10 papers
Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
Vikram Krishnamurthy, Luke Snow
Inverse reinforcement learning (IRL) recovers the loss function of a forward learner from its observed responses. Adaptive IRL aims to reconstruct the loss function of a forward le…
Efficient Counterfactual Estimation of Conditional Greeks via Malliavin-based Weak Derivatives
Vikram Krishnamurthy, Luke Snow
We study counterfactual gradient estimation of conditional loss functionals of diffusion processes. In quantitative finance, these gradients are known as conditional Greeks: the se…
Emergence of Structural Disparities in the Web of Scientific Citations
Buddhika Nettasinghe, Nazanin Alipourfard, Vikram Krishnamurthy +1
Scientific attention is unevenly distributed, creating inequities in recognition and distorting access to opportunities. Using citations as a proxy, we quantify disparities in atte…
Why Most Optimism Bandit Algorithms Have the Same Regret Analysis: A Simple Unifying Theorem
Vikram Krishnamurthy
Several optimism-based stochastic bandit algorithms -- including UCB, UCB-V, linear UCB, and finite-arm GP-UCB -- achieve logarithmic regret using proofs that, despite superficial…
Data-Driven Mechanism Design using Multi-Agent Revealed Preferences
Luke Snow, Vikram Krishnamurthy
We study a sequence of independent one-shot non-cooperative games where agents play equilibria determined by a tunable mechanism. Observing only equilibrium decisions, without para…
Efficient Neural SDE Training using Wiener-Space Cubature
Luke Snow, Vikram Krishnamurthy
A neural stochastic differential equation (SDE) is an SDE with drift and diffusion terms parametrized by neural networks. The training procedure for neural SDEs consists of optimiz…