DE-PSGLD is the first decentralized MCMC sampler for constrained convex domains that converges to a regularized Gibbs distribution with explicit 2-Wasserstein bounds for agents and network averages.
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9 Pith papers cite this work. Polarity classification is still indexing.
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Fractionally supervised classification is extended to maxima nominated samples via a new latent representation of the observed maximum and the unseen set composition, producing a valid EM algorithm and weighted-likelihood procedure.
Split-MoPE integrates split learning with predefined-expert routing to maximize usable data in vertical federated learning under sample misalignment, delivering state-of-the-art accuracy in one communication round plus built-in robustness and per-sample contribution scores.
MANOJAVAM unifies matrix multiplication and SVD for PCA on FPGA with block-streaming systolic arrays and pipelined Jacobi-CORDIC, delivering up to 22.75x SVD speedup and 42.14x lower energy than an NVIDIA A6000 GPU.
HiSS sampling uses logistic bridging in a Metropolis-within-Gibbs framework to enable transitions between disconnected modes while preserving the target discrete distribution.
XL-MIMO systems with analog combining perform OTA classification via ELM framework achieving over 90% accuracy with few ms latency under rich fading.
The thesis presents Pino, an end-to-end pipeline that supervises reinforcement learning agents with argumentation-based normative advisors, introduces an algorithm for automatic argument extraction, and defines a mitigation strategy for norm avoidance.
A smoothing stochastic gradient descent algorithm is introduced for non-smooth stochastic compositional optimization, achieving 1/T^{1/4} rate for convex cases and similar guarantees under other convexity settings.
A unified large deviations analysis is proposed to study acceleration mechanisms in variants of overdamped Langevin Monte Carlo methods, supported by numerical experiments.
citing papers explorer
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Decentralized Proximal Stochastic Gradient Langevin Dynamics
DE-PSGLD is the first decentralized MCMC sampler for constrained convex domains that converges to a regularized Gibbs distribution with explicit 2-Wasserstein bounds for agents and network averages.
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Fractionally Supervised Classification with Maxima Nominated Samples
Fractionally supervised classification is extended to maxima nominated samples via a new latent representation of the observed maximum and the unseen set composition, producing a valid EM algorithm and weighted-likelihood procedure.
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Mixture of Predefined Experts: Maximizing Data Usage on Vertical Federated Learning
Split-MoPE integrates split learning with predefined-expert routing to maximize usable data in vertical federated learning under sample misalignment, delivering state-of-the-art accuracy in one communication round plus built-in robustness and per-sample contribution scores.
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MANOJAVAM: A Scalable, Unified FPGA Accelerator for Matrix Multiplication and Singular Value Decomposition in Principal Component Analysis
MANOJAVAM unifies matrix multiplication and SVD for PCA on FPGA with block-streaming systolic arrays and pipelined Jacobi-CORDIC, delivering up to 22.75x SVD speedup and 42.14x lower energy than an NVIDIA A6000 GPU.
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Slithering Through Gaps: Capturing Discrete Isolated Modes via Logistic Bridging
HiSS sampling uses logistic bridging in a Metropolis-within-Gibbs framework to enable transitions between disconnected modes while preserving the target discrete distribution.
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Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining
XL-MIMO systems with analog combining perform OTA classification via ELM framework achieving over 90% accuracy with few ms latency under rich fading.
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What if Pinocchio Were a Reinforcement Learning Agent: A Normative End-to-End Pipeline
The thesis presents Pino, an end-to-end pipeline that supervises reinforcement learning agents with argumentation-based normative advisors, introduces an algorithm for automatic argument extraction, and defines a mitigation strategy for norm avoidance.
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Non-smooth stochastic gradient descent using smoothing functions
A smoothing stochastic gradient descent algorithm is introduced for non-smooth stochastic compositional optimization, achieving 1/T^{1/4} rate for convex cases and similar guarantees under other convexity settings.
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Accelerating Langevin Monte Carlo Sampling: A Large Deviations Analysis
A unified large deviations analysis is proposed to study acceleration mechanisms in variants of overdamped Langevin Monte Carlo methods, supported by numerical experiments.