Subhro Das | DeepAI

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∙ 06/17/2023

Non-asymptotic System Identification for Linear Systems with Nonlinear Policies

This paper considers a single-trajectory system identification problem f...

0 Yingying Li, et al. ∙

∙ 04/30/2023

Reliable Gradient-free and Likelihood-free Prompt Tuning

Due to privacy or commercial constraints, large pre-trained language mod...

0 Maohao Shen, et al. ∙

∙ 04/12/2023

Label-Free Concept Bottleneck Models

Concept bottleneck models (CBM) are a popular way of creating more inter...

0 Tuomas Oikarinen, et al. ∙

∙ 03/02/2023

Variance-reduced Clipping for Non-convex Optimization

Gradient clipping is a standard training technique used in deep learning...

0 Amirhossein Reisizadeh, et al. ∙

∙ 02/16/2023

Group Fairness with Uncertainty in Sensitive Attributes

We consider learning a fair predictive model when sensitive attributes a...

0 Abhin Shah, et al. ∙

∙ 02/11/2023

ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction

Deep neural networks (DNN) have shown great capacity of modeling a dynam...

0 Wang Zhang, et al. ∙

∙ 01/15/2023

Who Should Predict? Exact Algorithms For Learning to Defer to Humans

Automated AI classifiers should be able to defer the prediction to a hum...

0 Hussein Mozannar, et al. ∙

∙ 12/14/2022

Post-hoc Uncertainty Learning using a Dirichlet Meta-Model

It is known that neural networks have the problem of being over-confiden...

0 Maohao Shen, et al. ∙

∙ 05/18/2022

Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics

Rapid technological innovation threatens to leave much of the global wor...

0 Maysa M. Garcia de Macedo, et al. ∙

∙ 04/14/2022

An alternative approach for distributed parameter estimation under Gaussian settings

This paper takes a different approach for the distributed linear paramet...

0 Subhro Das, et al. ∙

∙ 03/07/2022

On observability and optimal gain design for distributed linear filtering and prediction

This paper presents a new approach to distributed linear filtering and p...

0 Subhro Das, et al. ∙

∙ 02/07/2022

Evaluating Robustness of Cooperative MARL: A Model-based Approach

In recent years, a proliferation of methods were developed for cooperati...

7 Nhan H. Pham, et al. ∙

∙ 10/31/2021

Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guarantees

We study the adaptive control of an unknown linear system with a quadrat...

0 Yingying Li, et al. ∙

∙ 10/28/2021

Selective Regression Under Fairness Criteria

Selective regression allows abstention from prediction if the confidence...

9 Abhin Shah, et al. ∙

∙ 10/10/2020

Online Optimal Control with Affine Constraints

This paper considers online optimal control with affine constraints on t...

0 Yingying Li, et al. ∙

∙ 07/02/2020

Verifiably Safe Exploration for End-to-End Reinforcement Learning

Deploying deep reinforcement learning in safety-critical settings requir...

22 Nathan Hunt, et al. ∙

∙ 06/23/2020

A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm

Out of the recent advances in systems and control (S&C)-based analysis o...

26 Orlando Romero, et al. ∙

∙ 06/18/2020

GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models

Generative adversarial networks (GANs) learn the distribution of observe...

35 Farzan Farnia, et al. ∙

∙ 06/15/2020

Formal Verification of End-to-End Learning in Cyber-Physical Systems: Progress and Challenges

Autonomous systems – such as self-driving cars, autonomous drones, and a...

0 Nathan Fulton, et al. ∙

∙ 06/08/2020

Stochastic Optimization with Non-stationary Noise

We investigate stochastic optimization problems under relaxed assumption...

0 Jingzhao Zhang, et al. ∙

∙ 02/10/2020

Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift

Most predictive models assume that training and test data are generated ...

0 Subhro Das, et al. ∙

∙ 01/28/2020

Learning Occupational Task-Shares Dynamics for the Future of Work

The recent wave of AI and automation has been argued to differ from prev...

0 Subhro Das, et al. ∙

∙ 06/19/2019

Learning Patient Engagement in Care Management: Performance vs. Interpretability

The health outcomes of high-need patients can be substantially influence...

0 Subhro Das, et al. ∙

∙ 05/08/2019

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of...

11 Chirag Nagpal, et al. ∙

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