most_recent.md

~reading

• Syllabus_RSM_Avi

20240601_bayesian

• dew-et-al-2019-modeling-dynamic-heterogeneity-using-gaussian-processes

20240527_VIT

• vit
• resnet
• WHEN VISION TRANSFORMERS OUTPERFORM RESNETS WITHOUT PRE-TRAINING OR STRONG DATA AUGMENTATIONS
• Do Vision Transformers See Like Convolutional Neural Networks
• ConvNets Match Vision Transformers at Scale
• Are Transformers More Robust Than CNNs

20240513_seachQuery

• eCeLLM- Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
• Large Language Model based Long-tail Query Rewriting in Taobao Search

20240512_ERM_DRO

• UNDERSTANDING WHY GENERALIZED REWEIGHTING DOES NOT IMPROVE OVER ERM
• UNDERSTANDING THE ROLE OF IMPORTANCE WEIGHTING FOR DEEP LEARNING
• Kullback-Leibler Divergence Constrained Distributionally Robust Optimization
• In Search of Lost Domain Generalization

20240509_DPO

• Understanding Contrastive Learning via Distributionally Robust Optimization
• STATISTICAL REJECTION SAMPLING IMPROVES PREFERENCE OPTIMIZATION
• SLiC-HF- Sequence Likelihood Calibration with Human Feedback
• RLPROMPT- Optimizing Discrete Text Prompts with Reinforcement Learning
• Optimizing Language Models for Human Preferences is a Causal Inference Problem
• Iterative Preference Learning from Human Feedback- Bridging Theory and Practice for RLHF under KL-Constraint
• Is DPO Superior to PPO for LLM Alignment- A Comprehensive Study
• Generalized Preference Optimization- A Unified Approach to Offline Alignment
• From r to Q∗ - Your Language Model is Secretly a Q-Function
• Direct Preference Optimization- Your Language Model is Secretly a Reward Model
• A note on DPO with noisy preferences & relationship to IPO
• A General Theoretical Paradigm to Understand Learning from Human Preferences

20240308_diffusion

• Neural Network Diffusion

20240222_fin_decision_making

• Consumer Financial Decision Making-Where We ve Been and Where We -re Going

20240217_rlhf_and_causal_inference

• Policy learning with observational data
• Large Language Models and Causal Inference in Collaboration- A Comprehensive Survey
• Do Transformers Really Perform Bad for Graph Representation
• Conservative Q-Learning for Offline Reinforcement Learning
• Choice Models and Permutation Invariance- Demand Estimation in Differentiated Products Markets
• Causal Transformer for Estimating Counterfactual Outcomes
• CAUSAL ESTIMATION FOR TEXT DATA WITH (APPARENT) OVERLAP VIOLATIONS
• BENCHMARKS FOR DEEP OFF-POLICY EVALUATION

20240125_creativityLLM

• When ChatGPT is gone- Creativity reverts and homogeneity persists
• Uncertainty in Language Models- Assessment through Rank-Calibration
• Training language models to follow instructions with human feedback
• Measuring Divergent Thinking Originality With Human Raters and TextMining Models- A Psychometric Comparison of Methods
• DOES WRITING WITH LANGUAGE MODELS REDUCE CONTENT DIVERSITY
• Computational Creativity and Music Generation Systems
• Beyond semantic distance- Automated scoring of divergent thinking greatly improves with large language models

20240117_structural

• Structural Econometric Modeling in Industrial Organization and Quantitative Marketing_ Theory and Applications (2023)
• Nonparametric Estimation of Habitual Brand Loyalty
• Handbook of the Economics of Marketing_ Marketing and Economics-North-Holland (2019)
• Estimating Parameters of Structural Models Using Neural Networks
• Deep Neural Networks for Estimation and Inference
• Causal Interpretation of Structural IV Estimands

20231212_LLM

• coauthor-Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities
• The Power of Scale for Parameter-Efficient Prompt Tuning
• TRUSTLLM- TRUSTWORTHINESS IN LARGE LANGUAGE MODELS
• Stanford_Speech and Language Processing
• Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling
• LORA- LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
• Deep Learning-based Estimation of Dynamic Discrete Choice Models with an Application to the Expansion of Walmart
• DECODINGTRUST- A Comprehensive Assessment of Trustworthiness in GPT Models
• Can LLMs Capture Human Preferences
• Alleviating Hallucinations of Large Language Models through Induced Hallucinations
• A Survey on Hallucination in Large Language Models

20231129_cb_fariness

• xia-et-al-2004-the-price-is-unfair-a-conceptual-framework-of-price-fairness-perceptions
• liu-et-al-2023-algorithm-aversion-evidence-from-ridesharing-drivers
• Perceptions of Price Unfairness Antecedents and Consequesnces
• Fairness as a Constraint on Profit Seeking- Entitlements in the Market
• Dynamic Pricing and Consumer Fairness Perceptions
• Consumer Perceptions of Price (Un)Fairness
• Algorithms propagate gender bias in the marketplace with consumers cooperation

20231121_RLHF

• malika_draft_jmp
• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
• The Challenge of Using LLMs to Simulate Human Behavior- A Causal Inference Perspective
• Secrets of RLHF in Large Language Models Part I- PPO
• Q-Transformer- Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
• Policy Optimization in RLHF- The Impact of Out-of-preference Data
• Is RLHF More Difficult than Standard RL
• Inverse Reinforcement Learning with Conditional Choice Probabilities
• Defining and Characterizing Reward Hacking
• Deep reinforcement learning from human preferences
• Contrastive Preference Learning- Learning from Human Feedback without RL
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Better Planning with Transformers via Search Dynamics Bootstrapping
• A Course in Reinforcement Learning

20231115_consumersearch

• a model of price adjustment
• Which demand systems can be generated by discrete choice
• Varian-ModelSales-1980

20231101_hallucination

• TruthfulQA- Measuring How Models Mimic Human Falsehoods
• On the Creativity of Large Language Models
• On Characterizations of Large Language Models and Creativity Evaluation
• Llama 2- Open Foundation and Fine-Tuned Chat Models
• Inspecting and Editing Knowledge Representations in Language Models
• Art or Artifice Large Language Models and the False Promise of Creativity

20231024_exploration

• where Consumers Diverge from Others- Identity Signaling and Product Domains
• kahn-et-al-1986-measuring-variety-seeking-and-reinforcement-behaviors-using-panel-data
• When to Trust Your Simulator- Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
• What Out-of-distribution Is and Is Not
• The Effects of Diversity in Algorithmic Recommendations on Digital Content Consumption- A Field Experiment
• Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning
• Offline Reinforcement Learning- Tutorial, Review, and Perspectives on Open Problems
• Off-Policy Deep Reinforcement Learning without Exploration
• Model-Based Reinforcement Learning with Nearly Tight Exploration Complexity Bounds
• Doubly Robust Policy Evaluation and Learning
• Counterfactual Reasoning and Learning Systems- The Example of Computational Advertising

20231011_Fairness_Algo

• Reducing Interference Bias in AB Tests of Ranking Algorithms
• Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy’s Price Discrimination Algorithms
• Apparent Algorithmic Discrimination and Real-Time Algorithmic Learnin
• Algorithmic Bias An Empirical Study of Apparent Gender-based Discrimination in the Display of STEM Career Ads

20230928_recSys

• why people skip music
• When Fairness meets Bias- a Debiased Framework for Fairness aware Top-N Recommendation
• Values of User Exploration in Recommender Systems
• Towards Robust Fairness-aware Recommendation
• Towards Open-World Recommendation- An Inductive Model-based Collaborative Filtering Approach
• Surrogate for Long-Term User Experience in Recommender Systems
• Surrogate for Long-Term User Experience in Recommender Systems 2023-11-01 16_17_20
• Recommending for a Multi-sided Marketplace A Multi-Objective Hierarchical Approach
• Recency Dropout for Recurrent Recommender Systems
• RLAIF- Scaling Reinforcement Learning from Human Feedback with AI Feedback
• LearningtoRankfor InformationRetrieval
• How do successful scholars get their best research ideas
• Deep Neural Networks for YouTube Recommendations

20230914_GNN

• Variational Graph Auto-Encoders
• NEURAL RELATIONAL INFERENCE WITH NODE-SPECIFIC INFORMATION
• Link Prediction Based on Graph Neural Networks
• Learnable Graph Convolutional Attention Networks
• Deepwalk Online learning of social representations

20230829_Fairness

• graph_convolution_network_base
• Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation
• Shin Oblander JMP draft
• Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems
• Fairness in Matching under Uncertainty
• Fairness and Abstractions in Sociotechnical Systems
• Disentangling and Operationalizing AI Fairness at LinkedIn
• Discrimination through Optimization- How Facebook’s Ad Delivery Can Lead to Biased Outcomes
• Discrimination through Image Selection by Job Advertisers on Facebook
• Controlling Fairness and Bias in Dynamic Learning-to-Rank

20230801_MDP

• Using Big Data to Model Time-Varying Effects for Marketing Resource (Re)Allocation
• Timeseries in MKT
• Sparse Sinkhorn Attention
• Morphing for Consumer Dynamics- Bandits Meet Hidden Markov Models
• Modelling Retail Customer Behavior at Merrill Lynch
• Modeling Marketing Dynamics by Time Series Econometrics
• MODELING CUSTOMER RELATIONSHIPS AS MARKOV CHAINS
• LightGBM- A Highly Efficient Gradient Boosting Decision Tree
• Language Models are Few-Shot Learners
• Improving Language Understanding by Generative Pre-Training
• Herding Learning and Incentives for Online Reviews
• Effects of Word-of-Mouth Versus Traditional Marketing Findings from an Internet Social Networking Site
• Dynamic discrete choice structural models- A survey
• Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability
• Comments on the Origin and Application of Markov Decision Processes
• Attention mechanisms in computer vision A survey
• A Survey on Evaluation of Large Language Models
• A Joint Model of Usage and Churn in Contractual Settings
• A General Survey on Attention Mechanisms in Deep Learning

20230601_demand

• Microeconometric models of consumer demand
• Assessing Consumer Demand with Noisy Neural Measurements

20230314_creativity

• What can quantitative measures of semantic distance tell us about creativity
• Toubia, Berger and Eliashberg 2021
• Toubia 2021
• The Cambridge Handbook of the Neuroscience of Creativity-Cambridge University Press (2018)
• Semantic Distance- An Automated Measure of Creativity That Is Novel and Appropriate
• Idea Generation Creativity and Prototypicality
• Extracting Features of Entertainment Products Guided LDA
• Can AI Help in Ideation- A Theory-Based Model for Idea Screening in Crowdsourcing Contests
• Automating creativity assessment with SemDis - An open platform for computing semantic distance
• Assessing Associative Distance Among Ideas Elicited by Tests of Divergent Thinking