arXiv Machine Learning
Jun 2
Mirror Descent Under Generalized Smoothness
arXiv:2502. 00753v4 Announce Type: replace-cross Abstract: Smoothness is crucial for attaining fast rates in first-order optimization.
Incremental Learning in Mirror Flows
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arXiv Machine Learning
5d ago
Mirror descent algorithms with logarithmic barriers
arXiv:2608. 22834v2 Announce Type: replace-cross Abstract: We derive convergence guarantees for mirror descent and proximal mirror descent with a logarithmic barrier as the distance-generating function.
arXiv Machine Learning
Sep 23
Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader
arXiv:2609.09466v2 Announce Type: replace Abstract: Online gradient descent is usually studied through external regret, where the learner competes with fixed alternatives. Recent work shows that firs...
arXiv Machine Learning
Aug 11
Online Learning of Scale Parameters in Score-Driven Filters
arXiv:2608. 09218v1 Announce Type: new Abstract: Score-driven filters multiply a scaled log-likelihood score by a gain that controls the update magnitude.
arXiv AI
Oct 15
A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning
arXiv:2610.14041v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) is classically solved using Lagrangian optimisation, by jointly optimising a primal-dual problem with gradient d...
arXiv Machine Learning
Jun 5
Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
arXiv:2605. 23415v2 Announce Type: replace Abstract: Reinforcement learning has long struggled with poor sample efficiency.
arXiv Machine Learning
Jun 11
Mirror Descent Beyond Euclidean Stability: An Exponential Separation in Initialization Sensitivity
arXiv:2606. 11431v1 Announce Type: new Abstract: Mirror Descent (MD) extends Gradient Descent (GD) beyond Euclidean geometry and has recently reappeared as a lens for KL-regularized policy optimization in reinforcement learning and LLM post-training.
arXiv Machine Learning
Sep 14
Block-Norm Geometries for Online Mirror Descent with Sparse Losses
The paper investigates how the choice of geometry in online mirror descent affects performance, particularly when loss gradients are sparse. It introduces randomized block‑norm mirror maps that interpolate between Euclidean and entropic geometries, achieving polynomial‑in‑dimension regret improvements over standard methods for various convex sets. The authors also demonstrate that naive alternation between mirror maps can lead to linear regret and propose a Hedge‑based meta‑algorithm that competes with the best mirror map in a finite portfolio, achieving near‑optimal regret for random block geometries.
arXiv Machine Learning
Jun 19
The Hidden Cost of Approximation in Online Mirror Descent
arXiv:2511. 22283v2 Announce Type: replace Abstract: Online mirror descent (OMD) is a fundamental algorithmic paradigm that underlies many algorithms in optimization, machine learning and sequential decision-making.
arXiv Machine Learning
Sep 24
Value Mirror Descent for Reinforcement Learning
arXiv:2604. 06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL).
arXiv Machine Learning
Sep 1
Reciprocity Separates Gradient Flow from Rotation in Conservative Physical Learning
arXiv:2608.30778v1 Announce Type: new Abstract: Physical learning lets a trainable material or network use its own physical response to carry error signals, reducing the need for a separately program...
arXiv AI
Oct 12
Beyond State-Wise Mirror Descent: Offline Policy Optimization with Parametric Policies
The paper extends theoretical guarantees for offline reinforcement learning to parameterized policy classes over large or continuous action spaces, overcoming limitations of prior algorithms that required finite action sets and state‑wise mirror descent. By addressing contextual coupling and linking mirror descent to natural policy gradients, the authors provide new analyses, algorithmic insights, and a surprising connection between offline RL and imitation learning.