2 citations · 3 across the 12 of their papers we have counts for
15 papers
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…
Compositional Reasoning in Language Models under Reinforcement Learning Post-Training
Yu He, Yingxi Li, Yifei Wang +1
Compositional reasoning is critical for real-world problem solving: since training data is necessarily limited, models must generalize by composing learned skills in new ways. Whil…
FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving
Henry Robbins, Connor Lawless, Madeleine Udell +1
Mixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications. A central challenge is designing computationall…
Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking
Nikil Roashan Selvam, Jay Baxter, Sophie Hilgard +4
Crowdsourced fact-checking systems have been adopted by major social media companies such as X, Meta, TikTok and Google with the aim of combating misleading information at scale wi…
Smoothed Analysis of Online Metric Matching with a Single Sample: Beyond Metric Distortion
Yingxi Li, Ellen Vitercik, Mingwei Yang
In the online metric matching problem, servers and requests lie in a metric space. Servers are available upfront, and requests arrive sequentially. An arriving request must…
Subsampling Graphs with GNN Performance Guarantees
Mika Sarkin Jain, Stefanie Jegelka, Ishani Karmarkar +2
How can we subsample graph data so that a graph neural network (GNN) trained on the subsample achieves performance comparable to training on the full dataset? This question is of f…