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Found 23 papers, 4 papers with code
Optimizing Leaky Private Information Retrieval Codes to Achieve ${O}(\log K)$ Leakage Ratio Exponent
Unlike the previous L-PIR scheme proposed by Samy et al., which only adjusted the probability allocation to the clean (low-cost) retrieval pattern, we optimize the probabilities assigned to all the retrieval patterns jointly.
Path-Guided Particle-based Sampling
We propose a path-guided particle-based sampling~(PGPS) method based on a novel Log-weighted Shrinkage (LwS) density path linking an initial distribution to the target distribution.
Transformers learn variable-order Markov chains in-context
Large language models have demonstrated impressive in-context learning (ICL) capability.
OD-Stega: LLM-Based Near-Imperceptible Steganography via Optimized Distributions
We consider coverless steganography where a Large Language Model (LLM) drives an arithmetic coding decoder to generate stego-texts.
Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model
This framework aims to produce meticulously aligned pseudo thermal images at the pixel level, leveraging edge information extracted from visible images.
Predicting DC-Link Capacitor Current Ripple in AC-DC Rectifier Circuits Using Fine-Tuned Large Language Models
Foundational Large Language Models (LLMs) such as GPT-3. 5-turbo allow users to refine the model based on newer information, known as ``fine-tuning''.
Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector
Large Language Models (LLMs) as chatbots have drawn remarkable attention thanks to their versatile capability in natural language processing as well as in a wide range of tasks.
Provable Policy Gradient Methods for Average-Reward Markov Potential Games
We prove that both algorithms based on independent policy gradient and independent natural policy gradient converge globally to a Nash equilibrium for the average reward criterion.
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection
RGB-Thermal (RGB-T) pedestrian detection aims to locate the pedestrians in RGB-T image pairs to exploit the complementation between the two modalities for improving detection robustness in extreme conditions.
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment.
Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem
We provide a new information-theoretic generalization error bound that is exactly tight (i. e., matching even the constant) for the canonical quadratic Gaussian (location) problem.
Optimization of Cryptocurrency Miners' Participation in Ancillary Service Markets
One of the most significant bottlenecks for the scalable deployment of such computation is its energy demand.
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth concave scalarization), hard constraints (constrained MDP), and max-min trade-off.
Approximate Top-$m$ Arm Identification with Heterogeneous Reward Variances
We study the effect of reward variance heterogeneity in the approximate top-$m$ arm identification setting.
On Top-$k$ Selection from $m$-wise Partial Rankings via Borda Counting
In the special case of $m=2$, i. e., pairwise comparison, the resultant bound is tighter than that given by Shah et al., leading to a reduced gap between the error probability upper and lower bounds.
Policy Optimization for Constrained MDPs with Provable Fast Global Convergence
We propose a new algorithm called policy mirror descent-primal dual (PMD-PD) algorithm that can provably achieve a faster $\mathcal{O}(\log(T)/T)$ convergence rate for both the optimality gap and the constraint violation.
A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy
The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data.
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges
Most of our lives are conducted in the cyberspace.
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
We show that when a strictly safe policy is known, then one can confine the system to zero constraint violation with arbitrarily high probability while keeping the reward regret of order $\tilde{\mathcal{O}}(\sqrt{K})$.
Individually Conditional Individual Mutual Information Bound on Generalization Error
We propose a new information-theoretic bound on generalization error based on a combination of the error decomposition technique of Bu et al. and the conditional mutual information (CMI) construction of Steinke and Zakynthinou.
Train Once, and Decode As You Like
In this paper we propose a unified approach for supporting different generation manners of machine translation, including autoregressive, semi-autoregressive, and refinement-based non-autoregressive models.
Dense Fusion Classmate Network for Land Cover Classification
Recently, FCNs based methods have made great progress in semantic segmentation.
On the Information Leakage in Private Information Retrieval Systems
We further characterize the optimal tradeoff between the minimum amount of common randomness and the total leakage.
