7 papers
When Can Safe Controllers Adapt? Information before Commitment
Venkatesh Saligrama
Safe adaptive control is online adaptation under a safety guarantee on the learning trajectory itself. The controller may use any causal, history-dependent rule and act differently…
Data Deletion Can Help in Adaptive RL
Param Budhraja, Aditya Gangrade, Alex Olshevsky +1
Deploying reinforcement learning policies in the real world requires adapting to time-varying environments. We study this problem in the contextual Markov Decision Process (cMDP) f…
Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification
Patrick Lutz, Themistoklis Haris, Arjun Chandra +2
Transformers can perform in-context classification from a few labeled examples, yet the inference-time algorithm remains opaque. We study multi-class linear classification in the h…
Linear Transformers Implicitly Discover Unified Numerical Algorithms
Patrick Lutz, Aditya Gangrade, Hadi Daneshmand +1
We train a linear attention transformer on millions of masked-block matrix completion tasks: each prompt is masked low-rank matrix whose missing block may be (i) a scalar predictio…
Constrained Linear Thompson Sampling
Aditya Gangrade, Venkatesh Saligrama
We study safe linear bandits (SLBs), where an agent selects actions from a convex set to maximize an unknown linear objective subject to unknown linear constraints in each round. E…
Deep Companion Learning: Enhancing Generalization Through Historical Consistency
Ruizhao Zhu, Venkatesh Saligrama
We propose Deep Companion Learning (DCL), a novel training method for Deep Neural Networks (DNNs) that enhances generalization by penalizing inconsistent model predictions compared…
Testing the Feasibility of Linear Programs with Bandit Feedback
Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama +1
While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem…