25 citations · 31 across the 7 of their papers we have counts for
8 papers
Online Allocation using Few Samples
Matthew Faw, Sahil Singla, Yifan Wang
We study online allocation problems where requests over resources arrive in an adversarial order and must be served immediately and irrevocably. This framework captures bot…
On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback
Matthew Faw, Constantine Caramanis, Jessica Hoffmann
Unconscious bias has been shown to influence how we assess our peers, with consequences for hiring, promotions and admissions. In this work, we focus on affinity bias, the componen…
In-Context Fine-Tuning for Time-Series Foundation Models
Abhimanyu Das, Matthew Faw, Rajat Sen +1
Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for of a time-series foundati…
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD
Matthew Faw, Litu Rout, Constantine Caramanis +1
This work considers the problem of finding a first-order stationary point of a non-convex function with potentially unbounded smoothness constant using a stochastic gradient oracle…
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
Matthew Faw, Isidoros Tziotis, Constantine Caramanis +3
We study convergence rates of AdaGrad-Norm as an exemplar of adaptive stochastic gradient methods (SGD), where the step sizes change based on observed stochastic gradients, for min…
Single-Sample Prophet Inequalities via Greedy-Ordered Selection
Constantine Caramanis, Paul Dütting, Matthew Faw +6
We study single-sample prophet inequalities (SSPIs), i.e., prophet inequalities where only a single sample from each prior distribution is available. Besides a direct, and optimal,…
Single-Sample Prophet Inequalities Revisited
Constantine Caramanis, Matthew Faw, Orestis Papadigenopoulos +1
The study of the prophet inequality problem in the limited information regime was initiated by Azar et al. [SODA'14] in the pursuit of prior-independent posted-price mechanisms. As…
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
Matthew Faw, Rajat Sen, Karthikeyan Shanmugam +2
We consider a covariate shift problem where one has access to several different training datasets for the same learning problem and a small validation set which possibly differs fr…