9 citations · 20 across the 9 of their papers we have counts for
11 papers
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat
Shantanu Ghosh, Ke Yu, Forough Arabshahi +1
ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models…
Tackling Shortcut Learning in Deep Neural Networks: An Iterative Approach with Interpretable Models
Shantanu Ghosh, Ke Yu, Forough Arabshahi +1
We use concept-based interpretable models to mitigate shortcut learning. Existing methods lack interpretability. Beginning with a Blackbox, we iteratively carve out a mixture of in…
Augmentation by Counterfactual Explanation -- Fixing an Overconfident Classifier
Sumedha Singla, Nihal Murali, Forough Arabshahi +2
A highly accurate but overconfident model is ill-suited for deployment in critical applications such as healthcare and autonomous driving. The classification outcome should reflect…
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Pooja Sethi, Denis Savenkov, Forough Arabshahi +6
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work…
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules
Forough Arabshahi, Jennifer Lee, Antoine Bosselut +2
One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common se…
Conversational Neuro-Symbolic Commonsense Reasoning
Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki +3
In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated pre…
Compositional Generalization with Tree Stack Memory Units
Forough Arabshahi, Zhichu Lu, Pranay Mundra +2
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain. Standard neural networks fail to a large extent…
Look-up and Adapt: A One-shot Semantic Parser
Zhichu Lu, Forough Arabshahi, Igor Labutov +1
Computing devices have recently become capable of interacting with their end users via natural language. However, they can only operate within a limited "supported" domain of disco…
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
Forough Arabshahi, Sameer Singh, Animashree Anandkumar
Neural programming involves training neural networks to learn programs, mathematics, or logic from data. Previous works have failed to achieve good generalization performance, espe…
Spectral Methods for Correlated Topic Models
Forough Arabshahi, Animashree Anandkumar
In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the li…
Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]
Forough Arabshahi, Furong Huang, Animashree Anandkumar +2
Scalable probabilistic modeling and prediction in high dimensional multivariate time-series is a challenging problem, particularly for systems with hidden sources of dependence and…