Public articles linked to the same research event.
arXiv In a unified prequential (test-then-train) protocol across real and synthetic streams, this study systematically compares tabular foundation models (TFMs) with streaming learners and streaming AutoML, finding that TFMs reach the highest predictive accuracy, recover faster after drift, and stay most accurate under label delay, but cost more than an order of magnitude more to serve; memory size and backbone quality matter more than the memory management policy, and repeated context encoding is the dominant cost, which a compact causal model with cached encoded context can hold nearly constant across memory sizes at close accuracy.
In a unified prequential (test-then-train) protocol across real and synthetic streams, this study systematically compares tabular foundation models (TFMs) with streaming learners and streaming AutoML, finding that TFMs reach the highest predictive accuracy, recover faster after drift, and stay most accurate under label delay, but cost more than an order of magnitude more to serve; memory size and backbone quality matter more than the memory management policy, and repeated context encoding is the dominant cost, which a compact causal model with cached encoded context can hold nearly constant across memory sizes at close accuracy.
In a unified prequential (test-then-train) protocol across real and synthetic streams, this study systematically compares tabular foundation models (TFMs) with streaming learners and streaming AutoML, finding that TFMs reach the highest predictive accuracy, recover faster after drift, and stay most accurate under label delay, but cost more than an order of magnitude more to serve; memory size and backbone quality matter more than the memory management policy, and repeated context encoding is the dominant cost, which a compact causal model with cached encoded context can hold nearly constant across memory sizes at close accuracy.
In a unified prequential (test-then-train) protocol across real and synthetic streams, this study systematically compares tabular foundation models (TFMs) with streaming learners and streaming AutoML, finding that TFMs reach the highest predictive accuracy, recover faster after drift, and stay most accurate under label delay, but cost more than an order of magnitude more to serve; memory size and backbone quality matter more than the memory management policy, and repeated context encoding is the dominant cost, which a compact causal model with cached encoded context can hold nearly constant across memory sizes at close accuracy.