158 citations · 280 across the 27 of their papers we have counts for
28 papers
Non-asymptotic System Identification for Linear Systems with Nonlinear Policies
Yingying Li, Tianpeng Zhang, Subhro Das +2
This paper considers a single-trajectory system identification problem for linear systems under general nonlinear and/or time-varying policies with i.i.d. random excitation noises.…
Reliable Gradient-free and Likelihood-free Prompt Tuning
Maohao Shen, Soumya Ghosh, Prasanna Sattigeri +3
Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs. Fine-tuning such models to downstream tasks is challenging b…
Label-Free Concept Bottleneck Models
Tuomas Oikarinen, Subhro Das, Lam M. Nguyen +1
Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts. However…
Variance-reduced Clipping for Non-convex Optimization
Amirhossein Reisizadeh, Haochuan Li, Subhro Das +1
Gradient clipping is a standard training technique used in deep learning applications such as large-scale language modeling to mitigate exploding gradients. Recent experimental stu…
Group Fairness with Uncertainty in Sensitive Attributes
Abhin Shah, Maohao Shen, Jongha Jon Ryu +4
Learning a fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. A common approach to learn such a model involves solvi…
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Wang Zhang, Tsui-Wei Weng, Subhro Das +3
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws. This pape…