1 citations · 1 across the 13 of their papers we have counts for
15 papers
L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents
Carson Eisenach, Robert Joseph George, Vincent Quenneville-Belair +2
The lasting gains in AI have come from methods that absorb more computation, and language models already scale computation within a call. Useful work, though, happens across calls:…
Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation
Angel Wang, Dominique Perrault-Joncas, Alvaro Maggiar +2
Deploying a new decision policy creates a cold-start problem for prediction models whose targets depend on the policy's actions: historical observations reflect earlier policies, w…
Proximal Residual Value Functions for Consistent Planning and Real-Time Execution
Harrison Waldon, Carson Eisenach, Akhil Bagaria +4
We study two-timescale decision systems in which a planning layer periodically supplies a continuation-value function to a real-time optimizer that allocates arriving resources, wi…
Towards Optimal Estimators for Randomized Control Trials
Harsh Parikh, Gabriel Levin-Konigsberg, Nilesh Tripuraneni +5
Randomized controlled trials (RCTs) are fundamental tools for causal inference across technology companies, pharmaceutical research, and federal agencies. While the standard differ…
Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems
Angel Wang, Dominique Perrault-Joncas, Alvaro Maggiar +2
In large-scale multi-agent systems with shared resource constraints, an upstream planner must iteratively evaluate candidate resource plans -- assessing feasibility, aggregate resp…
BRIDGE: Building Representations In Domain Guided Program Synthesis
Robert Joseph George, Carson Eisenach, Udaya Ghai +3
Large language models can generate plausible code, but remain brittle for formal verification in proof assistants such as Lean. A central scalability challenge is that verified syn…
Structure-Informed Deep Reinforcement Learning for Inventory Management
Alvaro Maggiar, Sohrab Andaz, Akhil Bagaria +4
This paper investigates the application of Deep Reinforcement Learning (DRL) to classical inventory management problems, with a focus on practical implementation considerations. We…
Outbound Modeling for Inventory Management
Riccardo Savorgnan, Udaya Ghai, Carson Eisenach +1
We study the problem of forecasting the number of units fulfilled (or ``drained'') from each inventory warehouse to meet customer demand, along with the associated outbound shippin…
Marketplace Operators Can Induce Competitive Pricing
Tiffany Ding, Dominique Perrault-Joncas, Orit Ronen +4
As e-commerce marketplaces continue to grow in popularity, it has become increasingly important to understand the role and impact of marketplace operators on competition and social…
A shared-revenue Bertrand game
Raj Pabari, Udaya Ghai, Dominique Perrault-Joncas +6
We introduce and analyze a variation of the Bertrand game in which the revenue is shared between two players. This game models situations in which one economic agent can provide go…
How Does Critical Batch Size Scale in Pre-training?
Hanlin Zhang, Depen Morwani, Nikhil Vyas +5
Training large-scale models under given resources requires careful design of parallelism strategies. In particular, the efficiency notion of critical batch size (CBS), concerning t…
Neural Coordination and Capacity Control for Inventory Management
Carson Eisenach, Udaya Ghai, Dhruv Madeka +3
This paper addresses the capacitated periodic review inventory control problem, focusing on a retailer managing multiple products with limited shared resources, such as storage or…
A Study on the Calibration of In-context Learning
Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu +5
Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern lan…
Contextual Bandits for Evaluating and Improving Inventory Control Policies
Dean Foster, Randy Jia, Dhruv Madeka
Solutions to address the periodic review inventory control problem with nonstationary random demand, lost sales, and stochastic vendor lead times typically involve making strong as…
Learning an Inventory Control Policy with General Inventory Arrival Dynamics
Sohrab Andaz, Carson Eisenach, Dhruv Madeka +4
In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arr…