga…
Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing
Jarosław Błasiok, Preetum Nakkiran
Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors. Calibration measures quantify th…
When Does Optimizing a Proper Loss Yield Calibration?
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +1
Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to pred…
Loss Minimization Yields Multicalibration for Large Neural Networks
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +2
Multicalibration is a notion of fairness for predictors that requires them to provide calibrated predictions across a large set of protected groups. Multicalibration is known to be…
A Unifying Theory of Distance from Calibration
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +1
We study the fundamental question of how to define and measure the distance from calibration for probabilistic predictors. While the notion of perfect calibration is well-understoo…
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu +2
Having similar behavior at training time and test time what we call a "What You See Is What You Get" (WYSIWYG) property is desirable in machine learning. Models trained wit…
Polar Codes with exponentially small error at finite block length
Jarosław Błasiok, Venkatesan Guruswami, Madhu Sudan
We show that the entire class of polar codes (up to a natural necessary condition) converge to capacity at block lengths polynomial in the gap to capacity, while simultaneously ach…
The Generic Holdout: Preventing False-Discoveries in Adaptive Data Science
Preetum Nakkiran, Jarosław Błasiok
Adaptive data analysis has posed a challenge to science due to its ability to generate false hypotheses on moderately large data sets. In general, with non-adaptive data analyses (…
General Strong Polarization
Jarosław Błasiok, Venkatesan Guruswami, Preetum Nakkiran +2
Arikan's exciting discovery of polar codes has provided an altogether new way to efficiently achieve Shannon capacity. Given a (constant-sized) invertible matrix , a family of p…
Predicting Positive and Negative Links with Noisy Queries: Theory & Practice
Charalampos E. Tsourakakis, Michael Mitzenmacher, Kasper Green Larsen +4
Social networks involve both positive and negative relationships, which can be captured in signed graphs. The {\em edge sign prediction problem} aims to predict whether an interact…