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Found 10 papers, 1 papers with code
Are Bounded Contracts Learnable and Approximately Optimal?
This paper considers the hidden-action model of the principal-agent problem, in which a principal incentivizes an agent to work on a project using a contract.
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants
Despite the emergence of numerous methods aimed at addressing this challenge, they are not fully identified to the structure that two observed variables are influenced by one latent variable and there might be a directed edge in between.
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
In light of the power of the closed-form solution to OICA corresponding to the One-Latent-Component structure, we formulate a way to estimate the mixing matrix using the higher-order cumulants, and further propose the testable One-Latent-Component condition to identify the latent variables and determine causal orders.
Efficient All-reduce for Distributed DNN Training in Optical Interconnect System
Our results also show that WRHT can reduce the communication time of all-reduce operation by 92. 42% and 91. 31% compared to two existing all-reduce algorithms running in the electrical interconnect system.
Adversarial Deep Learning for Online Resource Allocation
Inspired by adversarial training from Generative Adversarial Net (GAN) and the fact that competitive ratio of an online algorithm is based on worst-case input, we adopt deep neural networks to learn an online algorithm for a resource allocation and pricing problem from scratch, with the goal that the performance gap between offline optimum and the learned online algorithm can be minimized for worst-case input.
Accelerating Fully Connected Neural Network on Optical Network-on-Chip (ONoC)
Compared with ENoC, simulation results show that under batch sizes of 64 and 128, on average ONoC can achieve 21. 02% and 12. 95% on reducing training time with 47. 85% and 39. 27% on saving energy, respectively.
Edge-Weighted Online Bipartite Matching
Online bipartite matching and its variants are among the most fundamental problems in the online algorithms literature.
Data Structures and Algorithms Computer Science and Game Theory
Generalizing Complex Hypotheses on Product Distributions: Auctions, Prophet Inequalities, and Pandora's Problem
This paper explores a theory of generalization for learning problems on product distributions, complementing the existing learning theories in the sense that it does not rely on any complexity measures of the hypothesis classes.
Multi-scale Online Learning and its Applications to Online Auctions
For the online posted pricing problem, we show regret bounds that scale with the best fixed price, rather than the range of the values.
Budget Constraints in Prediction Markets
We give a detailed characterization of optimal trades under budget constraints in a prediction market with a cost-function-based automated market maker.
