A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New algorithms improve on bandit feedback in matrix games with unknown payoff matrices.
Unified model for prediction and deferral selects top-k entities efficiently.
Unified framework for deferring queries to top-k experts, improving accuracy-cost trade-offs.
In this work, we introduce the {\em average top-} (\atk) loss as a new aggregate loss for supervised learning, which is the average over the largest individual losses over a training dataset. We show that the \atk loss is a natural generalization of the two widely used aggregate losses, namely the average loss a…
We study realizable continual linear regression under random task orderings, a common setting for developing continual learning theory. In this setup, the worst-case expected loss after learning iterations admits a lower bound of . However, prior work using an unregularized scheme has only established an up…
The current trend of pushing CNNs deeper with convolutions has created a pressing demand to achieve higher compression gains on CNNs where convolutions dominate the computation and parameter amount (e.g., GoogLeNet, ResNet and Wide ResNet). Further, the high energy consumption of convolutions limits its deployment on m…