9 papers
Allocate Marginal Reviews to Borderline Papers Using LLM Comparative Ranking
Elliot L. Epstein, Rajat Dwaraknath, John Winnicki +1
This paper argues that large ML conferences should allocate marginal review capacity primarily to papers near the acceptance boundary, rather than spreading extra reviews via rando…
FlashSketch: Sketch-Kernel Co-Design for Fast Sparse Sketching on GPUs
Rajat Vadiraj Dwaraknath, Sungyoon Kim, Mert Pilanci
Sparse sketches such as the sparse Johnson-Lindenstrauss transform are a core primitive in randomized numerical linear algebra because they leverage random sparsity to reduce the a…
Sampling on Metric Graphs
Rajat Vadiraj Dwaraknath, Lexing Ying
Metric graphs are structures obtained by associating edges in a standard graph with segments of the real line and gluing these segments at the vertices of the graph. The resulting…
LLMs are Overconfident: Evaluating Confidence Interval Calibration with FermiEval
Elliot L. Epstein, John Winnicki, Thanawat Sornwanee +1
Large language models (LLMs) excel at numerical estimation but struggle to correctly quantify uncertainty. We study how well LLMs construct confidence intervals around their own an…
Variational inference and density estimation with non-negative tensor train
Xun Tang, Rajat Dwaraknath, Lexing Ying
This work proposes an efficient numerical approach for compressing a high-dimensional discrete distribution function into a non-negative tensor train (NTT) format. The two settings…
MatRL: Provably Generalizable Iterative Algorithm Discovery via Monte-Carlo Tree Search
Sungyoon Kim, Rajat Vadiraj Dwaraknath, Longling geng +1
Iterative methods for computing matrix functions have been extensively studied and their convergence speed can be significantly improved with the right tuning of parameters and by…