22 citations · 71 across the 12 of their papers we have counts for
16 papers
Peak-Aware One-Way Trading: Competitive and Learning-Augmented Algorithms for Energy Procurement
Jackson Bibbens, Adam Lechowicz, Nicolas Christianson +2
We introduce peak-aware one-way trading (), an online problem in which a player must purchase a fixed quantity of an asset by a deadline, minimizing its total cost…
DCEmbed: Scalable Optimization over Neural Surrogates
Akshay Sreekumar, Nicolas Christianson, Priya L. Donti +2
Neural surrogates can accelerate large-scale optimization by replacing expensive or intractable model components with efficient learned approximations, but solving the resulting em…
Multi-Scale Datacenter Power Modulation
Akshay Sreekumar, Nicolas Christianson, Fiodar Kazhamiaka +1
Cloud datacenters must increasingly modulate power in response to time-varying grid and infrastructure constraints. We study this problem as finite-horizon control of a networked h…
Online Smoothed Demand Management
Adam Lechowicz, Nicolas Christianson, Mohammad Hajiesmaili +2
We introduce and study a class of online problems called online smoothed demand management , motivated by paradigm shifts in grid integration and energy storage fo…
Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
Christopher Yeh, Nicolas Christianson, Adam Wierman +1
While deep learning models often achieve high predictive accuracy, their predictions typically do not come with any provable guarantees on risk or reliability, which are critical f…
End-to-End Conformal Calibration for Optimization Under Uncertainty
Christopher Yeh, Nicolas Christianson, Alan Wu +2
Machine learning can significantly improve performance for decision-making under uncertainty across a wide range of domains. However, ensuring robustness guarantees requires well-c…
Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study spatiotemporal online allocation with deadline constraints (), a new online problem motivated by emerging challenges in sustainability and ene…
Risk-Sensitive Online Algorithms
Nicolas Christianson, Bo Sun, Steven Low +1
We study the design of risk-sensitive online algorithms, in which risk measures are used in the competitive analysis of randomized online algorithms. We introduce the CVaR-comp…
Chasing Convex Functions with Long-term Constraints
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study a family of online metric problems with long-term constraints. In these problems, an online player makes decisions in a metric space t…
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
Adam Lechowicz, Nicolas Christianson, Bo Sun +4
We introduce and study online conversion with switching costs, a family of online problems that capture emerging problems at the intersection of energy and sustainability. In this…
Online Algorithms with Uncertainty-Quantified Predictions
Bo Sun, Jerry Huang, Nicolas Christianson +3
The burgeoning field of algorithms with predictions studies the problem of using possibly imperfect machine learning predictions to improve online algorithm performance. While near…
The Online Pause and Resume Problem: Optimal Algorithms and An Application to Carbon-Aware Load Shifting
Adam Lechowicz, Nicolas Christianson, Jinhang Zuo +4
We introduce and study the online pause and resume problem. In this problem, a player attempts to find the lowest (alternatively, highest) prices in a sequence of fixed length…
Chasing Convex Bodies and Functions with Black-Box Advice
Nicolas Christianson, Tinashe Handina, Adam Wierman
We consider the problem of convex function chasing with black-box advice, where an online decision-maker aims to minimize the total cost of making and switching between decisions i…
Smoothed Online Optimization with Unreliable Predictions
Daan Rutten, Nico Christianson, Debankur Mukherjee +1
We examine the problem of smoothed online optimization, where a decision maker must sequentially choose points in a normed vector space to minimize the sum of per-round, non-convex…
Optimizing the Human Learnability of Abstract Network Representations
William Qian, Christopher W. Lynn, Andrei A. Klishin +3
Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning h…
Architecture and evolution of semantic networks in mathematics texts
Nicolas H. Christianson, Ann Sizemore Blevins, Danielle S. Bassett
Knowledge is a network of interconnected concepts. Yet, precisely how the topological structure of knowledge constrains its acquisition remains unknown, hampering the development o…