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From the 1 of 8 linked papers with an AI index.

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8 papers

cs.LG2026

Learning to Control Coupled-Dynamics Environments with Joint Markov Decision Processes

Ege C. Kaya, Aliasghar Pourghani, Mahsa Ghasemi +2

Coupled-dynamics environments expose the one-step outcomes that would follow from several possible counterfactual actions under a common realization of exogenous randomness. The or…

cs.LG2026

First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints

Zhankun Luo, Antesh Upadhyay, Sang Bin Moon +1

The paper introduces a first-order Softmax‑Weighted Switching Gradient algorithm for distributed stochastic minimax optimization with stochastic constraints, offering theoretical g…

cs.LG2026

Mitigating Spurious Correlations with Memorization-Guided Dataset De-Biasing

Arda Fazla, Abolfazl Hashemi

Real-world datasets often contain spurious correlations that are not causally related to the target label. When such correlations dominate the majority of training samples, models…

cs.LG2026

Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning

Ege C. Kaya, Aliasghar Pourghani, Vijay Gupta +1

Average-reward reinforcement learning requires estimating the gain and the bias, which is defined only up to an additive constant. This makes direct distributional analogues ill-po…

cs.LG2026

A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning

Ege C. Kaya, Abolfazl Hashemi

We study finite-iteration behavior of the exact asynchronous recursions used by categorical distributional temporal-difference methods. The analysis covers scalar categorical TD in…

cs.LG2026

Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance

Arda Fazla, Ege C. Kaya, Antesh Upadhyay +1

Analysis of Stochastic Gradient Descent (SGD) and its variants typically relies on the assumption of uniformly bounded variance, a condition that frequently fails in practical non-…