228 citations · 813 across the 67 of their papers we have counts for
75 papers
From Data to Program: Fast & Direct Generative Program Inference from Empirical Data
Simon Klüttermann, Xueying Ding, Leman Akoglu
Estimating probability densities from a finite set of samples typically requires dataset-specific model fitting. We introduce PRODiGI, a pretrained data-to-program model that infer…
When Less Compute Is More: Adaptive Early Exit Improves Pretrained Outlier Detection
Tianyang Zhou, Leman Akoglu
Pretrained tabular foundation models process every dataset at a fixed depth, with inference costs growing with dataset size. To address this, we present the first study of depth-ad…
TANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures
Orkun Irsoy, Leman Akoglu, Osman Yagan
Networked systems, from power grids to traffic networks and cloud clusters, carry loads across nodes with limited capacity. A node whose load exceeds its capacity fails and sheds i…
Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
Tianyang Zhou, Wenbo Chen, Pierre Jinghong Liang +1
LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and la…
Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection
Vijeta Deshpande, Tootiya Giyahchi, Veena Padmanabhan +2
Safety detection models require examples of HHH (Helpful, Harmless, Honest)-violating outputs for robust generalization, however such examples are scarce. Activation Steering (AS)…
Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning
Xueying Ding, Leman Akoglu
Training foundation models is computationally intensive and often slow to converge. We introduce PIQL,Privileged Information for Quick and Quality Learning, the first framework to…