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README.md

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@@ -85,13 +85,9 @@ $\text{Acc.}$ is the accuracy of models trained on different samples. Samples' m
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- $\text{rdm-any}$: Randomly selected dataset (under the same compression ratio) with the same personalized evaluation methods.
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DD-Ranking uses a weight sum of $\text{IOR}$ and $-\text{HLR}$ to rank different methods:
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$$
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\alpha = w\text{IOR}-(1-w)\text{HLR}, \quad w \in [0, 1]
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$$
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$$\alpha = w\text{IOR}-(1-w)\text{HLR}, \quad w \in [0, 1]$$
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Formally, the **DD-Ranking Score (DDRS)** is defined as:
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$$
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\text{DDRS} = \frac{e^{\alpha}-e^{-1}}{e - e^{-1}}
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$$
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$$\text{DDRS} = \frac{e^{\alpha}-e^{-1}}{e - e^{-1}}$$
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By default, we set $w = 0.5$ on the leaderboard, meaning that both $\text{IOR}$ and $\text{HLR}$ are equally important. Users can adjust the weights to emphasize one aspect on the leaderboard.
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</details>

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