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Found 16 papers, 3 papers with code
A Nonparametric Stochastic Set Model: Identification, Optimization, and Prediction
Our model assumes bounded rationality of consumers, which results in symmetric demand cannibalization and intriguingly enables full identification.
Information Design in Allocation with Costly Verification
Agents learn private information (signals) from an information designer about the allocation payoff to the principal.
Implementation with Uncertain Evidence
We study a full implementation problem with hard evidence where the state is common knowledge but agents face uncertainty about the evidence endowments of other agents.
Maskin Meets Abreu and Matsushima
The theory of full implementation has been criticized for using integer/modulo games which admit no equilibrium (Jackson (1992)).
Direct Implementation with Evidence
In a richer model of evidence due to <cite>KT2012</cite>, we establish pure-strategy implementation with two or more agents in a direct revelation mechanism.
The Wisdom of the Crowd and Higher-Order Beliefs
The classic wisdom-of-the-crowd problem asks how a principal can "aggregate" information about the unknown state of the world from agents without understanding the information structure among them.
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions
In this paper, we propose a framework called TrustMAE to address the problem of product defect classification.
Investigating Ground-level Ozone Formation: A Case Study in Taiwan
In this paper, we aim to identify and understand the impact of various factors on O3 formation and predict the O3 concentrations under different pollution-reduced and climate change scenarios.
Information Design in Optimal Auctions
We also show that in an ex ante symmetric setting, an asymmetric information structure is never seller-worst but can generate a strictly higher surplus for the buyers than the symmetric buyer-optimal information structure.
Decision Forest: A Nonparametric Approach to Modeling Irrational Choice
In this model, each customer type is associated with a binary decision tree, which represents a decision process for making a purchase based on checking for the existence of specific products in the assortment.
Self-view Grounding Given a Narrated 360° Video
The negative log reconstruction loss of the reverse sentence (referred to as "irrelevant loss") is jointly minimized to encourage the reverse sentence to be different from the given sentence.
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes
The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such decision problems.
Learning Discrete Bayesian Networks from Continuous Data
Learning Bayesian networks from raw data can help provide insights into the relationships between variables.
