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+or+fairness.+In+this+work%252C+we+introduce+CuTS%252C+the+first+customizable+synthetic+tabular+data+generation+framework.+Customization+in+CuTS+is+achieved+via+declarative+statistical+and+logical+expressions%252C+supporting+a+wide+range+of+requirements+(e.g.%252C+DP+or+fairness%252C+among+others).+To+ensure+high+synthetic+data+quality+in+the+presence+of+custom+specifications%252C+CuTS+is+pre-trained+on+the+original+dataset+and+fine-tuned+on+a+differentiable+loss+automatically+derived+from+the+provided+specifications+using+novel+relaxations.+We+evaluate+CuTS+over+four+datasets+and+on+numerous+custom+specifications%252C+outperforming+state-of-the-art+specialized+approaches+on+several+tasks+while+being+more+general.+In+particular%252C+at+the+same+fairness+level%252C+we+achieve+2.3%2525+higher+downstream+accuracy+than+the+state-of-the-art+in+fair+synthetic+data+generation+on+the+Adult+dataset.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.64bb4824b894975cb232e5342dba73f7%3frik%3d2PfeQPFNTXlm6A%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Tabular+Data+Generation&simid=608006180143449286&FORM=IRPRST&ck=6F261553E066EF79434F2972786AFB98&selectedIndex=18&itb=0)
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