Two meta-learning algorithms are introduced: Meta-Variational Quantum Thermalizer (Meta-VQT) and Neural Network Meta-VQT (NN-Meta VQT) for efficient thermal state preparation of parametrized Hamiltonians on Noisy Intermediate-Scale Quantum (NISQ) devices and a Quantum Boltzmann Machine (QBM) on a 2-qubit Heisenberg mod...
R. V. Bhat, Rahul Bhowmick, Avinash Singh et al.· arXiv.org· 3 citations
We consider solutions to the linear quadratic Gaussian (LQG) regulator problem via policy gradient (PG) methods. Although PG methods have demonstrated strong theoretical guarantees in solving the linear quadratic regulator (LQR) problem, despite its nonconvex landscape, their theoretical understanding in the LQG settin...
Kasra Fallah, Leonardo F. Toso, James Anderson· 0 citations
We investigate cross-domain few-shot learning under the constraint that fine-tuning of backbones (i.e., feature extractors) is impossible or infeasible---a scenario that is increasingly common in practical use cases---and show how representing few-shot classification as a series of multiple instance verification (MIV)...
Score-based methods have recently seen increasing popularity in modeling and generation. Methods have been constructed to perform hypothesis testing and change-point detection with score functions, but these methods are in general not as powerful as their likelihood-based peers. Recent works consider generalizing the s...
Sean Moushegian, Taposh Banerjee, Vahid Tarokh· 0 citations
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This work introduces ShIOEnv, a Gymnasium-compatible Bash environment for synthesizing execution-annotated shell input traces and introduces grammar-constrained synthesis to generate syntactically valid arguments with high input irreducibility, which is estimated through self-ablative contrasts of execution artifacts.
We present the first large-scale open-set benchmark for multilingual audio-video deepfake detection. Our dataset comprises nearly 350 hours of real and fake videos across eight languages, with 59% of data being generated. For each language, the fake videos are generated with several distinct audio and video deepfake ge...
Florinel-Alin Croitoru, Vlad Hondru, Marius Popescu et al.· 0 citations
Contextual bandits with stage-wise constraints model decisions that must control risk while learning from their outcomes. We study this problem when only the selected action's reward and cost are observed, with a general reward class and a convex class of nonlinear cost functions. We propose CS-UCCB, which combines off...
Haichen Hu, David Simchi-Levi, Navid Azizan· 0 citations
While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification tasks, which rely heavily on deep learning. To bridge this gap, we introduce DetoxAI, an open-source Python library for improving fairness in...
Ignacy St\k{e}pka, Lukasz Sztukiewicz, Micha{\l} Wili\'nski et al.· 0 citations
This work evaluates ground-state estimation of 6- to 18-qubit Hamiltonians using the Variational Quantum Eigensolver (VQE) and quantum phase classification for Hamiltonians up to 12 qubits and uses these methods as a warm-start pretraining strategy for Hamiltonians with exponential-dimensional dynamical Lie algebras.
Rahul Bhowmick, Harsh Wadhwa, Avinash Singh et al.· 4 citations
We consider the problem of policy optimization with noisy rewards, where the policy is constrained to be in the neighborhood of a reference policy.We study the risk of the policy optimization process, defined to be the chance of obtaining a policy worse off than the reference policy. In an idealized multivariate Gaussi...
We study clustering for multi-layer networks, where each observation is an undirected network measured on the same set of vertices. Under the mixture multi-layer stochastic block model (MMSBM), each network is generated from one of two stochastic block models, and the goal is to recover the latent network labels. We es...
Language models relying on verbalized Chain-of-Thought (CoT) are constrained to token-by-token generation in the language space, where many tokens serve textual coherence rather than core reasoning. This entangles genuine differences in logical trajectories with superficial variations in verbalization. Moving reasoning...
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.
MIT News · Artificial Intelligence· news.mit.eduOct 28, 2026
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
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