We demonstrate a limitation of discounted expected utility, a standard approach for representing the preference to risk when future cost is discounted. Specifically, we provide an example of the preference of a decision maker that appears to be rational but cannot be represented with any discounted expected utility. A …
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48 results for “Iterative Decisions”
Graph-based NAS improves sample efficiency in architecture design.
problem Current NAS search spaces are static sequences, limiting expressiveness.
method Proposed graph-based search space with vertices and edges for iterative and branching decisions.
result Graph representation improves sample efficiency in architecture design.
Proposes a faster second-order method for MDPs.
problem Slow convergence of first-order value iteration methods in MDPs.
method Applies Newton-Raphson method to successive relaxation value iteration scheme.
result Second-order convergence and faster convergence to optimal solution.
Novel algorithm for Markov decision processes using rank-one approximation.
problem Solving planning and learning problems of Markov decision processes.
method Policy iteration with rank-one approximation of transition probability matrix.
result The proposed algorithm consistently outperforms first-order algorithms and their accelerated versions.
Study optimality in safety-constrained Markov decision processes using asynchronous value iteration and modified Q-learning.
problem Optimality in safety-constrained Markov decision processes with multichain structure.
method Formulated as a zero-sum game, constructed asynchronous value iteration scheme and modified Q-learning algorithm.
result Resolved Bellman's principle of optimality for multichain Markov decision processes and provided learning algorithms.
New algorithms solve robust MDPs efficiently, significantly faster than existing methods.
problem Computing robust MDP solutions with uncertainty in transition probabilities is computationally expensive.
method Partial policy iteration and fast robust Bellman operator computation methods.
result The proposed methods are many orders of magnitude faster than state-of-the-art approaches.
Paper proves exponential lower bounds for policy iteration in MDPs.
problem Proving lower bounds for policy iteration in multi-action MDPs.
method Generalized previous results to k-action MDPs, constructed families of MDPs.
result Proved novel exponential lower bound of (3+k)2^(N/2-3) iterations.
Theory unifies various reinforcement learning methods with a generalized regularized approach.
problem Improving reinforcement learning algorithms with regularization.
method Develops a theory of regularized Markov Decision Processes, extending previous approaches.
result Unified analysis of various reinforcement learning algorithms.
Study wSAA for contextual decisions, improving uncertainty quantification under computational constraints.
problem Uncertainty quantification limitations in wSAA for contextual stochastic optimization.
method Establish central limit theorems and asymptotic-normality-based confidence intervals for optimal costs.
result Over-optimizing can mitigate misspecification and preserve asymptotic normality, albeit at a slower convergence rate.
Max-plus algebra approximates MDP value iteration for reduced state space.
problem Approximating optimal value function in high-dimensional MDPs.
method Uses max-plus algebra to represent value functions in a smaller dictionary, leading to an adaptive matching pursuit algorithm.
result Theoretical results show reduced complexity not tied to state space size, with empirical success on simple problems.
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimalitymath.OC
Stochastic algo learns from evolving data, achieving optimal performance.
problem Performative prediction and multiplayer extensions.
method Stochastic approximation with decision-dependent distributions.
result Asymptotic normality and optimality of the algorithm's performance.
ID3 generates near-optimal decision trees for DNFs under product distributions.
problem Understanding the optimality of decision trees generated by ID3.
method Introducing a new metric (MIC) to measure the optimality of ID3-generated trees and comparing it with other algorithms.
result The TopDown variant of ID3 is near-optimal in learning read-once DNFs under product distributions, while another variant is not.
SnapBoost uses random base hypothesis classes to improve gradient boosting performance.
problem Improving gradient boosting performance.
method Heterogeneous Newton Boosting Machine (HNBM) with variable base hypothesis classes.
result SnapBoost achieves better generalization loss than competing frameworks.
RLCFR improves CFR's generalization in imperfect information games.
problem Improving CFR's performance in large-scale, imperfect information games.
method RLCFR integrates CFR with deep reinforcement learning to update strategies dynamically.
result RLCFR significantly enhances CFR's generalization ability in various games.
New framework optimizes complex systems decisions via simulation.
problem Optimizing strategic, tactical, and operational decisions in complex systems.
method Global-local metamodel assisted two-stage optimization via simulation.
result Framework efficiently searches for optimal decisions with unknown objective.
We develop robust Markov Decision Processes with risk measures for uncertain environments.
problem Uncertainty in Markov Decision Processes and its impact on risk measures.
method Formulation as a Stackelberg game, robust cost and value iterations, existence of optimal policies.
result Existence of deterministic optimal policies for robust optimization and risk measures.
This paper presents a way of solving Markov Decision Processes that combines state abstraction and temporal abstraction. Specifically, we combine state aggregation with the options framework and demonstrate that they work well together and indeed it is only after one combines the two that the full benefit of each is re…
Q-learning for average cost MDPs gets a concentration bound.
problem Finding bounds for Q-learning in average cost MDPs.
method Derives a concentration bound using shortest path problem equivalence.
result Numerical comparison with relative value iteration shows the bound's effectiveness.
This paper analyzes risk-sensitive reinforcement learning with Conditional Value-at-Risk (CVaR) for robust Markov Decision Processes.
problem Risk-sensitive reinforcement learning for robust Markov Decision Processes (RMDPs) with state-action-dependent ambiguity sets.
method The paper establishes a connection between robustness and risk sensitivity, defining a new risk measure NCVaR and proposing value iteration algorithms.
result The proposed approach using NCVaR optimization and value iteration algorithms can solve problems with state-action-dependent ambiguity sets.
AGBoost uses attention weights to improve GBM for regression problems.
problem Improving gradient boosting machine for regression tasks.
method Attention-based modification of GBM with trainable attention weights.
result AGBoost achieves better performance on regression datasets.
New method for robust policy evaluation in offline reinforcement learning with sequentially exogenous unobserved confounders.
problem Offline reinforcement learning in domains with unobserved confounders.
method Orthogonalized robust fitted-Q-iteration with closed-form solutions and bias-correction.
result Effective in simulations and real-world data, improving robustness and computational ease.
More and more processes governing our lives use in some part an automatic decision step, where -- based on a feature vector derived from an applicant -- an algorithm has the decision power over the final outcome. Here we present a simple idea which gives some of the power back to the applicant by providing her with alt…
The paper tackles robust reinforcement learning with performance guarantees.
problem Finding a robust policy for RMDP with state space uncertainties.
method Proposes RLSPI algorithm for learning optimal robust policy with performance bounds.
result Demonstrates the performance of RLSPI on standard benchmark problems.
Lower bounds for PI on multi-action MDPs are established, showing complexity grows with action count.
problem Establishing the minimum number of iterations for PI to converge on MDPs with multiple actions.
method Developed lower bounds for a specific PI variant on multi-action MDPs, scaling with action count.
result A particular PI variant can take iterations to terminate, scaling with action count.
This study models FOMC policy decisions using debate-based LLMs.
problem Accurately predicting central bank policy decisions, especially FOMC's, is challenging.
method A novel framework that simulates FOMC's collective decision-making process through iterative rounds of LLMs interacting as agents.
result The debate-based approach significantly outperforms standard LLMs in prediction accuracy.
Improved Politex algorithm reduces regret bound to O(√T) with experience replay.
problem Learning in infinite-horizon MDPs with function approximation.
method Sharpened regret analysis of Politex algorithm, experience replay implementation.
result First high-probability O(√T) regret bound for computationally efficient algorithm.
A new algorithm uses IVs to learn optimal policies from observational data.
problem Learning optimal policies from unobserved variable confounded data.
method IV-aided Value Iteration (IVVI) algorithm based on conditional moment restrictions.
result First provably efficient algorithm for instrument-aided offline RL.
This paper speeds up iterative GP inference with warm starting.
problem Improving scalability of Gaussian process inference.
method Warm starting sequential posteriors using known solutions.
result Significant speed-ups and improved Bayesian optimisation performance.
Paper improves worst-case regret bounds for RLSVI in reinforcement learning.
problem Minimizing regret in reinforcement learning with randomized value functions.
method Introduces a clipping variant of Thompson Sampling for RLSVI.
result Achieves a worst-case regret bound.
New method for optimistic planning in MDPs using regularization.
problem Optimistic planning in infinite-horizon discounted MDPs.
method Regularized dynamic programming for approximate value iteration.
result Achieves near-optimal statistical guarantees in learning policies.
Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processesstat.ME
Paper tackles robust decision-making from multiple sites with shared structure.
problem Learning robust sequential decisions from heterogeneous multi-site datasets.
method Group-Robust MDPs with d-rectangular uncertainty sets, feature-wise worst-case aggregation, and cluster-level pooling.
result Proves suboptimality bound for robust planning policy under robust partial coverage assumption.
Stochastic domains often involve risk-averse decision makers. While recent work has focused on how to model risk in Markov decision processes using risk measures, it has not addressed the problem of solving large risk-averse formulations. In this paper, we propose and analyze a new method for solving large risk-averse …
Heuristic tool estimates lactate threshold for easier training decisions.
problem Improving lactate threshold estimation for recreational runners.
method Formalized lactate threshold principles, iterative methodology, heuristic approach.
result Heuristic %60 of 'endurance running speed reserve' is reliable and accessible.
This dissertation advances scalable Gaussian processes using iterative methods and pathwise conditioning.
problem The classical Gaussian process formulation is not scalable for large datasets and modern hardware.
method Combining iterative methods and pathwise conditioning to improve scalability.
result Significantly reduced memory requirements and facilitated application to larger datasets.
Policy gradient methods with aggregated states can achieve better performance than approximate policy iteration.
problem Approximation errors in policy and value function approximations.
method State-aggregated representations and policy gradient methods.
result Policy gradient methods can achieve a per-period regret bounded by ε, while approximate policy iteration and value iteration have a higher regret.
Study tight offline learning bounds for linear MDPs using variance information.
problem Understanding statistical limits with linear function representations in offline reinforcement learning.
method Variance-aware pessimistic value iteration (VAPVI) that reweights Bellman residuals based on estimated variances.
result Improved offline learning bounds expressed in terms of system quantities.
Proposes SOR Q-learning for faster optimal value function computation in RL.
problem Finding optimal value function in Markov Decision Processes (MDPs).
method Successive Over-Relaxation (SOR) applied to Q-learning algorithm.
result SOR Q-learning converges faster to optimal value function compared to standard Q-learning.
The paper studies reward concentration in MDPs, covering asymptotic and non-asymptotic settings.
problem Reward concentration in Markov Decision Processes (MDPs).
method Unified approach to reward concentration in MDPs, including asymptotic and non-asymptotic bounds.
result Rate-equivalent definitions of regret for learning policies.
New algorithm improves knowledge transfer in dynamic decision-making.
problem Utilizing data from existing ventures to improve decision-making in new ventures.
method Proposes Transferred Fitted -Iteration algorithm for estimating optimal action-state function .
result Significantly improved final learning error of function.
Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementationscs.LG
In this paper we discuss policy iteration methods for approximate solution of a finite-state discounted Markov decision problem, with a focus on feature-based aggregation methods and their connection with deep reinforcement learning schemes. We introduce features of the states of the original problem, and we formulate …
New algorithm adapts to changing action sets in sequential decision making.
problem Adapting to a changing action set in sequential decision making problems.
method Breaks the problem into inferring action structure and optimizing policy.
result Demonstrates efficiency on real-world lifelong learning problems.
We consider the infinite-horizon discounted optimal control problem formalized by Markov Decision Processes. We focus on several approximate variations of the Policy Iteration algorithm: Approximate Policy Iteration, Conservative Policy Iteration (CPI), a natural adaptation of the Policy Search by Dynamic Programming a…
Paper approximates solutions for complex decision processes with limited precision.
problem Approximating the set of all solutions for Multi-objective Markov Decision Processes.
method Limited precision approach based on White's multi-objective value-iteration dynamic programming algorithm.
result The number of calculated solutions is tractable and approximates the true Pareto front.
New algorithm solves minimax games with linear constraints.
problem Nonconvex minimax games with coupled linear constraints.
method Primal-dual alternating proximal gradient (PDAPG) algorithm.
result Achieves ε-stationary solution within O(ε^(-2)) iterations for strongly concave settings.
New algorithm achieves optimal regret in average reward MDPs without prior bias information.
problem Achieving optimal regret in average reward MDPs with computational efficiency and without prior bias information.
method Projective Mitigated Extended Value Iteration (PMEVI) to compute bias-constrained optimal policies efficiently.
result First tractable algorithm with minimax optimal regret of .
VA-OPE improves OPE by incorporating variance information, achieving tighter error bounds.
problem Estimating value function of a target policy from offline data collected by a behavior policy.
method Proposes VA-OPE, an algorithm that reweights Bellman residual using estimated variance of the value function.
result Achieves a tighter error bound than the best-known result.
Framework for responsible LLM deployment with human involvement and decentralized technologies.
problem Challenges in deploying LLMs for high-stakes decisions, including data security and accountability.
method Interactive human involvement through multiple iterations, decentralized technologies, and automated auditing.
result Enhanced security and accountability in LLM deployment for financial decisions.
New sampling technique improves boosting model accuracy.
problem Improving generalization performance and learning time in stochastic gradient boosting.
method Formulated optimization problem to maximize estimation accuracy, leading to Minimal Variance Sampling (MVS).
result MVS significantly increases model quality and reduces the number of examples needed.