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Found 8 papers, 0 papers with code
Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
We consider robust empirical risk minimization (ERM), where model parameters are chosen to minimize the worst-case empirical loss when each data point varies over a given convex uncertainty set.
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
Motivated by this, a significant line of work has formalized settings where an action's loss is a function of the number of times that action was recently played in the prior $m$ timesteps, where $m$ corresponds to a bound on human memory capacity.
Complete Policy Regret Bounds for Tallying Bandits
Policy regret is a well established notion of measuring the performance of an online learning algorithm against an adaptive adversary.
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Reinforcement learning (RL) is empirically successful in complex nonlinear Markov decision processes (MDPs) with continuous state spaces.
When Is Generalizable Reinforcement Learning Tractable?
Agents trained by reinforcement learning (RL) often fail to generalize beyond the environment they were trained in, even when presented with new scenarios that seem similar to the training environment.
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
We focus on characterizing the convergence rate of these methods when applied to linear-quadratic systems, and study various settings of driving noise and reward feedback.
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
We apply this update to a variety of POMDP solvers and find that it enables us to scale CIRL to non-trivial problems, with larger reward parameter spaces, and larger action spaces for both robot and human.
Pragmatic-Pedagogic Value Alignment
In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users' objectives as they go.
