7 papers
Scoring Is Not Enough: Addressing Gaps in Utility-fairness Trade-offs for Ranking
Shubham Singh, Ian A. Kash, Mesrob I. Ohannessian
Scoring functions are used to represent the relevance of individual documents. In modern information retrieval or recommendation systems, they are often learned from data and play…
VAMPS: Visual-Assisted Mathematical Problem Solving Benchmark
Amirhossein Dabiriaghdam, Shayan Vassef, Mohammadreza Bakhtiari +5
Multimodal large language models are increasingly capable of complex reasoning, yet their performance often degrades when they must externalize a problem through a tool and then re…
Overfitting and Generalizing with (PAC) Bayesian Prediction in Noisy Binary Classification
Xiaohan Zhu, Mesrob I. Ohannessian, Nathan Srebro
We consider a PAC-Bayes type learning rule for binary classification, balancing the training error of a randomized ''posterior'' predictor with its KL divergence to a pre-specified…
Linearization Explains Fine-Tuning in Large Language Models
Zahra Rahimi Afzal, Tara Esmaeilbeig, Mojtaba Soltanalian +1
Parameter-Efficient Fine-Tuning (PEFT) is a popular class of techniques that strive to adapt large models in a scalable and resource-efficient manner. Yet, the mechanisms underlyin…
Induced Model Matching: Restricted Models Help Train Full-Featured Models
Usama Muneeb, Mesrob I. Ohannessian
We consider scenarios where a very accurate (often small) predictive model using restricted features is available when training a full-featured (often larger) model. This restricte…
Tight Bounds on the Binomial CDF, and the Minimum of i.i.d Binomials, in terms of KL-Divergence
Xiaohan Zhu, Mesrob I. Ohannessian, Nathan Srebro
We provide finite sample upper and lower bounds on the Binomial tail probability which are a direct application of Sanov's theorem. We then use these to obtain high probability upp…