355 citations · 730 across the 16 of their papers we have counts for
5 papers · 1 filter
Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data
Mahboobe Jadid, Melika Rezaye Garkani, Ali Mousavi
Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference…
Hamiltonian Adaptive Importance Sampling
Ali Mousavi, Reza Monsefi, Víctor Elvira
Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated…
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Andrew Bennett, Nathan Kallus, Lihong Li +1
Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings where experimentation is limited, such as education and healthcare. But, in these very sam…
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
Ali Mousavi, Lihong Li, Qiang Liu +1
Off-policy estimation for long-horizon problems is important in many real-life applications such as healthcare and robotics, where high-fidelity simulators may not be available and…
A Deep Learning Approach to Structured Signal Recovery
Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk
In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse rep…