6 papers
Steady-state Based Approach to Online Non-stochastic Control
Vijeth Hebbar, Spencer Hutchinson, Mahnoosh Alizadeh +1
We study the problem of online non-stochastic control (ONC), which is the control of a linear system under adversarial disturbances and adversarial cost functions, with the aim of…
"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)
Noel Brindise, Vijeth Hebbar, Riya Shah +1
In this work, we provide an extended discussion of a new approach to explainable Reinforcement Learning called Diverse Near-Optimal Alternatives (DNA), first proposed at L4DC 2025.…
Revisiting Regret Benchmarks in Online Non-Stochastic Control
Vijeth Hebbar, Cédric Langbort
In the online non-stochastic control problem, an agent sequentially selects control inputs for a linear dynamical system when facing unknown and adversarially selected convex costs…
Responding to Promises: No-regret learning against followers with memory
Vijeth Hebbar, Cédric Langbort
We consider a repeated Stackelberg game setup where the leader faces a sequence of followers of unknown types and must learn what commitments to make. While previous works have con…
Online Decision Making with History-Average Dependent Costs (Extended)
Vijeth Hebbar, Cedric Langbort
In many online sequential decision-making scenarios, a learner's choices affect not just their current costs but also the future ones. In this work, we look at one particular case…
On The Role of Social Identity in the Market for (Mis)information
Vijeth Hebbar, Cedric Langbort
Motivated by recent works in the communication and psychology literature, we model and study the role social identity -- a person's sense of belonging to a group -- plays in human…