1 code implementation • 21 Sep 2023 • Jarosław Błasiok, Preetum Nakkiran
We show that a simple modification fixes both constructions: first smooth the observations using an RBF kernel, then compute the Expected Calibration Error (ECE) of this smoothed function.
no code implementations • NeurIPS 2023 • Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
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 predict the ground-truth probabilities, which is indeed calibrated.
no code implementations • 19 Apr 2023 • Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Adam Tauman Kalai, Preetum Nakkiran
We show that minimizing the squared loss over all neural nets of size $n$ implies multicalibration for all but a bounded number of unlucky values of $n$.
no code implementations • 30 Nov 2022 • Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, Preetum Nakkiran
We study the fundamental question of how to define and measure the distance from calibration for probabilistic predictors.
1 code implementation • 7 Apr 2022 • Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jarosław Błasiok, Preetum Nakkiran
In contrast, we show that Differentially-Private (DP) training provably ensures the high-level WYSIWYG property, which we quantify using a notion of distributional generalization.
no code implementations • 14 Sep 2018 • Preetum Nakkiran, Jarosław Błasiok
In this work, we propose a new framework for adaptive science which exponentially improves on this number of queries under a restricted yet scientifically relevant setting, where the goal of the scientist is to find a single (or a few) true hypotheses about the universe based on the samples.
no code implementations • 5 Apr 2018 • Jarosław Błasiok
The distinct elements problem is one of the fundamental problems in streaming algorithms --- given a stream of integers in the range $\{1,\ldots, n\}$, we wish to provide a $(1+\varepsilon)$ approximation to the number of distinct elements in the input.
Data Structures and Algorithms
1 code implementation • 19 Sep 2017 • Charalampos E. Tsourakakis, Michael Mitzenmacher, Kasper Green Larsen, Jarosław Błasiok, Ben Lawson, Preetum Nakkiran, Vasileios Nakos
The {\em edge sign prediction problem} aims to predict whether an interaction between a pair of nodes will be positive or negative.
1 code implementation • 17 Sep 2016 • Jarosław Błasiok, Charalampos E. Tsourakakis
We verify experimentally the efficiency of our method on numerous real-world datasets, where we find that our method ($<$10 secs) is more than 3\, 000$\times$ faster than the state-of-the-art method \cite{hedge2015} ($>$9 hours) on medium scale datasets with 60\, 000 data points in 784 dimensions.
no code implementations • 18 Feb 2016 • Jarosław Błasiok, Jelani Nelson
Then, given some small number $p$ of samples, i. e.\ columns of $Y$, the goal is to learn the dictionary $A$ up to small error, as well as $X$.