arXiv research

CR-UOT improves matching of heterogeneous single-cell omics profiles.

New method optimizes costly functions with unknown costs and budget constraints.

Optimal benchmark design varies based on costs in financial manipulation.

CyBeR-0 optimizes federated learning with Byzantine resilience and reduced communication costs.

This paper studies the equilibrium price of an asset that is traded in continuous time between N agents who have heterogeneous beliefs about the state process underlying the asset's payoff. We propose a tractable model where agents maximize expected returns under quadratic costs on inventories and trading rates. The un…

New protocol reduces communication costs for heterogeneous bandits over complex networks.

LC-FL uses generative models to reduce communication costs in federated learning.

Bayesian design improves accuracy without extra cost.

Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically unsupervised. To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network…

Study how transaction costs impact stock returns and holdings in equilibrium.

We propose a continuous-time model of trading with heterogeneous beliefs. Risk-neutral agents face quadratic costs-of-carry on positions and thus their marginal valuations decrease with the size of their position, as it would be the case for risk-averse agents. In the equilibrium models of heterogeneous beliefs that fo…

We study risk-sharing economies where heterogenous agents trade subject to quadratic transaction costs. The corresponding equilibrium asset prices and trading strategies are characterised by a system of nonlinear, fully-coupled forward-backward stochastic differential equations. We show that a unique solution generally…

Paper analyzes D-SGD convergence with heterogeneous data and proposes topology learning.

We introduce reinforcement learning for heterogeneous teams in which rewards for an agent are additively factored into local costs, stimuli unique to each agent, and global rewards, those shared by all agents in the domain. Motivating domains include coordination of varied robotic platforms, which incur different costs…

Bayesian model for cost-effectiveness analysis with subgroup discovery.

Study on-chain peak shaving to reduce Ethereum transaction costs.

New bucketing scheme improves Byzantine robustness for heterogeneous data.

Paper tackles federated learning with privacy, enhancing target data analysis.

Investment and consumption strategies with luxury goods for retirement age.

Recent work has developed Bayesian methods for the automatic statistical analysis and description of single time series as well as of homogeneous sets of time series data. We extend prior work to create an interpretable kernel embedding for heterogeneous time series. Our method adds practically no computational cost co…

This research tackles sample complexity in causal graph recovery with temporal heterogeneity.

Optimizes user marketing campaigns to balance cost and effectiveness.

Study on federated learning with private label sets, showing privacy benefits without significant accuracy loss.

We study how trading costs are reflected in equilibrium returns. To this end, we develop a tractable continuous-time risk-sharing model, where heterogeneous mean-variance investors trade subject to a quadratic transaction cost. The corresponding equilibrium is characterized as the unique solution of a system of coupled…

Multitask Gaussian process regression reduces data generation costs for molecular property prediction.

Proposes a method for private aggregation in heterogeneous federated learning.

Private anchors affect how information is communicated and can improve or distort transmission.

DFFL tackles federated learning with heterogeneous objectives and constraints.

We propose a mean field game model to study the question of how centralization of reward and computational power occur in Bitcoin-like cryptocurrencies. Miners compete against each other for mining rewards by increasing their computational power. This leads to a novel mean field game of jump intensity control, which we…

Neural Index Policy for multi-action bandits with heterogeneous budgets.

A new method reduces communication costs in distributed learning.

We study the system of heterogeneous interbank lending and borrowing based on the relative average of log-capitalization given by the linear combination of the average within groups and the ensemble average and describe the evolution of log-capitalization by a system of coupled diffusions. The model incorporates a game…

Paper extends FFT-based differential privacy method to heterogeneous compositions.

Paper tackles best arm identification with cost consideration.

End-to-end framework learns LLM routing from observational data.

Study shows informed traders harm market makers but price discovery benefits outweigh costs.

Model liquidity premia using a risk-sharing economy with quadratic costs.

Study shows pooling scores for conformal prediction distorts group coverage.

Revisits behavioral finance option pricing model to align with rational asset pricing theory.

MetaDVFS uses device and application metadata to improve DVFS efficiency.

New method reduces labeler costs by aggregating predictions from local classifiers.

Proposes a new Lasso method with performance constraints.

Federated UCBVI reduces communication costs while minimizing regret in multi-agent settings.

We study consumption behaviour in systems with heterogeneous interacting agents. Two different models are introduced, respectively with long and short range interactions among agents. At any time step an agent decides whether or not to consume a good, doing so if this provides positive utility. Utility is affected by i…

Unified analysis of federated learning with compression for various data distributions.

FedDuA adapts global learning rate for federated learning.

RelaySum improves decentralized deep learning by uniformly distributing data across workers.

New algorithms tackle statistical heterogeneity in federated learning.