Jerry Li

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms

2018 · arXiv (Cornell University)

We study the problem of robustly learning multi-dimensional histograms. A $d$-dimensional function $h: D \rightarrow \mathbb{R}$ is called a $k$-histogram if there exists a partition of the domain $D \subseteq \mathbb{R}^d$ into $k$ axis-aligned rectangles …

Learning Structured Distributions From Untrusted Batches: Faster and Simpler

2020 · arXiv (Cornell University)

We revisit the problem of learning from untrusted batches introduced by Qiao and Valiant [QV17]. Recently, Jain and Orlitsky [JO19] gave a simple semidefinite programming approach based on the cut-norm that achieves essentially information-theoretically optimal …

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

2020 · arXiv (Cornell University)

Offline methods for reinforcement learning have a potential to help bridge the gap between reinforcement learning research and real-world applications. They make it possible to learn policies from offline datasets, thus overcoming concerns associated with …

Robust Estimation Under Heterogeneous Corruption Rates

2025

We study the problem of robust estimation under heterogeneous corruption rates, where each sample may be independently corrupted with a known but non-identical probability. This setting arises naturally in distributed and federated learning, crowdsourcing, and …

Automatic Prompt Optimization with “Gradient Descent” and Beam Search

2023

Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution …