1 citations · 1 across the 10 of their papers we have counts for
12 papers
Algorithmic Principles For Multiclass Learning Are Hard To Come By: Limits of Regularization and Proper Learning
Julian Asilis, Shaddin Dughmi, Vatsal Sharan +3
Two of the most fundamental questions in statistical learning theory are the following: which prediction problems are learnable, and how should they be learned? For the former, ele…
When Clean Data Hurts: Learning with Monotone Corruptions Beyond Binary Classification
Julian Asilis, Shaddin Dughmi, Chirag Pabbaraju
Optimal learners are tailored to exploit the i.i.d.\ data assumption underlying the classic PAC model. What if an i.i.d.\ training sample were corrupted with correctly labeled exam…
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.…
On Agnostic PAC Learning in the Small Error Regime
Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas
Binary classification in the classic PAC model exhibits a curious phenomenon: Empirical Risk Minimization (ERM) learners are suboptimal in the realizable case yet optimal in the ag…
Understanding Aggregations of Proper Learners in Multiclass Classification
Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas
Multiclass learnability is known to exhibit a properness barrier: there are learnable classes which cannot be learned by any proper learner. Binary classification faces no such bar…
Transductive Learning Is Compact
Julian Asilis, Siddartha Devic, Shaddin Dughmi +2
We demonstrate a compactness result holding broadly across supervised learning with a general class of loss functions: Any hypothesis class is learnable with transductive sampl…
Regularization and Optimal Multiclass Learning
Julian Asilis, Siddartha Devic, Shaddin Dughmi +2
The quintessential learning algorithm of empirical risk minimization (ERM) is known to fail in various settings for which uniform convergence does not characterize learning. It is…