Daniel Kane

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University of California, San Diego

Assistant Professor, Computer Science and Engineering & Dept. of Mathematics, Mathematical prodigy. By 3rd grade, he had mastered K through 9th-grade mathematics, At age 13, he took honors math courses at the University of Wisconsin–Madison and did research under the mentorship of Ken Ono while dual enrolled at Madison West High School, Gold medals in the 2002 and 2003 International Mathematical Olympiads, Fellow Laureate of the Davidson Institute for Talent Development.

Featured Co-authors

Ilias Diakonikolas
64 publications
Jerry Li
52 publications
Ankur Moitra
46 publications
Christos Tzamos
44 publications
Shay Moran
42 publications
Eric Price
37 publications
Dan Feldman
36 publications
Gautam Kamath
34 publications
Li-Yang Tan
28 publications
Rocco A. Servedio
25 publications
Yuval Filmus
25 publications

∙ 03/06/2022

Coresets for Data Discretization and Sine Wave Fitting

In the monitoring problem, the input is an unbounded stream P=p_1,p_2⋯ o...

Alaa Maalouf, et al. ∙

∙ 02/11/2022

Computational-Statistical Gaps in Reinforcement Learning

Reinforcement learning with function approximation has recently achieved...

Daniel Kane, et al. ∙

∙ 11/08/2021

Realizable Learning is All You Need

The equivalence of realizable and agnostic learnability is a fundamental...

Max Hopkins, et al. ∙

∙ 06/14/2021

Boosting in the Presence of Massart Noise

We study the problem of boosting the accuracy of a weak learner in the (...

Ilias Diakonikolas, et al. ∙

∙ 03/13/2021

Fooling Gaussian PTFs via Local Hyperconcentration

We give a pseudorandom generator that fools degree-d polynomial threshol...

Ryan O'Donnell, et al. ∙

∙ 02/09/2021

Bounded Memory Active Learning through Enriched Queries

The explosive growth of easily-accessible unlabeled data has lead to gro...

Max Hopkins, et al. ∙

∙ 01/06/2021

Highway: Efficient Consensus with Flexible Finality

There has been recently a lot of progress in designing efficient partial...

Daniel Kane, et al. ∙

∙ 05/13/2020

Robustly Learning any Clusterable Mixture of Gaussians

We study the efficient learnability of high-dimensional Gaussian mixture...

Ilias Diakonikolas, et al. ∙

∙ 01/15/2020

Noise-tolerant, Reliable Active Classification with Comparison Queries

With the explosion of massive, widely available unlabeled data in the pa...

Max Hopkins, et al. ∙

∙ 11/19/2019

Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering

We study high-dimensional sparse estimation tasks in a robust setting wh...

Ilias Diakonikolas, et al. ∙

∙ 02/10/2019

The Optimal Approximation Factor in Density Estimation

Consider the following problem: given two arbitrary densities q_1,q_2 an...

Olivier Bousquet, et al. ∙

∙ 11/06/2018

The entropy of lies: playing twenty questions with a liar

`Twenty questions' is a guessing game played by two players: Bob thinks ...

Yuval Dagan, et al. ∙

∙ 09/15/2017

The Orthogonal Vectors Conjecture for Branching Programs and Formulas

In the Orthogonal Vectors (OV) problem, we wish to determine if there is...

Daniel Kane, et al. ∙

∙ 06/23/2016

Robust Learning of Fixed-Structure Bayesian Networks

We investigate the problem of learning Bayesian networks in an agnostic ...

Ilias Diakonikolas, et al. ∙

∙ 04/21/2016

Robust Estimators in High Dimensions without the Computational Intractability

We study high-dimensional distribution learning in an agnostic setting w...

Ilias Diakonikolas, et al. ∙