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18,068 papers

#machine learning Preprint Open access Oct 2026

Modified Loss of Momentum Gradient Descent: Fine-Grained Analysis

We analyze gradient descent with Polyak (1964) heavy-ball momentum (HB) whose fixed momentum hyperparameter $\beta \in (0, 1)$ provides exponential decay of memory. Building on Kovachki and Stuart (2021), we prove that on an exponentially attractive invariant manifold the algorithm is exactly plain gradient descent wit...

Matias D. Cattaneo, Boris Shigida · 0 citations
#machine learning Preprint Open access Oct 2026

Causal Posterior Estimation

We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is intractable or computationally expensive, but generating outputs given parameter values is straightforward. CPE approximates the posterior distribution using flow matching...

Simon Dirmeier, Antonietta Mira · 0 citations
#machine learning Preprint Open access Oct 2026

The Utility and Complexity of in- and out-of-Distribution Machine Unlearning

Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge gaps post-deployment. Despite this importance, existing approaches are often heuristic and lack formal guarantees. In this paper, we analyze the fundamental utility, ti...

Youssef Allouah, Joshua Kazdan, Rachid Guerraoui et al. · 0 citations
#machine learning Preprint Oct 2026

Decentralized SGD under Heavy-Tailed Noise: Optimal Convergence Rates and the Role of Gradient Clipping

Heavy-tailed noise has been widely observed in modern machine learning, motivating the use of methods like gradient clipping and normalization. While these methods are well understood in centralized settings, much less is known in decentralized ones, where applying a nonlinearity to local gradients affects both optimiz...

Aleksandar Armacki, H. Cai, Ali H. Sayed · 0 citations
#machine learning Preprint Oct 2026

Rephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action Models

The sensitivity to instruction phrasing is characterized with statistically tested single-edit swings and an oracle phrase search, which shows that phrasing alone nearly closes the 21-point gap between in-distribution and out-of-distribution tasks.

Mikey Watts, Yu-Chen Cui · 0 citations
#machine learning Preprint Open access Oct 2026

Evolutionary Architecture Search for Chlorophyll-$a$ Prediction in Lakes using Sentinel-2

Small tabular datasets with expert-designed spectral features are the norm in operational Earth observation, and the networks applied to them are typically hand-designed. We revisit one such published model -- a Sentinel-2 algal bloom classifier -- and ask what architecture search adds, holding the task, the fe...

Kursat Komurcu, Linas Petkevicius · 0 citations
#machine learning Preprint Oct 2026

Best Arm Identification for Bandits with Shifting Means

We study the best arm identification problem in a stochastic environment with a novel form of adversarial perturbations, which we coin Shifting Means. While classically the mean rewards of the $K$ arms are stable in time, in Shifting Means only the gaps $\boldsymbol{\Delta}$ between mean rewards are stable, while their...

Lukas Zierahn, Wouter M. Koolen, Shubhada Agrawal et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields

Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computation...

Peng Liu, Shaoxiang Qin, Theodore Potsis et al. · 0 citations
#machine learning Preprint Oct 2026

Derivative Gaussian Processes on a Two-Direction Budget

Gradient observations promise more accurate Gaussian process (GP) surrogates, but the cost of incorporating them has long stood in the way of realizing that promise. We propose a derivative GP with a budget of just two directions per observed gradient. One direction focuses on each gradient's direct contribution to tar...

Hyunseok Seung, Matthias Katzfuss · 0 citations
#machine learning Preprint Oct 2026

Steerspeech: Activation Steering For Emotion Control In Generated Speech

SteerSpeech is presented, a lightweight activation-steering framework that controls emotion by injecting steering vectors into hidden activations that encourages monotonic emotion control while preserving speaker identity and linguistic content, constraining steering drift, and keeping the TTS backbone frozen.

A. Benazir, Darius Petermann, Felix Xiaozhu Lin et al. · 0 citations
#machine learning Preprint Oct 2026

RobotWorld: Benchmarking Multimodal Agents for Robot Use Across Diverse Tasks and Embodiments

General-purpose agents increasingly write code, use tools, and complete complex digital tasks, raising the question of how far these capabilities carry into the physical world. To investigate this, we introduce RobotWorld, a challenging simulation testbed for robot use: turning instructions and observations into physic...

Zhiqin Yang, Chen-Xin Li, Xiao-Meng Hu et al. · 2 citations
#machine learning Preprint Oct 2026

Rubix: Global Correspondence-Free Point Set Alignment through Assignment Geometry

The sharp bound of $n(n-1)$ vertices for $n\ge2$ is proved, answering Rote's rotation-assignment open problem, and assignment-based bounds extend the approach to three-dimensional rotations and partial matching at a supplied translation through branch-and-bound.

Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome · 0 citations

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Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

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