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Electronic Colloquium on Computational Complexity

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All reports by Author Suprovat Ghoshal:

TR19-079 | 28th May 2019
Arnab Bhattacharyya, Philips George John, Suprovat Ghoshal, Raghu Meka

Average Bias and Polynomial Sources

Revisions: 2

We identify a new notion of pseudorandomness for randomness sources, which we call the average bias. Given a distribution $Z$ over $\{0,1\}^n$, its average bias is: $b_{\text{av}}(Z) =2^{-n} \sum_{c \in \{0,1\}^n} |\mathbb{E}_{z \sim Z}(-1)^{\langle c, z\rangle}|$. A source with average bias at most $2^{-k}$ has min-entropy at least $k$, and ... more >>>

TR18-057 | 26th March 2018
Arnab Bhattacharyya, Suprovat Ghoshal, Karthik C. S., Pasin Manurangsi

Parameterized Intractability of Even Set and Shortest Vector Problem from Gap-ETH

The $k$-Even Set problem is a parameterized variant of the Minimum Distance Problem of linear codes over $\mathbb F_2$, which can be stated as follows: given a generator matrix $\mathbf A$ and an integer $k$, determine whether the code generated by $\mathbf A$ has distance at most $k$. Here, $k$ ... 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 >>>

TR15-193 | 26th November 2015
Arnab Bhattacharyya, Ameet Gadekar, Suprovat Ghoshal, Rishi Saket

On the hardness of learning sparse parities

This work investigates the hardness of computing sparse solutions to systems of linear equations over $\mathbb{F}_2$. Consider the $k$-EvenSet problem: given a homogeneous system of linear equations over $\mathbb{F}_2$ on $n$ variables, decide if there exists a nonzero solution of Hamming weight at most $k$ (i.e. a $k$-sparse solution). While ... more >>>

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