:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Guduguntla, Vamshi, Soni, Kavin, Das, Debanshu
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2604.02472
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Assessing the Operational Viability of Foundation Models for Time Series Forecasting
by: Soni, Kavin, et al.
Published: (2026)

Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
by: He, Bowei, et al.
Published: (2024)

Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
by: Su, Xinyan, et al.
Published: (2026)

Beyond Fluency: Toward Reliable Trajectories in Agentic IR
by: Sinha, Anushree, et al.
Published: (2026)

A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling
by: Zhang, Ruyue, et al.
Published: (2025)

Interpreting and Steering State-Space Models via Activation Subspace Bottlenecks
by: Mohan, Vamshi Sunku, et al.
Published: (2026)

Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
by: Zhu, Dingyuan, et al.
Published: (2024)

Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth
by: Wei, Yuxiang, et al.
Published: (2024)

Benchmarking for Deep Uplift Modeling in Online Marketing
by: Liu, Dugang, et al.
Published: (2024)

Uplift Modeling Under Limited Supervision
by: Panagopoulos, George, et al.
Published: (2024)

TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis
by: Zhang, Hangtao, et al.
Published: (2025)

Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques
by: Park, Yoon Tae, et al.
Published: (2024)

Principal Prototype Analysis on Manifold for Interpretable Reinforcement Learning
by: Vamshi, Bodla Krishna, et al.
Published: (2026)

A New Transformation Approach for Uplift Modeling with Binary Outcome
by: Li, Kun, et al.
Published: (2023)

UTBoost: Gradient Boosted Decision Trees for Uplift Modeling
by: Gao, Junjie, et al.
Published: (2023)

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
by: Sun, Zexu, et al.
Published: (2023)

GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface
by: Esparza, Miguel, et al.
Published: (2025)

Hierarchical Contextual Uplift Bandits for Catalog Personalization
by: Agrawal, Anupam, et al.
Published: (2026)

Value-Aware Numerical Representations for Transformer Language Models
by: Dutulescu, Andreea, et al.
Published: (2026)

M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
by: Sun, Zexu, et al.
Published: (2024)

Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
by: Yang, Yuxuan, et al.
Published: (2026)

Movie Revenue Prediction using Machine Learning Models
by: Udandarao, Vikranth, et al.
Published: (2024)

Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth
by: Kadioglu, Serdar, et al.
Published: (2024)

Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference
by: Akker, Bram van den
Published: (2025)

FairUDT: Fairness-aware Uplift Decision Trees
by: Zahid, Anam, et al.
Published: (2025)

Manifold-based Sampling for In-Context Hallucination Detection in Large Language Models
by: Vamshi, Bodla Krishna, et al.
Published: (2026)

Protocode: Prototype-Driven Interpretability for Code Generation in LLMs
by: Bodla, Krishna Vamshi, et al.
Published: (2025)

Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
by: Zhang, Jiaxin, et al.
Published: (2024)

Measuring Moral Inconsistencies in Large Language Models
by: Bonagiri, Vamshi Krishna, et al.
Published: (2024)

A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
by: Singh, Aman
Published: (2026)

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
by: Pillai, Abhirami
Published: (2026)

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
by: Liu, Yang, et al.
Published: (2025)

Uplift modeling with continuous treatments: A predict-then-optimize approach
by: De Vos, Simon, et al.
Published: (2024)

Revenue Maximization and Learning in Products Ranking
by: Chen, Ningyuan, et al.
Published: (2020)

Chatbots and Zero Sales Resistance
by: Succi, Sauro
Published: (2024)

Choice-Model-Assisted Q-learning for Delayed-Feedback Revenue Management
by: Shen, Owen, et al.
Published: (2026)

Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing
by: Sun, Zexu, et al.
Published: (2025)

Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
by: Huang, Yinqiu, et al.
Published: (2024)

Enhancing Retail Sales Forecasting with Optimized Machine Learning Models
by: Ganguly, Priyam, et al.
Published: (2024)

Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation
by: Das, Debanshu, et al.
Published: (2026)