Sequential Decision Making

Sequential decision-making (SDM) focuses on optimizing a series of actions over time to achieve a desired outcome in uncertain environments. Current research emphasizes developing efficient algorithms, often based on reinforcement learning (RL) or Bayesian methods, to handle complex scenarios with multiple objectives, delayed feedback, and high-dimensional data, incorporating techniques like Thompson sampling, Monte Carlo Tree Search, and various neural network architectures. SDM's significance lies in its broad applicability across diverse fields, from personalized medicine and financial modeling to robotics and resource management, driving advancements in both theoretical understanding and practical problem-solving.

Papers

June 26, 2023

Proportional Aggregation of Preferences for Sequential Decision Making
Nikhil Chandak, Shashwat Goel, Dominik Peters
Preference Feedback Sequential Decision Making Voting Rule Multiwinner Voting Approval Voting Fairness Aware Aggregation Proportional Representation

June 15, 2023

Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning
Amin Karbasi, Nikki Lijing Kuang, Yi-An Ma, Siddharth Mitra
Reinforcement Learning Thompson Sampling Sequential Decision Making Infinite Horizon Stochastic Multi Armed Bandit Order Optimal Regret

June 5, 2023

Data-Driven Online Model Selection With Regret Guarantees
Aldo Pacchiano, Christoph Dann, Claudio Gentile
Regret Bound Bandit Feedback Regret Analysis Regret Guarantee Sequential Decision Making Learning Guarantee Online Model Selection

May 27, 2023

Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making
Xuanjie Fang, Sijie Cheng, Yang Liu, Wei Wang
Language Model Adversarial Attack Pre Trained Language Model Sequential Decision Making Adversarial Perspective

May 15, 2023

MADDM: Multi-Advisor Dynamic Binary Decision-Making by Maximizing the Utility
Zhaori Guo, Timothy J. Norman, Enrico H. Gerding
Task Utility Sequential Decision Making Optimal Decision Dynamic Decision

April 20, 2023

A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making
Carlos Núñez-Molina, Pablo Mesejo, Juan Fernández-Olivares
Reinforcement Learning Sequential Decision Sequential Decision Making Symbolic Expression Hybrid Method Subsymbolic AI

April 19, 2023

Model Based Reinforcement Learning for Personalized Heparin Dosing
Qinyang He, Yonatan Mintz
Model Based Reinforcement Learning Sequential Decision Making Scenario Generation Dosing Model

March 30, 2023

MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
Anqi Li, Byron Boots, Ching-An Cheng
Imitation Learning Offline Reinforcement Learning Sequential Decision Making Evolving Observation Offline Imitation Offline Policy Learning

March 23, 2023

Boosting Reinforcement Learning and Planning with Demonstrations: A Survey
Tongzhou Mu, Hao Su
Reinforcement Learning Timely Survey Task Planning Noisy Demonstration Sequential Decision Making Robotics Benchmark Trial and Error

March 15, 2023

Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Takuya Kanazawa, Chetan Gupta
Policy Gradient Multi Objective Reinforcement Learning Sequential Decision Making Pareto Set Multi Objective Deep Reinforcement Learning Pareto Optimal Policy

February 21, 2023

Minimax-Bayes Reinforcement Learning
Thomas Kleine Buening, Christos Dimitrakakis, Hannes Eriksson, Divya Grover, Emilio Jorge
Sequential Decision Making Prior Distribution Minimax Q Learning Decision Theoretic

February 16, 2023

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss, Hrvoje Stojic, Uri Granta, Sebastian W. Ober, Artem Artemev, Khurram Ghani, Alexander Goodall, Andrei Paleyes, Sattar Vakili, Sergio Pascual-Diaz, Stratis Markou, Jixiang Qing, Nasrulloh R. B. S Loka, Ivo Couckuyt
Deep Learning Model Bayesian Optimization Large Depth Sequential Decision Making Black Box Function Tensorflow Probability

February 6, 2023

A Strong Baseline for Batch Imitation Learning
Matthew Smith, Lucas Maystre, Zhenwen Dai, Kamil Ciosek
Markov Decision Process Feature Imitation Strong Baseline Offline Reinforcement Learning Algorithm Sequential Decision Making Batch Reinforcement Learning

February 3, 2023

A Reduction-based Framework for Sequential Decision Making with Delayed Feedback
Yunchang Yang, Han Zhong, Tianhao Wu, Bin Liu, Liwei Wang, Simon S. Du
Sequential Decision Single Agent Sequential Decision Making Model Reduction Delayed Feedback Multi Agent Sequential Decision

January 27, 2023

Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation
Krishna C Kalagarla, Rahul Jain, Pierluigi Nuzzo
Posterior Sampling Constrained Markov Decision Process Simple Regret Sequential Decision Making Constraint Violation

January 21, 2023

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
Shujian Yu, Hongming Li, Sigurd Løkse, Robert Jenssen, José C. Príncipe
Financial Application Time Series Data Conditional Reasoning Sequential Decision Making Maximum Mean Discrepancy Inverse Divergence KL Divergence Cauchy Schwarz Divergence

January 13, 2023

Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities
Samer B. Nashed, Justin Svegliato, Su Lin Blodgett
Procedural Fairness Decision Making Emerging Opportunity Continuum Limit Critical Lesson Algorithmic Fairness Sequential Decision Making Ethical Decision Making Decision Making Algorithm

January 2, 2023

Local Differential Privacy for Sequential Decision Making in a Changing Environment
Pratik Gajane
Multi Armed Bandit Environment Feature Sequential Decision Local Differential Privacy Sequential Decision Making Optimal Mechanism

October 25, 2022

Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings
Diego Martinez-Taboada, Dino Sejdinovic
Bayesian Optimization Conditional Distribution Sequential Decision Making Balancing Efficiency Bayesian Kernel Untranscribed Data

October 20, 2022

Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning
Diddigi Raghu Ram Bharadwaj, Lakshya Kumar, Saif Jawaid, Sreekanth Vempati
Deep Reinforcement Learning Markov Decision Process E Commerce User Behavior Sequential Decision Making Session Based Recommendation Consumer Behavior

Sequential Decision Making

Papers

Proportional Aggregation of Preferences for Sequential Decision Making

Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning

Data-Driven Online Model Selection With Regret Guarantees

Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making

MADDM: Multi-Advisor Dynamic Binary Decision-Making by Maximizing the Utility

A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making

Model Based Reinforcement Learning for Personalized Heparin Dosing

MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations

Boosting Reinforcement Learning and Planning with Demonstrations: A Survey

Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning

Minimax-Bayes Reinforcement Learning

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

A Strong Baseline for Batch Imitation Learning

A Reduction-based Framework for Sequential Decision Making with Delayed Feedback

Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation

The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making

Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities

Local Differential Privacy for Sequential Decision Making in a Changing Environment

Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings

Fine-Grained Session Recommendations in E-commerce using Deep Reinforcement Learning