Zobrazeno 1 - 10
of 576
pro vyhledávání: '"Shah, Devavrat"'
We introduce a novel framework for human-AI collaboration in prediction and decision tasks. Our approach leverages human judgment to distinguish inputs which are algorithmically indistinguishable, or "look the same" to any feasible predictive algorit
Externí odkaz:
http://arxiv.org/abs/2410.08783
We study a class of structured Markov Decision Processes (MDPs) known as Exo-MDPs. They are characterized by a partition of the state space into two components: the exogenous states evolve stochastically in a manner not affected by the agent's action
Externí odkaz:
http://arxiv.org/abs/2409.14557
The framework of decision-making, modeled as a Markov Decision Process (MDP), typically assumes a single objective. However, most practical scenarios involve considering tradeoffs between multiple objectives. With that as the motivation, we consider
Externí odkaz:
http://arxiv.org/abs/2408.04488
Autor:
Shen, Dennis, Agarwal, Anish, Misra, Vishal, Schelter, Bjoern, Shah, Devavrat, Shiells, Helen, Wischik, Claude
The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic nearest neighbors (SNN) estimator. At its core, SNN leverages informati
Externí odkaz:
http://arxiv.org/abs/2405.20088
Autor:
Han, Jessy Xinyi, Miller, Andrew, Watkins, S. Craig, Winship, Christopher, Christia, Fotini, Shah, Devavrat
We are interested in developing a data-driven method to evaluate race-induced biases in law enforcement systems. While the recent works have addressed this question in the context of police-civilian interactions using police stop data, they have two
Externí odkaz:
http://arxiv.org/abs/2402.14959
We introduce a novel framework for incorporating human expertise into algorithmic predictions. Our approach focuses on the use of human judgment to distinguish inputs which `look the same' to any feasible predictive algorithm. We argue that this fram
Externí odkaz:
http://arxiv.org/abs/2402.00793
Autor:
Song, Bowen, Paolieri, Marco, Stewart, Harper E., Golubchik, Leana, McNitt-Gray, Jill L., Misra, Vishal, Shah, Devavrat
Objective: Our aim is to determine if data collected with inertial measurement units (IMUs) during steady-state running could be used to estimate ground reaction forces (GRFs) and to derive biomechanical variables (e.g., contact time, impulse, change
Externí odkaz:
http://arxiv.org/abs/2311.02287
We consider the classical problem of learning, with arbitrary accuracy, the natural parameters of a $k$-parameter truncated \textit{minimal} exponential family from i.i.d. samples in a computationally and statistically efficient manner. We focus on t
Externí odkaz:
http://arxiv.org/abs/2309.06413
We consider a variant of matrix completion where entries are revealed in a biased manner, adopting a model akin to that introduced by Ma and Chen. Instead of treating this observation bias as a disadvantage, as is typically the case, the goal is to e
Externí odkaz:
http://arxiv.org/abs/2306.04775
High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained human experts. A common source of concern about automation in these settings is that experts may exercise intuition that is difficult to model and/or have access to i
Externí odkaz:
http://arxiv.org/abs/2306.01646