8 papers
Semi-Random Graphs, Robust Asymmetry, and Reconstruction
Julian Asilis, Xi Chen, Dutch Hansen +1
The Graph Reconstruction Conjecture famously posits that any undirected graph on at least three vertices is determined up to isomorphism by its family of (unlabeled) induced subgra…
Resa: Transparent Reasoning Models via SAEs
Shangshang Wang, Julian Asilis, Ömer Faruk Akgül +4
How cost-effectively can we elicit strong reasoning in language models by leveraging their underlying representations? We answer this question with Resa, a family of 1.5B reasoning…
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
Woody Haosheng Gan, Deqing Fu, Julian Asilis +5
Steering methods have emerged as effective and targeted tools for guiding large language models' (LLMs) behavior without modifying their parameters. Multimodal large language model…
Tina: Tiny Reasoning Models via LoRA
Shangshang Wang, Julian Asilis, Ömer Faruk Akgül +3
How cost-effectively can strong reasoning abilities be achieved in language models? Driven by this fundamental question, we present Tina, a family of tiny reasoning models achieved…
Local Regularizers Are Not Transductive Learners
Sky Jafar, Julian Asilis, Shaddin Dughmi
We partly resolve an open question raised by Asilis et al. (COLT 2024): whether the algorithmic template of local regularization -- an intriguing generalization of explicit regular…
Proper Learnability and the Role of Unlabeled Data
Julian Asilis, Siddartha Devic, Shaddin Dughmi +2
Proper learning refers to the setting in which learners must emit predictors in the underlying hypothesis class , and often leads to learners with simple algorithmic forms (e.g.…