2 citations · 7 across the 12 of their papers we have counts for
12 papers
Training and Evaluating Diffusion Policies with Long Context Lengths
Abhinav Agarwal, Adam Wei, Taylan Kargin +6
Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations. Policies trained with these methods, however, typically condition robot actions on only…
Distributionally Robust K-Means Clustering
Vikrant Malik, Taylan Kargin, Babak Hassibi
K-means clustering is a workhorse of unsupervised learning, but it is notoriously brittle to outliers, distribution shifts, and limited sample sizes. Viewing k-means as Lloyd--Max…
Optimal Infinite-Horizon Mixed Control
Vikrant Malik, Taylan Kargin, Joudi Hajar +1
We study the problem of mixed control in the infinite-horizon setting. We identify the optimal causal controller that minimizes the …
The Distributionally Robust Infinite-Horizon LQR
Joudi Hajar, Taylan Kargin, Vikrant Malik +1
We explore the infinite-horizon Distributionally Robust (DR) linear-quadratic control. While the probability distribution of disturbances is unknown and potentially correlated over…
Distributionally Robust Kalman Filtering over Finite and Infinite Horizon
Taylan Kargin, Joudi Hajar, Vikrant Malik +1
This paper investigates the distributionally robust filtering of signals generated by state-space models driven by exogenous disturbances with noisy observations in finite and infi…
Infinite-Horizon Distributionally Robust Regret-Optimal Control
Taylan Kargin, Joudi Hajar, Vikrant Malik +1
We study the infinite-horizon distributionally robust (DR) control of linear systems with quadratic costs, where disturbances have unknown, possibly time-correlated distribution wi…
A Distributionally Robust Approach to Shannon Limits using the Wasserstein Distance
Vikrant Malik, Taylan Kargin, Victoria Kostina +1
We consider the rate-distortion function for lossy source compression, as well as the channel capacity for error correction, through the lens of distributional robustness. We assum…
Wasserstein Distributionally Robust Regret-Optimal Control in the Infinite-Horizon
Taylan Kargin, Joudi Hajar, Vikrant Malik +1
We investigate the Distributionally Robust Regret-Optimal (DR-RO) control of discrete-time linear dynamical systems with quadratic cost over an infinite horizon. Regret is the diff…
Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
Yingying Li, Jing Yu, Lauren Conger +2
This paper studies uncertainty set estimation for unknown linear systems. Uncertainty sets are crucial for the quality of robust control since they directly influence the conservat…
Wasserstein Distributionally Robust Regret-Optimal Control under Partial Observability
Joudi Hajar, Taylan Kargin, Babak Hassibi
This paper presents a framework for Wasserstein distributionally robust (DR) regret-optimal (RO) control in the context of partially observable systems. DR-RO control considers the…
Stochastic Mirror Descent in Average Ensemble Models
Taylan Kargin, Fariborz Salehi, Babak Hassibi
The stochastic mirror descent (SMD) algorithm is a general class of training algorithms, which includes the celebrated stochastic gradient descent (SGD), as a special case. It util…
Thompson Sampling Achieves Regret in Linear Quadratic Control
Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli +2
Thompson Sampling (TS) is an efficient method for decision-making under uncertainty, where an action is sampled from a carefully prescribed distribution which is updated based on t…