Papers for

machine learning modelers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Small solution sharing boosts multi-objective optimization speed

Understanding the Co-evolutionary Solution Exchange of the MOEA/D: Already the Smallest Communication Radius Gives the Best-Possible Speed-Up

Abstract: Besides domination-based algorithms such as the NSGA-II, the decomposition-based MOEA/D is one of the most popular and successful optimization heuristic for multi-objective problems. It decomposes the problem into several, single-objective subproblems. By solving these in a co-evolutionary manner and archiving non-dominated solutions, it approximates or computes the Pareto front. Interestingly, so far no advantages from the co-evolutionary communication of subproblem solutions were proven. In this first mathematical runtime analysis addressing this research question, we investigate how the mutation-based MOEA/D solves the classic OneMinMax benchmark when solutions are exchanged among subproblems in different distances $r$. We show, for the first time, that the MOEA/D profits from an exchange of solutions, and that already very small communication radii $r$ are sufficient to maximally profit from this feature of the algorithm. More precisely, for all values $r \ge 1$ we prove a tight runtime guarantee of $Θ(n^2)$, where $n$ is the problem size. This is asymptotically faster than the known bound of $O(n^2 \log n)$ guarantee for $r=0$, which we complement with a matching bound of $Ω(n^2 \log n)$.

Mon 26 OctNeural and Evolutionary Computing
The gist
Multi-objective optimization tries to find the best trade-offs between different goals at once. A popular method, MOEA/D, breaks these problems into smaller ones and solves them together. The authors studied how sharing solutions between these smaller problems helps speed up the process. They found that even sharing solutions on a very small scale speeds up finding good solutions significantly compared to sharing nothing.
Open → 2610.35373v1

Exact math improves modeling of light in biaxial crystals

Exact Quotients of Fresnel-Kummer Surfaces and Certified Biaxial Refraction

Abstract: The Fresnel wave surface governs the propagation of light in a transparent biaxial crystal. It is a special Kummer quartic, and we identify it exactly. Over the complex numbers the wave surface of a crystal is the Kummer surface of the Jacobian of an explicit genus-two curve branched at the signed square roots of the three principal permittivities. This Jacobian is isogenous, by an isogeny with kernel of order four, to a product of two elliptic curves. One elliptic curve carries the three permittivities, and the other carries the optic-axis angle. The physical family is Zariski dense in the locus of genus-two curves with an extra involution, and its automorphism strata are explicit. The identification instantiates a task-aware quotient, which identifies parameters that differ by a nuisance transformation and carries invariant coordinates and explicit strata. For biaxial crystals, two ratios of the permittivities form a complete invariant of the wave surface up to rotation and rescaling, and the four real nodes are given in closed form. At an interface the candidate transmitted waves are the roots of a quartic of exact degree four. Its real-root count, root order, and repeated-root events are decided by exact algebraic predicates, and along the generic single-node encounters of the paper its discriminant vanishes to second order. Floating-point solvers drop forward transmitted modes near the optic axes, and the certified solver does not. On exact equivalence classes, learned models on quotient coordinates are invariant and more accurate than models on raw tensors, while learned root-count predicates fail near the optic axes.

Sat 19 SeptComputer Vision and Pattern Recognition
The gist
Light travels differently through certain crystals that bend and split the light waves in complex ways. The authors found a precise mathematical description of the surface that governs this behavior, called the Fresnel wave surface, linking it to special shapes from algebraic geometry. This lets them accurately predict how light splits and refracts inside the crystal, especially near difficult cases where previous approximations failed. Their approach also improves data-driven models by focusing on essential parameters that capture all needed information without distractions.
Open → 2609.23249v1