33 hasil untuk "Pokutta, Sebastian"
Menampilkan 1–20 · 0,56 detik · RSS
-
Preprint Teks lengkap
Frank-Wolfe Beyond 1/t Convergence
We consider smooth convex minimization over compact convex sets, i.e., $\min_{x \in C} f(x)$ with the (vanilla) Frank-Wolfe algorithm. Well-known lower bounds establish a worst-case $Ω(1/t)$ primal-gap barrier in the general smooth convex case, and faster convergence usually requires favorable…
-
Preprint Teks lengkap
Fast Frank--Wolfe Algorithms with Adaptive Bregman Step-Size for Weakly Convex Functions
We propose Frank--Wolfe (FW) algorithms with an adaptive Bregman step-size strategy for smooth adaptable (also called: relatively smooth) (weakly-) convex functions. This means that the gradient of the objective function is not necessarily Lipschitz continuous, and we only require the smooth…
-
Preprint Teks lengkap
Better bounds on finite-order Grothendieck constants
Grothendieck constants $K_G(d)$ bound the advantage of $d$-dimensional strategies over $1$-dimensional ones in a specific optimisation task. They have applications ranging from approximation algorithms to quantum nonlocality. However, apart from $d=2$, their values are unknown. Here, we exploit a…
-
Preprint Teks lengkap
Tensor Decomposition for Non-Clifford Gate Minimization
Fault-tolerant quantum computation requires minimizing non-Clifford gates, whose implementation via magic state distillation dominates the resource costs. While $T$-count minimization is well-studied, dedicated $CCZ$ factories shift the natural target to direct Toffoli minimization. We develop…
-
Preprint Teks lengkap
Alternating Linear Minimization: Revisiting von Neumann's alternating projections
In 1933 von Neumann proved a beautiful result that one can approximate a point in the intersection of two convex sets by alternating projections, i.e., successively projecting on one set and then the other. This algorithm assumes that one has access to projection operators for both sets. In this…
-
Preprint Teks lengkap
Norm-induced Cuts: Outer Approximation for Lipschitzian Constraint Functions
In this paper, we consider a finite-dimensional optimization problem minimizing a continuous objective on a compact domain subject to a multi-dimensional constraint function. For the latter, we assume the availability of a global Lipschitz constant. In recent literature, methods based on non-convex…
-
Preprint Teks lengkap
A Free Lunch in LLM Compression: Revisiting Retraining after Pruning
Post-training pruning substantially reduces inference costs but often causes severe quality degradation without adapting the remaining weights. For LLMs, such retraining is commonly considered impractical due to large computational costs, motivating increasingly sophisticated pruning criteria to…
-
Preprint Teks lengkap
The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday research practice. This paper is a practical guide to AI-assisted research in mathematics and machine learning: We discuss…
-
Preprint Teks lengkap
Investigating the Influence of Spatial Ability in Augmented Reality-assisted Robot Programming
Augmented Reality (AR) offers promising opportunities to enhance learning, but its mechanisms and effects are not yet fully understood. As learning becomes increasingly personalized, considering individual learner characteristics becomes more important. This study investigates the moderating effect…
-
Preprint Teks lengkap
The Good, the Bad and the Ugly: Meta-Analysis of Watermarks, Transferable Attacks and Adversarial Defenses
We formalize and analyze the trade-off between backdoor-based watermarks and adversarial defenses, framing it as an interactive protocol between a verifier and a prover. While previous works have primarily focused on this trade-off, our analysis extends it by identifying transferable attacks as a…
-
Preprint Teks lengkap
FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets
The increasing heterogeneity of student populations poses significant challenges for teachers, particularly in mathematics education, where cognitive, motivational, and emotional differences strongly influence learning outcomes. While AI-driven personalization tools have emerged, most remain…
-
Preprint Teks lengkap
Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training
Augmented Reality (AR) offers powerful visualization capabilities for industrial robot training, yet current interfaces remain predominantly static, failing to account for learners' diverse cognitive profiles. In this paper, we present an AR application for robot training and propose a multi-agent…
-
Artikel Jurnal Teks lengkap
Feature Engineering for the Prediction of Scoliosis in 5q‐Spinal Muscular Atrophy
BACKGROUND: 5q‐Spinal muscular atrophy (SMA) is now one of the 5% treatable rare diseases worldwide. As disease‐modifying therapies alter disease progression and patient phenotypes, paediatricians and consulting disciplines face new unknowns in their treatment decisions. Conclusions made from…
-
Preprint Teks lengkap
Efficient Quadratic Corrections for Frank-Wolfe Algorithms
We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including blended conditional gradients, blended pairwise conditional gradients, and fully-corrective Frank-Wolfe. For this, we prove tight convergence guarantees together with an optimal face identification…
-
Preprint Teks lengkap
SparseSwaps: Tractable LLM Pruning Mask Refinement at Scale
The resource requirements of neural networks can be significantly reduced through pruning - the removal of seemingly less important parameters. However, for LLMs, full retraining to recover pruning-induced performance degradation is often prohibitive and classical approaches such as magnitude…
-
Preprint Teks lengkap
From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs
We investigate the extent to which an LLM's hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight instruction-tuned transformer models, we run two experimental paradigms -- similarity-based forced choice and free association -- over a shared…
-
Preprint Teks lengkap
Global Optimization for Combinatorial Geometry Problems Revisited in the Era of LLMs
Recent progress in LLM-driven algorithm discovery, exemplified by DeepMind's AlphaEvolve, has produced new best-known solutions for a range of hard geometric and combinatorial problems. This raises a natural question: to what extent can modern off-the-shelf global optimization solvers match such…
-
Preprint Teks lengkap
Stable Personas: Dual-Assessment of Temporal Stability in LLM-Based Human Simulation
Large Language Models (LLMs) acting as artificial agents offer the potential for scalable behavioral research, yet their validity depends on whether LLMs can maintain stable personas across extended conversations. We address this point using a dual-assessment framework measuring both self-reported…
-
Preprint Teks lengkap
Scenario Reduction for Distributionally Robust Optimization
Stochastic and (distributionally) robust optimization problems often become computationally challenging as the number of scenarios or data points increases. Scenario reduction is therefore a key technique for improving tractability. We introduce a general scenario reduction method for…
-
Preprint Teks lengkap
Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings
While Prover-Verifier Games (PVGs) offer a promising path toward verifiability in nonlinear classification models, they have not yet been applied to complex inputs such as high-dimensional images. Conversely, expressive concept encodings effectively allow to translate such data into interpretable…