\begin{algorithm}[!ht]\caption{LSH, private procedures}\label{alg:LSH_private_app}
\begin{algorithmic}[1]
\State {\bf data structure} \textsc{LSH}
\State
\State {\bf private}
\Procedure{\textsc{ChooseHashFunc}}{$k,L\in \mathbb{N}_+$}\label{lin:choose_hash_func}
    \For{$l \in [L]$}
        \State \Comment{Amplify hash functions by concatenating}
        \State $\mathcal{H}_{l} \leftarrow$ sample $k$ hash functions $(f_{1,l},f_{2,l},\cdots,f_{k,l})$ from $\mathcal{H}$ \label{lin:LSH_sample_k_functions}
    \EndFor
\EndProcedure

\State
\Procedure{\textsc{ConstructHashTable}}{$\{x_i\}_{i\in[n]}\subset \mathbb{R}^d$}\label{lin:construct_hash_table}
    \For{$l\in [L]$}
        \For{$i\in [n]$}
            \State $\mathcal{H}_l(x_i)$.\textsc{Insert}($x_i$) \label{lin:find_bucket_insert_element}
            \State $\mathcal{T}_l\leftarrow \mathcal{T}_l\cup \mathcal{H}_l(x_i)$ \Comment{Creat hashtable by aggregating buckets} \label{lin:aggregate_hash_table}
        \EndFor
    \EndFor
\EndProcedure

\State {\bf end data structure}
\end{algorithmic}
\end{algorithm}

\begin{algorithm}[!ht]
\caption{LSH, private procedures}\begin{algorithmic}
[1]
\State {\bf data structure} \textsc{LSH}
\State
\State {\bf private}
\Procedure{\textsc{ChooseHashFunc}}{$k,L\in \mathbb{N}_+$} \For{$l \in [L]$}
\State \Comment{Amplify hash functions by concatenating}
\State $\mathcal{H}_{l} \leftarrow$ sample $k$ hash functions $(f_{1,l},f_{2,l},\cdots,f_{k,l})$ from $\mathcal{H}$ \EndFor
\EndProcedure
\State
\Procedure{\textsc{ConstructHashTable}}{$\{x_i\}_{i\in[n]}\subset \mathbb{R}^d$} \For{$l\in [L]$}
\For{$i\in [n]$}
\State $\mathcal{H}_l(x_i)$.\textsc{Insert}($x_i$) \State $\mathcal{T}_l\leftarrow \mathcal{T}_l\cup \mathcal{H}_l(x_i)$ \Comment{Creat hashtable by aggregating buckets} \EndFor
\EndFor
\EndProcedure
\State {\bf end data structure}
\end{algorithmic}
\end{algorithm}
{
    "title": "dynamic maintenance of kernel density estimation data structure: from   practice to theory",
    "id": "2208.03915",
    "abstract": "kernel density estimation (kde) stands out as a challenging task in machine learning. the problem is defined in the following way: given a kernel function $f(x,y)$ and a set of points $\\{x_1, x_2, \\cdots, x_n \\} \\subset \\mathbb{r}^d$, we would like to compute $\\frac{1}{n}\\sum_{i=1}^{n} f(x_i,y)$ for any query point $y \\in \\mathbb{r}^d$. recently, there has been a growing trend of using data structures for efficient kde. however, the proposed kde data structures focus on static settings. the robustness of kde data structures over dynamic changing data distributions is not addressed. in this work, we focus on the dynamic maintenance of kde data structures with robustness to adversarial queries. especially, we provide a theoretical framework of kde data structures. in our framework, the kde data structures only require subquadratic spaces. moreover, our data structure supports the dynamic update of the dataset in sublinear time. furthermore, we can perform adaptive queries with the potential adversary in sublinear time.",
    "categories": "cs.lg stat.ml",
    "doi": "",
    "created": "2022-08-08",
    "updated": "2024-02-13",
    "authors": [
        "jiehao liang",
        "zhao song",
        "zhaozhuo xu",
        "junze yin",
        "danyang zhuo"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2208.03915"
}
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