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REPORTS > KEYWORD > HALFSPACES:
Reports tagged with Halfspaces:
TR06-057 | 19th April 2006

#### Cryptographic Hardness Results for Learning Intersections of Halfspaces

We give the first representation-independent hardness results for
PAC learning intersections of halfspaces, a central concept class
in computational learning theory. Our hardness results are derived
from two public-key cryptosystems due to Regev, which are based on the
worst-case hardness of well-studied lattice problems. Specifically, we
prove that a polynomial-time ... more >>>

TR06-059 | 3rd May 2006
Vitaly Feldman, Parikshit Gopalan, Subhash Khot, Ashok Kumar Ponnuswami

#### New Results for Learning Noisy Parities and Halfspaces

We address well-studied problems concerning the learnability of parities and halfspaces in the presence of classification noise.

Learning of parities under the uniform distribution with random classification noise,also called the noisy parity problem is a famous open problem in computational learning. We reduce a number of basic problems regarding ... more >>>

TR07-128 | 10th November 2007
Kevin Matulef, Ryan O'Donnell, Ronitt Rubinfeld, Rocco Servedio

#### Testing Halfspaces

This paper addresses the problem of testing whether a Boolean-valued function f is a halfspace, i.e. a function of the form f(x)=sgn(w &#8901; x - &#952;). We consider halfspaces over the continuous domain R^n (endowed with the standard multivariate Gaussian distribution) as well as halfspaces over the Boolean cube {-1,1}^n ... more >>>

TR10-025 | 24th February 2010
Alexander A. Sherstov

#### Optimal bounds for sign-representing the intersection of two halfspaces by polynomials

The threshold degree of a function
$f\colon\{0,1\}^n\to\{-1,+1\}$ is the least degree of a
real polynomial $p$ with $f(x)\equiv\mathrm{sgn}\; p(x).$ We
prove that the intersection of two halfspaces on
$\{0,1\}^n$ has threshold degree $\Omega(n),$ which
matches the trivial upper bound and completely answers
a question due to Klivans (2002). The best ... more >>>

TR13-008 | 7th January 2013

#### Moment-Matching Polynomials

We give a new framework for proving the existence of low-degree, polynomial approximators for Boolean functions with respect to broad classes of non-product distributions. Our proofs use techniques related to the classical moment problem and deviate significantly from known Fourier-based methods, which require the underlying distribution to have some product ... more >>>

TR14-166 | 8th December 2014
Mark Bun, Thomas Steinke

#### Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness

Polynomial approximations to boolean functions have led to many positive results in computer science. In particular, polynomial approximations to the sign function underly algorithms for agnostically learning halfspaces, as well as pseudorandom generators for halfspaces. In this work, we investigate the limits of these techniques by proving inapproximability results for ... more >>>

TR17-115 | 5th July 2017
Arnab Bhattacharyya, Suprovat Ghoshal, Rishi Saket

#### Hardness of learning noisy halfspaces using polynomial thresholds

We prove the hardness of weakly learning halfspaces in the presence of adversarial noise using polynomial threshold functions (PTFs). In particular, we prove that for any constants $d \in \mathbb{Z}^+$ and $\epsilon > 0$, it is NP-hard to decide: given a set of $\{-1,1\}$-labeled points in $\mathbb{R}^n$ whether (YES Case) ... more >>>

TR19-016 | 5th February 2019
Alexander A. Sherstov

#### The hardest halfspace

We study the approximation of halfspaces $h:\{0,1\}^n\to\{0,1\}$ in the infinity norm by polynomials and rational functions of any given degree. Our main result is an explicit construction of the "hardest" halfspace, for which we prove polynomial and rational approximation lower bounds that match the trivial upper bounds achievable for all ... more >>>

TR19-161 | 13th November 2019
Suprovat Ghoshal, Rishi Saket

#### Hardness of Learning DNFs using Halfspaces

The problem of learning $t$-term DNF formulas (for $t = O(1)$) has been studied extensively in the PAC model since its introduction by Valiant (STOC 1984). A $t$-term DNF can be efficiently learnt using a $t$-term DNF only if $t = 1$ i.e., when it is an AND, while even ... more >>>

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