activity
20192026
most citedDomain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve

14 citations · 32 across the 28 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.CL2022★ 1 cited

LEATHER: A Framework for Learning to Generate Human-like Text in Dialogue

Anthony Sicilia, Malihe Alikhani

Algorithms for text-generation in dialogue can be misguided. For example, in task-oriented settings, reinforcement learning that optimizes only task-success can lead to abysmal lex…

cs.CL2022

Modeling Non-Cooperative Dialogue: Theoretical and Empirical Insights

Anthony Sicilia, Tristan Maidment, Pat Healy +1

Investigating cooperativity of interlocutors is central in studying pragmatics of dialogue. Models of conversation that only assume cooperative agents fail to explain the dynamics…

cs.LG2022★ 3 cited

PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners

Anthony Sicilia, Katherine Atwell, Malihe Alikhani +1

Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking…

cs.CV2022★ 4 cited

Test-time Fourier Style Calibration for Domain Generalization

Xingchen Zhao, Chang Liu, Anthony Sicilia +2

The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods…

cs.CL2022

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error

Katherine Atwell, Anthony Sicilia, Seong Jae Hwang +1

Discourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level. The current performance of discourse models is very low on texts outside…