Zobrazeno 1 - 10
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pro vyhledávání: '"Li, Michael"'
In 1992, Bitar and Goles introduced the parallel chip-firing game on undirected graphs. Two years later, Prisner extended the game to directed graphs. While the properties of parallel chip-firing games on undirected graphs have been extensively studi
Externí odkaz:
http://arxiv.org/abs/2407.15889
Autor:
Chopra, Tanush, Li, Michael
We propose a framework for evaluating strategic deception in large language models (LLMs). In this framework, an LLM acts as a game master in two scenarios: one with random game mechanics and another where it can choose between random or deliberate a
Externí odkaz:
http://arxiv.org/abs/2407.00948
We define an online learning and optimization problem with irreversible decisions contributing toward a coverage target. At each period, a decision-maker selects facilities to open, receives information on the success of each one, and updates a machi
Externí odkaz:
http://arxiv.org/abs/2406.14777
Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture aspects of the syntax and semantics of language. But what {\em should} e
Externí odkaz:
http://arxiv.org/abs/2406.03707
Autor:
Li, Michael
In this paper, we investigate the eigenvalues of the Laplacian matrix of the "graph of graphs", in which cubic graphs of order n are joined together using Whitehead moves. Our work follows recent results from arXiv:2303.13923 , which discovered a sig
Externí odkaz:
http://arxiv.org/abs/2405.14592
Autor:
Li, Michael Lingzhi, Imai, Kosuke
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today'
Externí odkaz:
http://arxiv.org/abs/2404.17019
We introduce the "cram" method, a general and efficient approach to simultaneous learning and evaluation using a generic machine learning (ML) algorithm. In a single pass of batched data, the proposed method repeatedly trains an ML algorithm and test
Externí odkaz:
http://arxiv.org/abs/2403.07031
Statistical model discovery is a challenging search over a vast space of models subject to domain-specific constraints. Efficiently searching over this space requires expertise in modeling and the problem domain. Motivated by the domain knowledge and
Externí odkaz:
http://arxiv.org/abs/2402.17879
Predictive contagion models are ubiquitous in epidemiology, social sciences, engineering, and management. This paper formulates a prescriptive contagion analytics model where a decision-maker allocates shared resources across multiple segments of a p
Externí odkaz:
http://arxiv.org/abs/2310.14559
Autor:
Li, Michael Lingzhi, Imai, Kosuke
Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals who are likely to benefit from a treatment the most (``exceptional responders'') or those who are harmed by it. A common a
Externí odkaz:
http://arxiv.org/abs/2310.07973