Lunjia Hu | DeepAI

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∙ 05/30/2023

When Does Optimizing a Proper Loss Yield Calibration?

Optimizing proper loss functions is popularly believed to yield predicto...

0 Jarosław Błasiok, et al. ∙

∙ 04/19/2023

Loss minimization yields multicalibration for large neural networks

Multicalibration is a notion of fairness that aims to provide accurate p...

0 Jarosław Błasiok, et al. ∙

∙ 02/24/2023

Generative Models of Huge Objects

This work initiates the systematic study of explicit distributions that ...

0 Lunjia Hu, et al. ∙

∙ 11/30/2022

A Unifying Theory of Distance from Calibration

We study the fundamental question of how to define and measure the dista...

0 Jarosław Błasiok, et al. ∙

∙ 11/16/2022

Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes

In many learning theory problems, a central role is played by a hypothes...

0 Lunjia Hu, et al. ∙

∙ 10/24/2022

Subspace Recovery from Heterogeneous Data with Non-isotropic Noise

Recovering linear subspaces from data is a fundamental and important tas...

0 John Duchi, et al. ∙

∙ 10/16/2022

Loss Minimization through the Lens of Outcome Indistinguishability

We present a new perspective on loss minimization and the recent notion ...

0 Parikshit Gopalan, et al. ∙

∙ 09/15/2022

Omnipredictors for Constrained Optimization

The notion of omnipredictors (Gopalan, Kalai, Reingold, Sharan and Wiede...

2 Lunjia Hu, et al. ∙

∙ 03/09/2022

Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability

We give the first sample complexity characterizations for outcome indist...

4 Lunjia Hu, et al. ∙

∙ 11/08/2021

An Improved Local Search Algorithm for k-Median

We present a new local-search algorithm for the k-median clustering prob...

0 Vincent Cohen-Addad, et al. ∙

∙ 06/29/2021

Near-Optimal Explainable k-Means for All Dimensions

Many clustering algorithms are guided by certain cost functions such as ...

21 Moses Charikar, et al. ∙

∙ 03/02/2021

Approximation Algorithms for Orthogonal Non-negative Matrix Factorization

In the non-negative matrix factorization (NMF) problem, the input is an ...

0 Moses Charikar, et al. ∙

∙ 08/18/2020

Robust Mean Estimation on Highly Incomplete Data with Arbitrary Outliers

We study the problem of robustly estimating the mean of a d-dimensional ...

14 Lunjia Hu, et al. ∙

∙ 03/24/2020

Robust and On-the-fly Dataset Denoising for Image Classification

Memorization in over-parameterized neural networks could severely hurt g...

0 Jiaming Song, et al. ∙

∙ 02/25/2020

The Power of Many Samples in Query Complexity

The randomized query complexity R(f) of a boolean function f{0,1}^n→{0,1...

0 Andrew Bassilakis, et al. ∙

∙ 10/28/2018

Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation

It is widely believed that learning good representations is one of the m...

0 Liwei Wang, et al. ∙

∙ 11/01/2017

Active Tolerant Testing

In this work, we give the first algorithms for tolerant testing of nontr...

0 Avrim Blum, et al. ∙

∙ 02/18/2017

Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes

In this work we study the quantitative relation between the recursive te...

0 Lunjia Hu, et al. ∙

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