5 citations · 5 across the 9 of their papers we have counts for
10 papers
Scaling Point-in-Time Language Models
Bryan Kelly, Semyon Malamud, Johannes Schwab +1
Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests a…
Limits To (Machine) Learning
Zhimin Chen, Bryan Kelly, Semyon Malamud
Machine learning (ML) methods are highly flexible, but their ability to approximate the true data-generating process is fundamentally constrained by finite samples. We characterize…
Training NTK to Generalize with KARE
Johannes Schwab, Bryan Kelly, Semyon Malamud +1
The performance of the data-dependent neural tangent kernel (NTK; Jacot et al. (2018)) associated with a trained deep neural network (DNN) often matches or exceeds that of the full…
Large and Deep Factor Models
Bryan Kelly, Boris Kuznetsov, Semyon Malamud +1
We show that a deep neural network (DNN) trained to construct a stochastic discount factor (SDF) admits an additive decomposition separating nonlinear characteristic discovery from…
A Simple Algorithm For Scaling Up Kernel Methods
Teng Andrea Xu, Bryan Kelly, Semyon Malamud
The recent discovery of the equivalence between infinitely wide neural networks (NNs) in the lazy training regime and Neural Tangent Kernels (NTKs) (Jacot et al., 2018) has revived…
Deep Regression Ensembles
Antoine Didisheim, Bryan Kelly, Semyon Malamud
We introduce a methodology for designing and training deep neural networks (DNN) that we call "Deep Regression Ensembles" (DRE). It bridges the gap between DNN and two-layer neural…