4 citations · 8 across the 4 of their papers we have counts for
7 papers
Causally Inspired Regularization Enables Domain General Representations
Olawale Salaudeen, Sanmi Koyejo
Given a causal graph representing the data-generating process shared across different domains/distributions, enforcing sufficient graph-implied conditional independencies can ident…
Proxy Methods for Domain Adaptation
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen +5
We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the…
Goodness-of-Fit of Attributed Probabilistic Graph Generative Models
Pablo Robles-Granda, Katherine Tsai, Oluwasanmi Koyejo
Probabilistic generative models of graphs are important tools that enable representation and sampling. Many recent works have created probabilistic models of graphs that are capabl…
No Bidding, No Regret: Pairwise-Feedback Mechanisms for Digital Goods and Data Auctions
Zachary Robertson, Oluwasanmi Koyejo
The growing demand for data and AI-generated digital goods, such as personalized written content and artwork, necessitates effective pricing and feedback mechanisms that account fo…
Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle
Rylan Schaeffer, Mikail Khona, Zachary Robertson +5
Double descent is a surprising phenomenon in machine learning, in which as the number of model parameters grows relative to the number of data, test error drops as models grow ever…
Preference Completion from Partial Rankings
Suriya Gunasekar, Oluwasanmi Koyejo, Joydeep Ghosh
We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over…