We study optimal diagonal preconditioning using the classical worst-case $\kappa$-condition number and the averaging-based $\omega$-condition number.
For the $\kappa$-optimal preconditioning problem, we derive an affine-based pseudoconvex reformulation with three key advantages: all stationary points are global minim...
Saeed Ghadimi, Woosuk L. Jung, Arnesh Sujanani et al.· 0 citations
Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based governors are increasingly inadequate for managing the complexity of heterogeneous System-on-Chip designs and diverse application workloads. Although reinforcement learning approa...
Jinqi Yan, Fang He, Qianlong Sang et al.· 0 citations
A scalable ML framework that efficiently learns from quantum many-body data by leveraging the exponential decay of correlations prevalent in noncritical systems is proposed, and it is rigorously proved that the GLQK substantially improves the sample complexity in the system size $n$ over the existing shadow kernel.
Koki Chinzei, Quoc-Hoan Tran, Norifumi Matsumoto et al.· 2 citations
In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) averaging under Markovian noise. We focus on the version of the algorithm with constant step size and propose a novel decomposition of the bias via a linearization technique. We a...
I. Levin, Alexey Naumov, Sergey Samsonov· AAAI Conference on Artificia...· 2 citations
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In many applications, one needs to learn a dynamical system from its solutions sampled at a finite number of time points. The learning problem is often formulated as an optimization problem over a chosen function class. However, in the optimization procedure, prediction data from generic dynamics requires a numerical i...
Grammatical Error Correction (GEC) and feedback play a vital role in supporting second language (L2) learners, educators, and examiners. While written GEC is well-established, spoken GEC (SGEC), aiming to provide feedback based on learners' speech, poses additional challenges due to disfluencies, transcription errors,...
Mengjie Qian, Rao Ma, Stefano Bann\`o et al.· 0 citations
We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimator that provides upper bounds on the norm and derive probabilistic guarantees on its underestimation. Compared to standard approaches such as the power method, the proposed estim...
Alexey Naumov, Maxim Rakhuba, Denis Ryapolov et al.· 0 citations
Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large...
Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptat...
Konstantinos Skianis, John Pavlopoulos, A. Seza Do\u{g}ru\"oz· 0 citations
In this paper, we introduce a simple yet effective tabular data watermarking mechanism with statistical guarantees. We show theoretically that the proposed watermark can be effectively detected, while faithfully preserving the data fidelity, and also demonstrates appealing robustness against additive noise attack. The...
Hengzhi He, Peiyu Yu, Junpeng Ren et al.· 0 citations
Efficiently tackling multiple tasks within complex environments, such as those found in robot manipulation, remains an ongoing challenge in robotics and an opportunity for data-driven solutions such as reinforcement learning (RL). The high cost of data gathering in robotics encourages data-efficient approaches such as...
Carlos Plou, Ana C. Murillo, Ruben Martinez-Cantin· 0 citations
We consider online scheduling on unrelated (heterogeneous) machines in a speed-oblivious setting, where an algorithm is unaware of the exact job-dependent processing speeds. We show strong impossibility results for clairvoyant and non-clairvoyant algorithms and overcome them in models inspired by practical settings: (i...
Alexander Lindermayr, Nicole Megow, Martin Rapp· 0 citations
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
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