24 citations · 118 across the 50 of their papers we have counts for
13 papers · 1 filter
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
Alex Christopher Stutts, Danilo Erricolo, Theja Tulabandhula +1
We present a novel statistical approach to incorporating uncertainty awareness in model-free distributional reinforcement learning involving quantile regression-based deep Q networ…
Conformalized Multimodal Uncertainty Regression and Reasoning
Domenico Parente, Nastaran Darabi, Alex C. Stutts +2
This paper introduces a lightweight uncertainty estimator capable of predicting multimodal (disjoint) uncertainty bounds by integrating conformal prediction with a deep-learning re…
A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit
Priyank Agrawal, Theja Tulabandhula, Vashist Avadhanula
In this paper, we consider the contextual variant of the MNL-Bandit problem. More specifically, we consider a dynamic set optimization problem, where a decision-maker offers a subs…
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect
Priyank Agrawal, Theja Tulabandhula
We study the effect of persistence of engagement on learning in a stochastic multi-armed bandit setting. In advertising and recommendation systems, repetition effect includes a wea…
Incentivising Exploration and Recommendations for Contextual Bandits with Payments
Priyank Agrawal, Theja Tulabandhula
We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users. By using payments to incentivize thes…
Thompson Sampling for a Fatigue-aware Online Recommendation System
Yunjuan Wang, Theja Tulabandhula
In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period. The users respond by selecting one of…
Bandits with Temporal Stochastic Constraints
Priyank Agrawal, Theja Tulabandhula
We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it. Impairment effect is the phenomena where an agent only accrues reward for a…
Efficient Reinforcement Learning via Initial Pure Exploration
Sudeep Raja Putta, Theja Tulabandhula
In several realistic situations, an interactive learning agent can practice and refine its strategy before going on to be evaluated. For instance, consider a student preparing for…
Provable Inductive Robust PCA via Iterative Hard Thresholding
U. N. Niranjan, Arun Rajkumar, Theja Tulabandhula
The robust PCA problem, wherein, given an input data matrix that is the superposition of a low-rank matrix and a sparse matrix, we aim to separate out the low-rank and sparse compo…
Learning to Partition using Score Based Compatibilities
Arun Rajkumar, Koyel Mukherjee, Theja Tulabandhula
We study the problem of learning to partition users into groups, where one must learn the compatibilities between the users to achieve optimal groupings. We define four natural obj…
Faster Reinforcement Learning Using Active Simulators
Vikas Jain, Theja Tulabandhula
In this work, we propose several online methods to build a \emph{learning curriculum} from a given set of target-task-specific training tasks in order to speed up reinforcement lea…
Learning Personalized Optimal Control for Repeatedly Operated Systems
Theja Tulabandhula
We consider the problem of online learning of optimal control for repeatedly operated systems in the presence of parametric uncertainty. During each round of operation, environment…
Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes
K J Prabuchandran, Tejas Bodas, Theja Tulabandhula
A recent goal in the Reinforcement Learning (RL) framework is to choose a sequence of actions or a policy to maximize the reward collected or minimize the regret incurred in a fini…