Capacity-Constrained Online Convex Optimization with Delayed Feedback
problem Online learning with delayed feedback under a hard capacity constraint
method Reduction to a delayed and weighted OCO problem using a scheduler
result First regret guarantees for capacity-constrained OCO under convex and strongly convex losses
Study capacity constraints in continual learning with a simple model.
problem Understanding optimal resource allocation for agents with limited memory and compute resources.
method Analyzes a capacity-constrained linear-quadratic-Gaussian (LQG) sequential prediction problem and demonstrates optimal capacity allocation strategies.
result Derives a solution to the capacity-constrained LQG sequential prediction problem and shows how to optimally allocate capacity across sub-problems in the steady state.
Study optimal treatment assignment policies under strategic agent responses.
problem Learning optimal treatment policies with strategic agents complicates estimation.
method Dynamic model with threshold convergence to mean-field equilibrium, consistent estimator for policy gradient.
result Threshold for treatment assignment converges to mean-field equilibrium threshold under large but finite number of agents.
The paper tackles imbalanced classification under operational constraints, proposing a framework to maximize sensitivity.
problem Detecting minority class observations under severe class imbalance and operational constraints.
method Formal classification framework under capacity constraints, maximizing sensitivity while respecting a user-defined label limit.
result The optimal classifier under capacity constraints is equivalent to the Bayes classifier with reweighted prior probabilities.
Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measuremen…
In this paper we propose a novel index to quantify and measure the flow of information on macro and micro scales. We discuss the implications of this index for knowledge management fields and also as intellectual capital that can thus be utilized by entrepreneurs. We explore different function and human oriented metric…
Matched filters reveal optimal normalization methods for different market participants.
problem Optimizing signal extraction from order flow for market microstructure analysis.
method General matched filter principle applied to normalization strategies.
result Optimal normalization methods (e.g., SMC and STV) differ based on trader types. Modeling European spot power markets with game theory for Nash equilibria.
problem Optimizing electricity markets with risk-averse players and constraints.
method Game-theoretic framework with Jacobi and Gauss-Seidel schemes for approximate Nash equilibria.
result Innovative risk aversion model reduces price dimensionality and ensures boundedness.
This paper extends financial theory to measure learnable market structure under computational constraints.
problem Understanding learnable market structure under bounded computational capacity.
method Introduces financial epiplexity as a measure of learnable market structure, extending classical information theory.
result Proves that equal entropy does not imply equal epiplexity and derives thresholds for useful regimes.
Financial markets are not random, but hard to predict due to hidden causes and strategic use.
problem Hard to predict financial markets
method Disciplined thesis on the distinction between no-arbitrage, informational efficiency, and net exploitability
result Discovers that markets are hard to predict due to hidden causes and strategic use
Study online learning with delays and capacity constraints, achieving optimal regret bounds.
problem Online learning with delays and capacity constraints.
method Novel scheduling and preemptive techniques, matching upper and lower bounds.
result Achieves optimal regret bounds across all capacity levels.
This paper improves few-shot learning by reducing sample complexity using representation learning.
problem Reducing sample complexity for target tasks with limited data.
method Representation learning to pool all source task samples for target task learning.
result Representation learning can achieve substantial sample size reduction, bypassing the $Ω( rac{1}{T})$ barrier.