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Found 9 papers, 0 papers with code
Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
This paper proposes an extension of variational inference (VI)-based Bayesian learning that integrates calibration regularization for improved ID performance, confidence minimization for OOD detection, and selective calibration to ensure a synergistic use of calibration regularization and confidence minimization.
Shortcut-connected Expert Parallelism for Accelerating Mixture-of-Experts
Expert parallelism has been introduced as a strategy to distribute the computational workload of sparsely-gated mixture-of-experts (MoE) models across multiple computing devices, facilitating the execution of these increasingly large-scale models.
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation
To tackle long planning horizon problems in reinforcement learning with general function approximation, we propose the first algorithm, termed as UCRL-WVTR, that achieves both \emph{horizon-free} and \emph{instance-dependent}, since it eliminates the polynomial dependency on the planning horizon.
Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds
Our algorithm, termed as \textsc{Heavy-LSVI-UCB}, achieves the \emph{first} computationally efficient \emph{instance-dependent} $K$-episode regret of $\tilde{O}(d \sqrt{H \mathcal{U}^*} K^\frac{1}{1+\epsilon} + d \sqrt{H \mathcal{V}^* K})$.
Calibration-Aware Bayesian Learning
Deep learning models, including modern systems like large language models, are well known to offer unreliable estimates of the uncertainty of their decisions.
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
Despite a large amount of effort in dealing with heavy-tailed error in machine learning, little is known when moments of the error can become non-existential: the random noise $\eta$ satisfies Pr$\left[|\eta| > |y|\right] \le 1/|y|^{\alpha}$ for some $\alpha > 0$.
Continual Learning Approach for Improving the Data and Computation Mapping in Near-Memory Processing System
Along with NMP and memory system development, the mapping for placing data and guiding computation in the memory-cube network has become crucial in driving the performance improvement in NMP.
Toward Taming the Overhead Monster for Data-Flow Integrity
Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks.
Hardware Architecture
Coloring Big Graphs with AlphaGoZero
We show that recent innovations in deep reinforcement learning can effectively color very large graphs -- a well-known NP-hard problem with clear commercial applications.
