arXiv Machine Learning
DePICT: Decision-Preserving Interface for Constrained Downstream Tasks
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arXiv Computation and Language
Sep 1
AlgoWorlds: Benchmarking Tool Use for Global Optimization in Algorithmic Worlds
arXiv:2608.29397v1 Announce Type: new Abstract: Tool-use benchmarks generally evaluate whether an agent completes a workflow using appropriate tools and valid arguments. However, feasibility alone is...
arXiv Machine Learning
Jun 15
Utility-Constrained Policy Optimization
arXiv:2606. 14029v1 Announce Type: new Abstract: Constrained MDPs (CMDPs) are a widely adopted framework for incorporating safety into RL agents; however, the framework does not support risk-sensitive constraints.
arXiv Statistics ML
Oct 26
Contextual Decision Making: Predictive or Generative
The paper compares two strategies for contextual decision making: predict‑then‑optimize (PtO), which learns the conditional expected objective, and generate‑then‑optimize (GtO), which learns conditional uncertainty and optimizes over generated scenarios. It analyzes how the choice of learning target influences statistical accuracy and optimization tractability, showing that smoothness can compensate for GtO’s higher dimensionality. The study also discusses tradeoffs in convexity preservation, scenario‑wise convexity, and model flexibility, supported by synthetic experiments and a real case study.
arXiv AI
Sep 1
SimGuide: Typed Multi-Context User Representations for Preference-Conditioned Agent Planning
arXiv:2608.24888v2 Announce Type: replace Abstract: Agents that act on a user's behalf must plan differently for different users, and increasingly do so from some structured representation of user co...
arXiv Machine Learning
Sep 23
Differentiable Policy Transport over Multi-Layer Network Feasibility Geometry
The paper introduces Network Feasibility Geometry Reinforcement Learning (NFG‑RL), a method that enforces multi‑layer network constraints—such as interference, power‑rate coupling, flow conservation, service chains, capacity, latency, and reliability—by transporting a proto‑policy through a differentiable feasibility map. By compiling heterogeneous constraints into typed residual blocks and using a variational transport operator, NFG‑RL ensures almost‑sure feasible execution and shapes exploration and gradients to respect active constraints. Experiments on two wireless‑edge surrogate environments show that NFG‑RL boosts feasible utility by 37.5–41.5 %, cuts raw‑action violations by 48.5–60.8 %, and reduces P99 delay by 57.0–75.5 % compared to leading baselines.
arXiv Machine Learning
Aug 28
Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions
The paper introduces a new approach to safety in contextual bandits with continuous actions, focusing on high‑probability constraints on the realized cost rather than expected cost. It presents the High‑Probability Constrained UCB algorithm, which balances reward exploration with conservative safety estimation, and provides theoretical regret guarantees for linear models and extensions to general function classes. Experiments demonstrate that this realized‑cost safety framework significantly reduces safety violations compared to expected‑cost constrained methods.