Publications (11)
Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling
William Zhang, Saurabh Amin, Georgia Perakis
Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling process as a black box, overlooking opp…
Approximation Algorithms for Inventory Problems with Decomposable Submodular Ordering Costs
Retsef Levi, Georgia Perakis, Emily Zhang
This paper develops an approximation algorithm for the submodular joint replenishment problem (SJRP) under a broad family of decomposable submodular ordering cost functions. In the…
CoRe: Coherency Regularization for Hierarchical Time Series
Rares Cristian, Pavithra Harhsa, Georgia Perakis +1
Hierarchical time series forecasting presents unique challenges, particularly when dealing with noisy data that may not perfectly adhere to aggregation constraints. This paper intr…
Data Analytics in Operations Management: A Review
Velibor V. MiÅ¡iÄ, Georgia Perakis
Research in operations management has traditionally focused on models for understanding, mostly at a strategic level, how firms should operate. Spurred by the growing availability…
Efficient End-to-End Learning for Decision-Making: A Meta-Optimization Approach
Rares Cristian, Pavithra Harsha, Georgia Perakis +1
End-to-end learning has become a widely applicable and studied problem in training predictive ML models to be aware of their impact on downstream decision-making tasks. These end-t…
Causal LLM Routing: End-to-End Regret Minimization from Observational Data
Asterios Tsiourvas, Wei Sun, Georgia Perakis
LLM routing aims to select the most appropriate model for each query, balancing competing performance metrics such as accuracy and cost across a pool of language models. Prior appr…
Optimizing Objective Functions from Trained ReLU Neural Networks via Sampling
Georgia Perakis, Asterios Tsiourvas
This paper introduces scalable, sampling-based algorithms that optimize trained neural networks with ReLU activations. We first propose an iterative algorithm that takes advantage…
Aligning Learning and Endogenous Decision-Making
Rares Cristian, Pavithra Harsha, Georgia Perakis +1
Many of the observations we make are biased by our decisions. For instance, the demand of items is impacted by the prices set, and online checkout choices are influenced by the ass…
Tight Mixed-Integer Optimization Formulations for Prescriptive Trees
Max Biggs, Georgia Perakis
We focus on modeling the relationship between an input feature vector and the predicted outcome of a trained decision tree using mixed-integer optimization. This can be used in man…
Heterogeneous Treatment Effects in Panel Data
Retsef Levi, Elisabeth Paulson, Georgia Perakis +1
We address a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns. Many existing methods either do not util…
Inter-Series Transformer: Attending to Products in Time Series Forecasting
Rares Cristian, Pavithra Harsha, Clemente Ocejo +4
Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have sho…