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\begin{tabular}{l|rr} \toprule \textbf{Task}& \textbf{BC (Expert)} & \textbf{CQL (Noisy Expert)} \\ \midrule pick-place-open-grasp & 14.5\% $\pm$ 1.8\% & \textbf{85.7\%} $\pm$ 3.1\%\\ close-open-grasp & 17.4\% $\pm$ 3.1\% & \textbf{90.3\%} $\pm$ 2.3\%\\ open-grasp & 33.2\% $\pm$ 8.1\% & \textbf{92.4\%} $\pm$ 4.9\% \\ \...
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\begin{tabular}{l|r|rrr} \toprule \textbf{Domain / Behavior Policy} & \textbf{Task/Data Quality}& \textbf{BC} & \textbf{Na\"ive CQL} & \textbf{Tuned CQL} \\ \midrule \textbf{7 Atari games (RL policy)} & Pong, Expert & 109.78 $\pm$ 2.93 & 102.03 $\pm$ 4.43 & 105.84 $\pm$ 2.22\\ & Breakout, Expert & 75.59 $\pm$ 21.59 & 7...
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\begin{tabular}{l|rr} \toprule \textbf{Task}& \textbf{BC-PI} & \textbf{CQL} \\ \midrule Pong & 100.03 $\pm$ 5.01 & 94.48 $\pm$ 8.39\\ Breakout & 25.99 $\pm$ 1.98 & 86.92 $\pm$ 13.74 \\ Asterix & 29.77 $\pm$ 5.33 & 157.54 $\pm$ 37.94\\ SpaceInvaders & 31.45 $\pm$ 1.96 & 63.7 $\pm$ 16.18\\ Q*bert & 106.06 $\pm$ 8.63 & 88...
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\begin{tabular}{|l|c|c|c|} \hline & Yelp & Clothing & Movies \\ \hline \# of users & 6,784 & 21,181 & 81,780 \\\hline \# of items & 10,003 & 17,710 & 24,628 \\\hline \# of interactions & 106,630 & 145,281 & 1,028,839 \\\hline sparsity & 0.16\% & 0.04\% & 0.05\% \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|} \hline Model & Yelp & Clothing & Movies \\ \hline VCM-Se & 0.036 & 0.015 & 0.046 \\ \hline VCM-OD & 0.047 & 0.025 & 0.047 \\ \hline VCM-NV & 0.044 & 0.024 & 0.051 \\ \hline VCM & \textbf{0.051$\dag$} & \textbf{0.027$\dag$} & \textbf{0.057$\dag$} \\ \hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|} \hline & \multicolumn{2}{c|}{\textbf{VCM-Se}} & \multicolumn{2}{c|}{\textbf{VCM}} \\ \hline \textbf{level} & \textbf{$\hat{c}_{u}$} & \textbf{NDCG} & \textbf{$\hat{c}_{u}$} & \textbf{NDCG} \\ \hline \multicolumn{5}{|c|}{\textbf{Yelp}} \\ \hline \textbf{5-20} & 9.7 & 0.032 & \textbf{18.3(+87...
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\begin{tabular}{l|ccc} \toprule Structure & RNA Splicing & Eye-tracking for ASD & MNIST \\ \midrule 1-hidden layer & {[}30{]} & {[}500{]} & {[}100{]} \\ 2-hidden layer & {[}30, 10{]} & \textbf{{[}3000, 500{]}} & {[}1000, 100{]} \\ 3-hidden layer & \textbf{{[}30, 20, 10{]}} & {[}3000, 1000, 500{]} & \textbf{{[}1000, 100...
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\begin{tabular}{lrr} \toprule \textbf{Dataset} & $\Bar{S}$ & $\Bar{R}$ [\%] \\ \midrule \textbf{Iris} & $0.41$ & $0.05\,(0.04)$ \\ \textbf{Half-moons} & $0.37$ & $7.01\,(17.1)$\\ \textbf{BostonRF} & $0.63$ & $16.0\,(25.2)$ \\ \textbf{BostonSVC} & $0.29$ & $6.1\,(13.6)$\\ \textbf{Recidivism} & $0.81$ & $26.6\,(30.8)$\\ ...
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\begin{tabular}{lrrr} \toprule \textbf{Dataset} & \textbf{HCLS} & \textbf{GS} & \textbf{LORE} \\ \midrule \textbf{Iris} & $0.63$ & $0.63$ & $0.70$ \\ \textbf{Half-moons} & $0.83$ & $0.67$ & $0.83$ \\ \textbf{BostonSVC} & $0.95$ & $0.93$ & $1.0$ \\ \textbf{BostonRF} & $0.86$ & $0.84$ & $1.0$\\ \textbf{Recidivism} & $0.9...
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\begin{tabular}{ccccc|ccccc} \toprule Synthetic & $\boldsymbol{x}$ & $\boldsymbol{t}_1$ & $\boldsymbol{t}_2$ & $\hat{\boldsymbol{y}}$& FMNIST & $\boldsymbol{x}$ & $\boldsymbol{t}_1$ & $\boldsymbol{t}_2$ & $\hat{\boldsymbol{y}}$ \\ \midrule MLP1 & 1024 & 11 & 6 & 2 & MLP4 & 784 & 512 & 256 & 10\\ MLP2 & 1024 & 7 & 6 & 2...
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\begin{tabular}{c||c} \hline Unigram & Bilinear:linear mean $L_1$ ratio \\ \hline \hline $\land$ & 1.95 \\ $\lor$ & 1.63 \\ $c$ & 1.47 \\ $d$ & 1.41 \\ $b$ & 1.34 \\ $f$ & 1.22 \\ $a$ & 1.21 \\ $e$ & 1.18 \\ $\neg$ & 0.89 \\ $($ & 0.65\\ $)$ & 0.38 \\ \hline \end{tabular}
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\begin{tabular}{p{15mm}||p{70mm}p{30mm}} \hline PoS Tag & Definition & Example \\ \hline \hline CC & Coordinating conjunction & and, or \\ CD & Cardinal Number & 5, seven \\ DT & Determiner & the, these, any \\ FW & Foreign Word & ich, oui \\ IN & Preposition & in, of, like \\ JJ & Adjective & white, large, opaque \\ N...
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\begin{tabular}{lcccc} \toprule & \multicolumn{4}{c}{\textbf{\# GPU} (\# CPU)} \\ Algo & \textbf{1} (5) & \textbf{2} (10) & \textbf{4} (20) & \textbf{8} (40) \\ \midrule A2C & 3.8 & 2.2 & 1.2 & 0.59 \\ A3C & -- & 2.4 & 1.3 & 0.65 \\ PPO & 4.4 & 2.6 & 1.5 & 1.1 \\ APPO & -- & 2.8 & 1.5 & 0.71 \\ \toprule Algo-B.S. & \te...
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\begin{tabular}{lcc} \toprule \# Sims (per Core) & Sync & Async \\ \midrule 64 (2) & 29.6 & 31.9 \\ 128 (4) & 33.0 & 34.7 \\ 256 (8) & 35.7 & 36.7 \\ 512 (16) & 35.8 & 38.4 \\ \bottomrule \end{tabular}
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\begin{tabular}{l|l} \toprule & Layer type (no.~of dimensions) and nonlinearity\\ \hline & Input ($|V|$)\\ 1 to $r$ & LSTM ($\gamma$)\\ & \qquad sigmoid (gates); tanh (hidden and cell state update)\\ & \qquad orthogonal initialization, gradient clipping at 50.0\\ $r$ + 1 & Fully connected dense (1,024)\\ & \qquad leaky...
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\begin{tabular}{l|cccc} \toprule & $r=1, \gamma=128$ & $r=1, \gamma=512$ & $r=2, \gamma=128$ & $r=2, \gamma=512$\\ \hline Scheme 1 & 60.5 / 7.0 & 138.8 / 21.7 & 106.5 / 13.7 & 267.4 / 43.3\\ Scheme 2 & 46.5 / 7.0 & 114.6 / 21.7 & 93.1 / 13.7 & 245.8 / 43.3\\ Scheme 3 & 60.5 / 0.7 & 138.8 / 1.2 & 106.5 / 1.1 & 267.5 / 2...
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\begin{tabular}{ccccrcc} \toprule &&&\multicolumn{4}{c}{Number of successful rotations} \\ \cmidrule(r){4-7} Run & Policy Checkpoint & Time (days) & Transfer metric & ADR entropy & Mean & p-value \\ \midrule 1 & 1 & 0.74 & 80.7 & $-\infty$ npd & 1.55 & 0.022 \\ & 2 & 3.8 & 61.6 & $-0.78$ npd & 3.35 & 0.54 \\ & 3 & 5.4 ...
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\begin{tabular}{l|c|cc|cc|cc|c} \toprule \multicolumn{1}{c|}{\bf Model} &\multicolumn{1}{c|}{\bf SGD-M} &\multicolumn{1}{c}{\bf SAM-5} &\multicolumn{1}{c|}{\bf LookSAM-5} &\multicolumn{1}{c}{\bf SAM-10} &\multicolumn{1}{c|}{\bf LookSAM-10} &\multicolumn{1}{c}{\bf SAM-20} &\multicolumn{1}{c|}{\bf LookSAM-20} &\multicolu...
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\begin{tabular}{c|c|cc|cc|c} \toprule \multicolumn{1}{c|}{\bf Model} &\multicolumn{1}{c|}{\bf AdamW} &\multicolumn{1}{c}{\bf SAM-5} &\multicolumn{1}{c|}{\bf LookSAM-5} &\multicolumn{1}{c}{\bf SAM-10} &\multicolumn{1}{c|}{\bf LookSAM-10} &\multicolumn{1}{c}{\bf SAM} \\ \hline \multicolumn{1}{c|}{\bf ViT-B-16} & 74.7/59....
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\begin{tabular}{lcccccc} \toprule \multicolumn{1}{c}{\bf Model} & \multicolumn{1}{c}{\bf Algorithm} &\multicolumn{1}{c}{\bf RandAug} &\multicolumn{1}{c}{\bf Mixup} &\multicolumn{1}{c}{\bf Optimizer} &\multicolumn{1}{c}{\bf 32k} &\multicolumn{1}{c}{\bf 64k} \\ \hline \multicolumn{1}{l}{\bf ViT-B-16} &\multicolumn{1}{l}{...
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\begin{tabular}{lcccc} \toprule \multicolumn{1}{c}{\bf Algorithm} &\multicolumn{1}{c}{\bf 4k} &\multicolumn{1}{c}{\bf 8k} &\multicolumn{1}{c}{\bf 16k} &\multicolumn{1}{c}{\bf 32k} \\ \hline \multicolumn{1}{l}{\bf LAMB} &74.6 &74.3 & 74.4 & 72.4\\ \multicolumn{1}{l}{\bf LAMB + SAM} &78.6 &78.3 &77.6 &75.1 \\ \multicolum...
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\begin{tabular}{lcccc} \toprule \multicolumn{1}{c}{\bf Algorithm} &\multicolumn{1}{c}{\bf 4k} &\multicolumn{1}{c}{\bf 8k} &\multicolumn{1}{c}{\bf 16k} &\multicolumn{1}{c}{\bf 32k} \\ \hline \multicolumn{1}{l}{\bf LAMB } & \textbf{4.8h} & \textbf{2.4h} & \textbf{1.2h} & / \\ \multicolumn{1}{l}{\bf LAMB + LayerSAM} & 8.4...
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\begin{tabular}{lccc} \toprule \multicolumn{1}{l}{\bf Batch Size} &\multicolumn{1}{c}{\bf $\alpha$ = 0.5} &\multicolumn{1}{c}{\bf $\alpha$ = 0.7} &\multicolumn{1}{c}{\bf $\alpha$ = 1.0} \\ \hline \multicolumn{1}{l}{\bf 16384} & 77.7 & \textbf{78.4} & 78.2 \\ \multicolumn{1}{l}{\bf 32768} &76.5 & \textbf{77.1} & 75.9 \\...
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\begin{tabular}{lcccc} \toprule \multicolumn{1}{l}{\bf Batch Size} &\multicolumn{1}{c}{\bf $\rho$ = 0.5} &\multicolumn{1}{c}{\bf $\rho$ = 0.8} &\multicolumn{1}{c}{\bf $\rho$ = 1.0} &\multicolumn{1}{c}{\bf $\rho$ = 1.2} \\ \hline \multicolumn{1}{l}{\bf 16384} & 77.0 & 77.8 & \textbf{78.4} & 77.9 \\ \multicolumn{1}{l}{\b...
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\begin{tabular}{lcccccc} \multicolumn{1}{c}{\bf Model} &\multicolumn{1}{c}{\bf Params} &\multicolumn{1}{c}{\bf Patch Resolution} &\multicolumn{1}{c}{\bf Sequence Length} &\multicolumn{1}{c}{\bf Hidden Size} &\multicolumn{1}{c}{\bf Heads} &\multicolumn{1}{c}{\bf Layers} \\ \hline \\ ViT-B-16 & 87M & $16 \times 16$ & 196...
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\begin{tabular}{l|c|c} \hline Methods & Training & Inference \\ \hline TSC$_{indp}$ & $\sum_{b=1}^{B}L_bC_b$ & $\sum_{b=1}^{B}L_bC_b$\\ \hline TSC$_{2fc}$ & $\sum_{b=1}^{B}L_b\frac{C_b^{2}}{r}(1 + k_{1}k_{2})$ & $\sum_{b=1}^{B}L_bC_b$\\ \hline TSC$_{LSTM}$ & $\sum_{b=1}^{B}\frac{C_b^{2}}{r}(1+ \frac{8}{r} + k_{1}k_{2})...
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\begin{tabular}{l|c|c|c} \hline \multirow{2}*{Methods} & top-1 & Params(M) & GFLOPs\\ ~ & \small (err.) & \small \emph{(train / infer)} & \small \emph{(train / infer)} \\ \hline ResNet (reported) & $24.70$ & $25.56$ & $3.86$\\ \hline ResNet (ours) & $24.42$ & $25.56$ & $3.86$\\ \hline TSC$_{indp}$-ResNet & $24.12$ & $2...
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\begin{tabular}{l|c|c|c|c|c|c} \hline \multirow{3}*{Models} & \multicolumn{3}{c|}{re-implementation} & \multicolumn{3}{c}{with our time stepping controller}\\ \cline{2-7} ~ & \multirow{2}*{Error. (\%)} & \multirow{2}*{Params(M)} & \multirow{2}*{GFLOPs} & Error. (\%) & Params(M) & GFLOPs\\ ~ & ~ & ~ & ~ & \small (gain) ...
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\begin{tabular}{l|c|c|c|c|c|c} \hline Models & C-3\_1 & C-4\_1 & C-4\_2 & C-5\_1 & C-5\_2 & C-5\_3 \\ \hline TSC-50 & 0.510 & 0.500 & 0.731 & 0.835 & 0.901 & 0.926 \\ TSC-101 & 0.508 & 0.513 & 0.657 & 0.854 & 0.927 & 0.950 \\ \hline \end{tabular}
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\begin{tabular}{l|c|c} \hline Methods & CIFAR-10 & CIFAR-100 \\ \hline DenseNet & 5.24 & 24.42\\ \hline TSC-DenseNet & 5.10 & 23.35 \\ \hline CliqueNet & 5.03 & 22.80\\ \hline TSC-CliqueNet & 4.79 & 22.04\\ \hline \end{tabular}
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\begin{tabular}{llrrrl} \toprule Held-out task & Method & Test (\%) & Train (\%) & AUC$^\dagger$ & \\ \midrule Facescrub & Leap & 19.9 & 0.0 & 11.6 & \\ & Finetuning & 32.7 & 0.0 & 13.2 & \\ & Progressive Nets$^\ddagger$ & \textbf{18.0} & 0.0 & \textbf{8.9} & \\ & HAT$^\ddagger$ & 25.6 & 0.1 & 14.6 & \\ & No pretrainin...
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\begin{tabular}{lrrrrrr} \toprule Method & Leap & Reptile & Finetuning$^\dagger$ & MAML & FOMAML & No pretraining \\ No. Pretraining tasks &&&&&& \\ \midrule 1 & 62.3 & 59.8 & 46.5 & 64.0 & 64.5 & 82.3 \\ 3 & 46.5 & 46.5 & 36.0 & 56.2 & 59.0 & 82.3 \\ 5 & 40.3 & 41.4 & 32.5 & 50.1 & 53.0 & 82.5 \\ 10 & 32.6 & 35.6 & 28...
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\begin{tabular}{lrrrrrr} \toprule & Leap & Finetuning & Reptile & MAML & FOMAML & No pretraining \\ &&&&&& \\ \midrule Meta training &&&&&& \\ \midrule \hspace{2pt} Learning rate & 0.1 & --- & 0.1 & 0.5 & 0.5 & --- \\ \hspace{2pt} Training steps & 1000 & 1000 & 1000 & 1000 & 1000 & --- \\ \hspace{2pt} Batch size (tasks...
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\begin{tabular}{llrrrl} \toprule Held-out task & Method & Test (\%) & Train (\%) & AUC$^\dagger$ & \\ \midrule Facescrub & Leap & 19.9 & 0.0 & 11.6 & \\ & Finetuning & 32.7 & 0.0 & 13.2 & \\ & Progressive Nets$^\ddagger$ & \textbf{18.0} & 0.0 & \textbf{8.9} & \\ & HAT$^\ddagger$ & 25.6 & 0.1 & 14.6 & \\ & No pretrainin...
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\begin{tabular}{lrrrrrr} \toprule & Leap & Finetuning & Progressive Nets & HAT & No pretraining \\ \midrule Meta training &&&&&& \\ \midrule \hspace{2pt} Learning rate & 0.01 & --- & --- & --- & --- \\ \hspace{2pt} Training steps & 1000 & 1000 & 1000 & 1000 & --- \\ \hspace{2pt} Batch size & 10 & 10 & 10 & 10 & --- \\ ...
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\begin{tabular}{lrrrc} \toprule Environment & Action Space & Mean Reward$^\dagger$ & Standard Deviation$^\dagger$ & Pretraining Env\\ \midrule AirRaid & 6 & 2538 & 624 & Y \\ UpNDown & 6 & 52417 & 2797 & Y \\ WizardOfWor & 10 & 2531 & 182 & Y \\ \midrule Breakout & 4 & 338 & 13 & N \\ SpaceInvaders & 6 & 1065 & 103 & N...
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\begin{tabular}{ccccccccc} \hline \multirow{2}*{Attack} & \multicolumn{2}{c}{VGG19} & \multicolumn{2}{c}{Resnet34} & \multicolumn{2}{c}{DenseNet121} & \multicolumn{2}{c}{MobilenetV2} \\ \cmidrule(r){2-3} \cmidrule(r){4-5} \cmidrule(r){6-7} \cmidrule(r){8-9} & Success & Queries & Success & Queries & Success & Queries & ...
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\begin{tabular}{ccc} \hline ConvNet1 & ConvNet2 & FCNet \\ \hline Conv(64, 5, 5)+ReLU & Conv(16, 3, 3)+ReLU & FC(512)+ReLU \\ MaxPool(2,2) & Conv(16, 3, 3)+ReLU & FC(10)+Softmax \\ Conv(64, 5, 5)+ReLU & MaxPool(2,2) & \\ MaxPool(2,2) & Conv(32, 3, 3)+ReLU & \\ Dropout(0.25) & Conv(32, 3, 3)+ReLU & \\ FC(128)+ReLU & Con...
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\begin{tabular}{cccc} \hline & MNIST & CIFAR10 & ImageNet\\\hline \multirow{12}*{Encoder} & ConvReLUBN(16,3,3) & ConvReLUBN(16,3,3) & ConvReLUBN(16,3,3) \\ & ConvReLUBN(32,3,3) & ConvReLUBN(32,3,3) & ConvReLUBN(32,3,3) \\ & ConvReLUBN(32,3,3) & ConvReLUBN(32,3,3) & MaxPool(2,2) \\ & MaxPool(2,2) & MaxPool(2,2) & ConvRe...
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\begin{tabular}{cccccc} \hline & \multirow{2}*{MNIST} & \multirow{2}*{CIFAR10} & \multicolumn{3}{c}{ImageNet} \\ \cmidrule(r){4-6} & & & Un-targeted & Targeted & Un-targeted Defense \\ \hline Sample size ($b$) & 20 & 20 & 20 & 20 & 20 \\ Learning rate ($\eta$)& 0.2 & 0.05 & 0.1 & 0.05 & 0.1 \\ \hline \end{tabular}
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\begin{tabular}{cccccc} \hline & \multirow{2}*{MNIST} & \multirow{2}*{CIFAR10} & \multicolumn{3}{c}{ImageNet} \\ \cmidrule(r){4-6} & & & Un-targeted & Targeted & Un-targeted Defense \\ \hline Sample size ($b$)& 20 & 20 & 20 & 20 & 20 \\ Learning rate ($\eta$)& 0.2 & 0.05 & 0.1 & 0.05 & 0.1 \\ White-box iteration & 50 &...
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\begin{tabular}{cccccc} \hline & \multirow{2}*{MNIST} & \multirow{2}*{CIFAR10} & \multicolumn{3}{c}{ImageNet} \\ \cmidrule(r){4-6} & & & Un-targeted & Targeted & Un-targeted Defense \\ \hline Sample size ($b$)& 20 & 20 & 20 & 20 & 20 \\ Learning rate ($\eta$)& 5.0 & 20.0 & 5.0 & 3.0 & 5.0 \\ \hline \end{tabular}
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\begin{tabular}{cccccc} \hline & \multirow{2}*{MNIST} & \multirow{2}*{CIFAR10} & \multicolumn{3}{c}{ImageNet} \\ \cmidrule(r){4-6} & & & Un-targeted & Targeted & Un-targeted Defense \\ \hline Sample size ($b$) & 20 & 20 & 20 & 20 & 20 \\ Learning rate ($\eta$)& 0.1 & 0.05 & 0.005 & 0.003 & 0.005 \\ White-box iteration ...
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\begin{tabular}{cccccc} \hline & \multirow{2}*{MNIST} & \multirow{2}*{CIFAR10} & \multicolumn{3}{c}{ImageNet} \\ \cmidrule(r){4-6} & & & Un-targeted & Targeted & Un-targeted Defense \\ \hline Sample size ($b$)& 20 & 20 & 20 & 20 & 20 \\ Learning rate ($\eta$)& 0.3 & 2.0 & 5.0 & 3.0 & 5.0 \\ \hline \end{tabular}
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\begin{tabular}{ccc} \hline &CIFAR10 Defense & ImageNet Defense \\ \hline Sample size ($b$)& 20 & 20 \\ Learning rate ($\eta$)& 2.0 & 5.0 \\ White-box iteration & 100 & 100 \\ White-box margin($\kappa$) & 100 & 100 \\ White-box learning rate & 1.0 & 2.0 \\\hline \end{tabular}
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\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline & $\gamma_{1} =0.075$ & $\gamma_{1} =0.10$ & $ \gamma_{1} =0.125$ & $\gamma_{1} =0.15$ & $ \gamma_{1} =0.175$ & $ \gamma_{1} =0.20$ & $\gamma_{1} =0.225$ & $ \gamma_{1} =0.25$ \\ \hline Precision & 58\% & 60\% & 60\% & 63\% & 68\% & 66\% & 70\% & 70\% \\ \hline Recall & 95\% ...
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\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline & $\gamma_{2} =0.25$ & $\gamma_{2} =0.275$ & $ \gamma_{2} =0.30$ & $\gamma_{2} =0.325$ & $ \gamma_{2} =0.35$ & $ \gamma_{2} =0.375$ & $\gamma_{2} =0.40$ & $ \gamma_{2} =0.425$ \\ \hline Precision & 54\% & 55\% & 58\% & 56\% & 60\% & 61\% & 65\% & 69\% \\ \hline Recall & 75\% ...
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\begin{tabular}{|c|c|c|c|c|} \hline Defense method & No defense & Spatial defense only & Temporal defense only & Detection+temporal/spatial defense \\ \hline Accuracy & 26\% & 44\% & 41\% & \textbf{48\%} \\ \hline \end{tabular}
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\begin{tabular}{cccc} \toprule & \multicolumn{2}{c}{\textbf{bio-diseasome}} & \textbf{USCA312} \\ & $D_{avg}$ & mAP & $D_{avg}$ \\ \cmidrule(lr){2-3}\cmidrule(lr){4-4} $\mathbb{E}^{20}$ & 0.0384 & 0.7603 & \textbf{0.0016} \\ $\mathbb{H}^{20}$ & 0.0537 & 0.9194 & 0.0174 \\ $\mathbb{H}^{10} \times \mathbb{E}^{10}$ & 0.02...
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\begin{tabular}{ccccccccccccccc} \toprule & \multicolumn{2}{c}{\textsc{3D Grid}} & \multicolumn{2}{c}{\textsc{4D Grid}} & \multicolumn{2}{c}{\textsc{Tree}} & \multicolumn{2}{c}{\textsc{Tree $\times$ Grid}} & \multicolumn{2}{c}{\textsc{Tree $\times$ Tree}} & \multicolumn{2}{c}{\textsc{Tree $\diamond$ Grids}} & \multicol...
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\begin{tabular}{ccccccccccccccc} \toprule & \multicolumn{2}{c}{\textsc{3D Grid}} & \multicolumn{2}{c}{\textsc{4D Grid}} & \multicolumn{2}{c}{\textsc{Tree}} & \multicolumn{2}{c}{\textsc{Tree $\times$ Grid}} & \multicolumn{2}{c}{\textsc{Tree $\times$ Tree}} & \multicolumn{2}{c}{\textsc{Tree $\diamond$ Grids}} & \multicol...
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\begin{tabular}{cccc} \toprule \multicolumn{1}{l}{} & \multicolumn{2}{c}{\textbf{bio-diseasome}} & \textbf{USCA312} \\ \multicolumn{1}{l}{} & $D_{avg}$ & \multicolumn{1}{l}{mAP} & $D_{avg}$ \\ \cmidrule(lr){2-3}\cmidrule(lr){4-4} $\mathbb{E}^{6}$ & 0.0724 & 0.5492 & \textbf{0.0016} \\ $\mathbb{H}^{6}$ & 0.0583 & 0.8415...
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\begin{tabular}{cccc} \toprule \multicolumn{1}{l}{} & \multicolumn{2}{c}{\textbf{bio-diseasome}} & \textbf{USCA312} \\ \multicolumn{1}{l}{} & $D_{avg}$ & \multicolumn{1}{l}{mAP} & $D_{avg}$ \\ \cmidrule(lr){2-3}\cmidrule(lr){4-4} $\mathbb{E}^{12}$ & 0.0455 & 0.6993 & \textbf{0.0016} \\ $\mathbb{H}^{12}$ & 0.0573 & 0.89...
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\begin{tabular}{c|l|c} \toprule Method & Description & Accuracy\\ \toprule Baseline & Simple feature concatenations.& 0.714\\ \hline DNN & Multiview neural network. & 0.763\\ \hline M-NMF & Joint matrix factorization. & 0.632\\ \hline DCCAE & A deep version of CCA. & 0.716\\ VCCA & A variational version of CCA. & 0.758...
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\begin{tabular}{ccc} \toprule \textbf{Method} & \textbf{Label Propagation} & \textbf{Trainable Label Propagation} \\ \midrule Cora-full & 66.44 ± 0.93 & \textbf{67.23 ± 0.66}\\ Pubmed & 83.45 ± 0.63 & \textbf{83.52 ± 0.59} \\ Arxiv & 67.11 ± 0.00 & \textbf{68.42 ± 0.01} \\ Products & 74.24 ± 0.00 & \textbf{75.61 ± 0.21...
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\begin{tabular}{ccc} \toprule \textbf{Method} & \textbf{C\&S} & \textbf{Trainable C\&S} \\ \midrule House & 0.51 ± 0.01 & \textbf{0.45 ± 0.01} \\ County & 1.42 ± 0.14 & \textbf{1.13 ± 0.09} \\ VK & 7.02 ± 0.20 & \textbf{6.95 ± 0.22} \\ Avazu & \textbf{0.106 ± 0.014} & \textbf{0.106 ± 0.014} \\ \bottomrule \end{tabular}
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\begin{tabular}{cccc} \toprule \textbf{Method} & \textbf{MLP} & \textbf{MLP+C\&S} & \textbf{MLP+Trainable C\&S}\\ \midrule Cora-full & 60.12 ± 0.29 (61.09 ± 0.39) & 66.95 ± 1.46 (68.26 ± 1.24) & \textbf{67.89 ± 1.37} (\textbf{69.09 ± 1.25}) \\ Pubmed & 88.72 ± 0.34 (89.25 ± 0.26) & 89.12 ± 0.27 (89.45 ± 0.17) & \textbf...
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\begin{tabular}{c|c|c|c|c|c|c}{} & \multicolumn{3}{c|}{MIT-BIH dataset} &\multicolumn{3}{c}{Alibaba dataset}\\ {Model} & Precision & Recall & F1-score & Precision & Recall & F1-score \\ Seq2Seq & 87.7\% & 87.7\% & 87.7\% & 33.8\% & 33.8\% & 33.8\% \\ ResNet1D-34 & 97.4\% & 97.4\% & 97.4\% & 85.7\% & 85.7\% & 85.7\% \\ ...
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\begin{tabular}{c|c} Model & F1-score \\ \textbf{SE-ECGNet} & \textbf{89.8\%} \\ SE-ECGNet w/o 2-D Convolution Blocks & 86.6\% \\ SE-ECGNet w/o the SE-Module & 87.4\% \\ SE-ECGNet w/o the Parallel Blocks & 88.7\% \\ ECGNet & 86.2\% \\ \end{tabular}
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\begin{tabular}{cccc} \hline \hline Step & $ \kappa $ & $\#$ nodes left & Clusters recovered \\ \hline 1 & 1 & 1100 & $V_1$ \\ 2 & 1 & 300 & $V_2$\\ 3 & 1 & 100 & $V_3$\\ 4 & 1 & 20 & $V_4$ \\ \hline \hline \end{tabular}
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\begin{tabular}{cccc} \hline\hline Step & $ \kappa $ & $\#$ nodes left & Clusters recovered \\ \hline 1 & 1 & 1100 & $V_1$ \\ 2 & 1 & 300 & $V_2$\\ 3 & 1 & 100 & $V_3$, $V_4$ \\ \hline\hline \end{tabular}
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\begin{tabular}{cccc} \hline\hline Step & $ \rho $ & $\#$ nodes left & Clusters recovered \\ \hline 1 & 0.2 & 1100 & $V_1$ \\ 2 & 0.4 & 300 & $V_2$\\ 3 & 0.95 & 100 & $V_3$, $V_4$ \\ \hline\hline \end{tabular}
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\begin{tabular}{cccc} \hline\hline Step & $ \rho $ & $\#$ nodes left & Clusters recovered \\ \hline 1 & 0.15 & 4500 & $V_1$ \\ 2 & 0.175 & 1300 & $V_2$\\ 3 & 0.2 & 500 & $V_3$, $V_4$ \\ 4 & 0.475 & 100 & $V_5$, $V_6$ \\ \hline\hline \end{tabular}
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\begin{tabular}{lccc} \toprule Data set & Naive & Flexible & Better? \\ \midrule Breast & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\ Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\ Glass2 & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\ Credit & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6& \\ Horse & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\time...
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\begin{tabular}{c|ccc|ccc} \toprule & \multicolumn{3}{c|}{CIFAR-10} & \multicolumn{3}{c}{CIFAR-100} \\ $\%$ mislabeled & 0 & 20 & 40 & 0 & 20 & 40 \\\midrule \textsc{ERM} & $96.3 \pm 0.1$ & $88.5 \pm 0.1$ & $84.4 \pm 0.5$ & $81.6 \pm 0.2$ & $69.6 \pm 0.1$ & $55.7 \pm 0.5$ \\ \textit{mixup} & ${97.0} \pm 0.1$ & $93.9 \p...
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\begin{tabular}{c|c|cc} \toprule Task & \% samples removed ($c$) & \textsc{ERM} & \textsc{ODD} \\\midrule \multirow{3}{*}{CIFAR-50} & 30 & $78.5 \pm 0.1$ & $\textbf{79.0} \pm 0.1$ \\ & 50 & $77.9 \pm 0.1$ & $\textbf{78.6} \pm 0.2$ \\ & 70 & $77.5 \pm 0.1$ & $\textbf{78.1} \pm 0.1$ \\\midrule \multirow{3}{*}{CIFAR-20} &...
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\begin{tabular}{c|ccc} \toprule $\%$ mislabeled & 0 & 20 & 40 \\\midrule \textsc{ERM} & \textbf{78.7} (94.3) & 72.6 (90.2) & 61.2 (84.4) \\ \textsc{Luo} & 76.7 (93.3) & 75.2 (92.3) & 73.2 (91.0) \\ \textsc{MentorNet} & - & - & 65.1 (85.9) \\\midrule \textsc{ODD} ($p=10$) & \textbf{78.7} (94.0) & \textbf{77.5} (93.5) & ...
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\begin{tabular}{c|ccccc|c} \toprule \multirow{2}{*}{\% Mislabeled} & \multicolumn{5}{|c|}{Hyperparameter $p$} & \multirow{2}{*}{Network} \\ & 1 & 10 & 30 & 50 & 80 & \\\midrule $0\%$ & 5.5 & 2.3 & 1.1 & 0.7 & 0.4 & \multirow{3}{*}{ResNet-152} \\ $20\%$ & 23.8 & 20.8 & 19.2 & 17.5 & 0.7 &\\ $40\%$ & 44.1 & 40.2 & 36.2 &...
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\begin{tabular}{cllll} \toprule $p$ & \multicolumn{2}{l}{Webvision} & \multicolumn{2}{l}{ImageNet} \\ \midrule & Top1 & Top5 & Top1 & Top 5\\\midrule 1 & 74.01 & 89.93 & 65.77 & 85.40 \\ 10 & 74.31 & 90.55 & 66.09 & 85.86 \\ 30 & 74.62 & 90.63 & 66.73 & 86.32 \\ 50 & 74.43 & 90.78 & 66.58 & 86.21 \\ 80 & 74.33 & 90.30 ...
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\begin{tabular}{lllll} \hline \#ent. & \#scenario & \#category & \#rel. & \#train \\ \hline 17.37M & 182K & 8.96K & 5.18K & 60.65M \\ \hline\hline \#item & \#value & \#user & \#edge\_click & \#edge\_purchase \\ \hline 9.14M & 8.04M & 482M & 7,952M & 144M \\\hline \end{tabular}
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\begin{tabular}{c|cccc||cccc} \hline & \multicolumn{4}{c||}{Hit@10 (10\%) in LP-Filtered} & \multicolumn{4}{c}{Accuracy (\%) in TC} \\ \hline\hline $n_h$ & 200 & 500 & 800 & & 200 & 500 & 800 & \\ evaluation result & 43.38 & \textbf{43.66} & 43.41 & & \textbf{77.25} & 77.13 & 77.19 & \\ \hline $n_B$ & 100 & 500 & 1000 ...
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\begin{tabular}{llrrr} \toprule & & $N$ & $F$ & $k^*$ \\ type & dataset & & & \\ \midrule artificial& b & 1000 & 10 & 3 \\ & c & 1000 & 10 & 4 \\ & d & 1000 & 10 & 4 \\ & e & 1000 & 10 & 2 \\ benchmark & Iris & 150 & 4 & 3 \\ & Wine & 178 & 13 & 3 \\ & Wine quality & 1599 & 11 & 6 \\ real-world & ACS county & 3142 & 21...
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\begin{tabular}{lllllllllllllll} \cmidrule(lr){1-5} \cmidrule(lr){6-10} \cmidrule(lr){11-15} \multicolumn{5}{c}{Varying Sample size $n$}& \multicolumn{5}{c}{Varying Between-group correlation $\gamma$} & \multicolumn{5}{c}{Varying Within-group correlation $\rho$} \\ \cmidrule(lr){1-5} \cmidrule(lr){6-10} \cmidrule(lr){1...
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\begin{tabular}{lllllllllllllll} \cmidrule(lr){1-5} \cmidrule(lr){6-10} \cmidrule(lr){11-15} \multicolumn{5}{c}{Varying Sample size $n$}& \multicolumn{5}{c}{Varying Between-group correlation $\gamma$} & \multicolumn{5}{c}{Varying Within-group correlation $\rho$} \\ \cmidrule(lr){1-5} \cmidrule(lr){6-10} \cmidrule(lr){1...
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\begin{tabular}{c|c} \hline Method & group-feature selected \\ \hline group-SLOPE & lcavol, lweight, svi, gleason\\ Deep-gKnock & lcavol, lweight \\ \hline \end{tabular}
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\begin{tabular}{c|ccc} \hline Method & $s$ & $q$ & $P(Q\geq q)$ \\ \hline Lasso & $100$ & $21$ & $0.256$\\ group-SLOPE & $41$ & $12$ & $0.04673$\\ Deep-gKnock & $26$ & $11$ & $0.00192$\\ \hline \end{tabular}
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\begin{tabular}{ccccccc} \toprule Dataset & \# Class & Dataset Size & Official Train/Test/Extra Split & Our Data Split(TC/DC/HO) & Model Architecture & Model Acc. On HO set(\%) (A/B/C) \\ \midrule CIFAR-10 & 10 & 60K & 50K/10K/x & 20K/39K/1K & ResNet-18 & 70.1/66.4/68.3 \\ SVHN & 10 & 630K & 73K/26K/531K & 50K/49K/531K...
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\begin{tabular}{|cc|ccccc|} \cline{3-7} \multicolumn{2}{c|}{} & \multicolumn{1}{c}{$\bar{c}_1$} & \multicolumn{1}{c}{$\bar{c}_2$} & \multicolumn{1}{c}{$\bar{c}_3$} & \multicolumn{1}{c}{$\bar{c}_4$} & \multicolumn{1}{c|}{$\bar{C}$} \\ \hline \multirow{2}{*}{$\mathcal{P}_1$}& w/o regularization & 150.13 & 110.94 & 108.41...
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\begin{tabular}{llllllll} \toprule Model & \multicolumn{6}{c}{Pretrained on ImageNet} & \multicolumn{1}{c}{Untrained} \\ \cmidrule(r){2-7} \cmidrule(r){8-8} &Counting & \multicolumn{2}{c}{MNIST} & \multicolumn{2}{c}{CIFAR-10} & \multicolumn{1}{c}{Shuffled MNIST} & \multicolumn{1}{c}{MNIST} \\ \cmidrule(r){3-4} \cmidrul...
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\begin{tabular}{ll} \toprule Model & Accuracy \\ \midrule Inception V3 & $0.78$ \\ Inception V4 & $0.802$ \\ Inception Resnet V2 & $0.804$ \\ Resnet V2 152 & $0.778$ \\ Resnet V2 101 & $0.77$ \\ Resnet V2 50 & $0.756$ \\ Inception V3 adv. & $0.776$ \\ \bottomrule \end{tabular}
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\begin{tabular}{llllllll} \toprule ImageNet Model & $\lambda$ & batch & GPUS & learn rate & decay & epochs/decay & steps \\ \midrule Inception V3 & $0.01$ & $50$ & $4$ & $0.05$ & $0.96$ & $2$ & $100000$ \\ Inception V4 & $0.01$ & $50$ & $4$ & $0.05$ & $0.96$ & $2$ & $100000$ \\ Inception Resnet V2 & $0.01$ & $50$ & $4$...
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\begin{tabular}{llllllll} \toprule ImageNet Model & $\lambda$ & batch & GPUS & learn rate & decay & epochs/decay & steps \\ \midrule Inception V3 & $0.05$ & $100$ & $4$ & $0.05$ & $0.96$ & $2$ & $60000$ \\ Inception V4 & $0.05$ & $100$ & $4$ & $0.05$ & $0.96$ & $2$ & $60000$ \\ Inception Resnet V2 & $0.05$ & $50$ & $8$...
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\begin{tabular}{llllllll} \toprule ImageNet Model & $\lambda$ & batch & GPUS & learn rate & decay & epochs/decay & steps \\ \midrule Inception V3 & $0.01$ & $50$ & $6$ & $0.05$ & $0.99$ & $4$ & $300000$ \\ Inception V4 & $0.01$ & $50$ & $6$ & $0.05$ & $0.99$ & $4$ & $300000$ \\ Inception Resnet V2 & $0.01$ & $50$ & $6$...
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\begin{tabular}{llllllll} \toprule Random Model & $\lambda$ & batch & GPUS & learn rate & decay & epochs/decay & steps \\ \midrule Inception V3 & $0.01$ & $50$ & $4$ & $0.05$ & $0.96$ & $2$ & $100000$ \\ Inception V4 & $0.01$ & $50$ & $4$ & $0.05$ & $0.96$ & $2$ & $100000$ \\ Inception Resnet V2 & $0.01$ & $50$ & $4$ &...
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\begin{tabular}{llccccccc} \toprule \cmidrule(l){1-2} \cmidrule(l){3-4} \cmidrule(l){5-8} \cmidrule(l){9-9} $\cal X$ & $\cal B$ & \textit{Const} & \textit{NMF} & \textit{AM} & \textit{LMM} & \textit{NES} & \textit{LMM+NES} & \textit{Supervised} \\ \midrule $0$-$4$ & $5$-$9$ & 10.6/0.65 & 16.5/0.71 & 17.8/0.83 & 15.1/0....
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\begin{tabular}{llcccccccc} \toprule \cmidrule(l){1-2} \cmidrule(l){3-4} \cmidrule(l){5-9} \cmidrule(l){10-10} $\cal X$ & $\cal B$ & \textit{Const} & \textit{NMF} & \textit{AM} & \textit{LMM} & \textit{NES} & \textit{AM+NES} & \textit{LMM+NES} & \textit{Supervised} \\ \midrule Vocals & Instrumental & 0.0 & 0.0 & 0.0 & ...
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\begin{tabular}{|l|c|c|} \hline Worker Preference \% & MUG & NIR \\ \hline\hline MoCoGAN / RMoCoGAN & 40.9 / \textbf{59.1} & 45.2 / \textbf{54.8} \\ MoCoGAN / RJGAN & 32.8 / \textbf{67.2} & 42.7 / \textbf{57.3}\\ \hline \end{tabular}
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\begin{tabular}{|l|c|c|c|} \hline Dataset & MoCoGAN & RMoCoGAN & RJGAN\\ \hline\hline MUG & 134.8 & 104.4 & \textbf{99.9} \\ NIR & 125.7 & 118.7& \textbf{105.5} \\ \hline \end{tabular}
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\begin{tabular}{|l|c|c|c|} \hline & MoCoGAN & RJGAN & RJGAN \\&&&with pre-training\\ \hline\hline FVD after &&& \\1K iterations & 445.3 & 438.7 & \textbf{199.6} \\ \hline \end{tabular}
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\begin{tabular}{|l|c|c|} \hline Dataset & Baseline & RJGAN \\ \hline\hline MUG & 4.24 $\pm$ 0.02 & \textbf{4.50 $\pm$ 0.01} \\ \hline \end{tabular}
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\begin{tabular}{cl} \toprule Notation & Definition \\ \midrule $W^{(d)}_t$ & global deep model at round $t$ \\ $W^{(s)}_t$ & global shallow model at round $t$ \\ $W_t^{(c)}$ & shallow model kept in client $c$ at round $t$ \\ $W_t^{(m)}$ & deep model kept in meditor $m$ at round $t$ \\ $\mathcal{U}$ & all the clients \\...
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\begin{tabular}{ccccccc} \toprule Dataset & Clients & Mediators & $\eta$ & classes per client & $\mathcal{I}$ & $\mathcal{L}$\\ \midrule CIFAR10 & 100 & 3 & 0.015 & 3 & 10 & 1\\ \midrule FMNIST & 100 & 3 & 0.015 & 2 & 10 & 1\\ \bottomrule \end{tabular}
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\begin{tabular}{lrr} \hline sex & \# of examples & \% of examples \\ \hline Female & 267 & 26.7\% \\ Male & 733 & 73.3\% \\ \hline \end{tabular}
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\begin{tabular}{|l|c|c|} \hline & Accuracy changes (\%) & Score changes (\%) \\ \hline \hline LIME & 15.04 & 26.98 \\ OLLIE & 15.47 & 23.52 \\ \textbf{OnML} & \textbf{25.52} & \textbf{33.48} \\ \hline \end{tabular}
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\begin{tabular}{|c|ccc|} \hline & \multicolumn{3}{|c|}{ Permuted MNIST ($K=20$) } \\ Method & ACC (\%) & BT (\%) & FA (\%) \\ \hline Fine tune & 46.8 $\pm$ 0.6 & \textbf{-14.8 $\pm$ 1.1} & -0.3 $\pm$ 0.8 \\ MetaCL, w/o \emph{reg} & \textbf{48.0 $\pm$ 0.8} & -16.8 $\pm$ 0.8 & \textbf{3.3 $\pm$ 0.8} \\ \hline MAS & 55.7 ...
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\begin{tabular}{|c|ccc|} \hline & \multicolumn{3}{|c|}{ CIFAR-100 ($K=20$) } \\ Method & ACC (\%) & BT (\%) & FA (\%) \\ \hline Fine tune & 22.9 $\pm$ 0.8 & -14.0 $\pm$ 0.7 & 4.1 $\pm$ 0.8 \\ MetaCL, w/o \emph{reg} & \textbf{27.5 $\pm$ 1.4} & \textbf{-11.7 $\pm$ 1.3} & \textbf{7.0 $\pm$ 0.6} \\ \hline MAS & 34.2 $\pm$ ...
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\begin{tabular}{|c|ccc|} \hline & \multicolumn{3}{|c|}{ CUB ($K=10$) } \\ Method & ACC (\%) & BT (\%) & FA (\%) \\ \hline Fune tine & 9.8 $\pm$ 0.8 & \textbf{-32.1 $\pm$ 0.9} & -15.3 $\pm$ 0.7 \\ MetaCL, w/o \emph{reg} & \textbf{11.4 $\pm$ 0.4} & -36.2 $\pm$ 0.7 & \textbf{-9.1 $\pm$ 0.6} \\ \hline MAS & 26.4 $\pm$ 1.0 ...
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\begin{tabular}{cccccc} \hline Operation & Kernel & Stride & Filters & Dropout & Nonlin. \\ \hline 3x32x32 input & & & & & \\ Conv & 3 $\times$ 3 & $1\times 1$ & 32 & & ReLU \\ Conv & 3 $\times$ 3 & $1\times 1$ & 32 & & ReLU \\ MaxPool & & $2\times 2$ & & 0.5 & \\ Conv & 3 $\times$ 3 & $1\times 1$ & 64 & & ReLU \\ Conv...
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\begin{tabular}{c|ccc} \hline Overview & Perm. MNIST & CIFAR & CUB \\ \hline Num. of tasks & 20 & 10 & 10 \\ Input size & $1\times 28 \times 28$ & $3 \times 32 \times 32$ & $ 3\times 224 \times 224$ \\ Evaluation protocol & single-head & multi-head & multi-head \\ Num. of classes per tasks & 10 & 10 & 20 \\ Num. of ori...
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\begin{tabular}{cccccccccc} \hline \multirow{2}{*}{Methods} & \multicolumn{3}{c}{Cora} & \multicolumn{3}{c}{Citeseer} & \multicolumn{3}{c}{Pubmed} \\ \cline{2-10} & ACC & NMI & ARI & ACC & NMI & ARI & ACC & NMI & ARI \\ \hline \multicolumn{10}{c}{Supervised} \\ \hline GCN & 68.3 & 52.3 & 50.9 & 68.8 & 41.9 & 43.1 & 69....