5 citations · 5 across the 9 of their papers we have counts for
9 papers
Learning from Local Walks on Dynamic Graphs with Bandit Feedback
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study stochastic multi-armed bandits on dynamic graphs, where arms correspond to the vertices of a network with time-varying edges. In this setting, the learner is restricted to…
Data Attribution in Adaptive Learning
Amit Kiran Rege
Machine learning models increasingly generate their own training data -- online bandits, reinforcement learning, and post-training pipelines for language models are leading example…
The Role of Generator Access in Autoregressive Post-Training
Amit Kiran Rege
We study how generator access constrains autoregressive post-training. The central question is whether the learner is confined to fresh root-start rollouts or can return to previou…
Flickering Multi-Armed Bandits
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We introduce Flickering Multi-Armed Bandits (FMAB) to model sequential decision-making in environments with changing action availability, where accessibility of the next action is…
A Unified Framework for Locality in Scalable MARL
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph. This works only when the system is value-l…
Multi-Agent Lipschitz Bandits
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward. Our objective is to…
Incentivized Lipschitz Bandits
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study incentivized exploration in multi-armed bandit (MAB) settings with infinitely many arms modeled as elements in continuous metric spaces. Unlike classical bandit models, we…
Where Did Your Model Learn That? Label-free Influence for Self-supervised Learning
Nidhin Harilal, Amit Kiran Rege, Reza Akbarian Bafghi +2
Self-supervised learning (SSL) has revolutionized learning from large-scale unlabeled datasets, yet the intrinsic relationship between pretraining data and the learned representati…
Hamiltonian Learning using Machine Learning Models Trained with Continuous Measurements
Kris Tucker, Amit Kiran Rege, Conor Smith +2
We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the…