Search Results for author:
Found 13 papers, 1 papers with code
Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem
We further show that although the conditional bounding and the reference distribution can make the bound exactly tight, removing them does not significantly degrade the bound, which leads to a mutual-information-based bound that is also asymptotically tight in this setting.
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.
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.
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.
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.
