This paper describes an adaptive duo-binary four-level pulse amplitude modulation (Duo-PAM4) detector that significantly reduces the bit error rate (BER) of the conventional wireline transceivers under high insertion loss (IL) channels. The parallel maximum likelihood sequence detection (MLSD) combined with parallel feed-forward equalization (FFE) is proposed to generate, equalize, and detect Duo-PAM4 signals, thus reducing BER compared to conventional decision feedback equalizer (DFE) and slicers. The proposed reduced branch MLSD reduces power consumption compared to MLSD. An improved delay zero-forcing algorithm for Duo-PAM4 is proposed to achieve fast convergence of the FFE tap coefficients, reducing convergence time by up to 72.5% compared to conventional ZF algorithms for Duo-PAM4. Both the proposed and conventional detectors are implemented in a 28-nm CMOS process at 56 Gb/s and 38-dB insertion loss. The FFE+MLSD and FFE+RB-MLSD reduce the BER by two orders of magnitude compared to conventional FFE+DFE+slicer. The RB-MLSD reduces power consumption by 21.1% compared to conventional MLSD. The detector can be easily migrated to 112Gb/s or 224Gb/s transceivers.

To solve the problem of high bit error rate (BER) due to high inter-symbol interference (ISI) in high-speed wireline transceivers, this paper proposes a low-complexity adaptive reduced-state sequence detector (ARSSD). The detector is based on the maximum likelihood sequence detection (MLSD) to reduce the detection bit error rate (BER), adopts the ISI parameter acquisition method based on the zero-forcing algorithm to achieve the adaptive detector parameters, and combines the viterbi algorithm and the set partitioning algorithm to reduce the complexity of operations. The behavioral simulation and the implementation of the hardware circuit are completed in this paper. The experimental results based on the analog front-end and the field programmable gate array (FPGA) show that when the pulse amplitude modulation 4 (PAM4) bit rate is 12 ∼ 56Gbps and the channel loss is -5dB ∼ -17dB@14GHz, the detection BERs of 32x4 parallel ARSSDs are reduced by two orders of magnitude compared to the conventional decision feedback equalization, which is consistent with the results of the behavioral simulation.

Bi-level optimizaiton (BO) has become a fundamental mathematical framework for addressing hierarchical machine learning problems. As deep learning models continue to grow in size, the demand for scalable bi-level optimization has become increasingly critical. Traditional gradient-based bi-level optimizaiton algorithms, due to their inherent characteristics, are ill-suited to meet the demands of large-scale applications. In this paper, we introduce **F**orward **G**radient **U**nrolling with **F**orward **G**radient, abbreviated as **$($FG$)^2$U**, which achieves an unbiased stochastic approximation of the meta gradient for bi-level optimizaiton. $($FG$)^2$U circumvents the memory and approximation issues associated with classical bi-level optimizaiton approaches, and delivers significantly more accurate gradient estimates than existing large-scale bi-level optimizaiton approaches. Additionally, $($FG$)^2$U is inherently designed to support parallel computing, enabling it to effectively leverage large-scale distributed computing systems to achieve significant computational efficiency. In practice, $($FG$)^2$U and other methods can be strategically placed at different stages of the training process to achieve a more cost-effective two-phase paradigm. Further, $($FG$)^2$U is easy to implement within popular deep learning frameworks, and can be conveniently adapted to address more challenging zeroth-order bi-level optimizaiton scenarios. We provide a thorough convergence analysis and a comprehensive practical discussion for $($FG$)^2$U, complemented by extensive empirical evaluations, showcasing its superior performance in diverse large-scale bi-level optimizaiton tasks.

Learning an optimal policy from offline data is notoriously challenging, which requires the evaluation of the learning policy using data pre-collected from a static logging policy. We study the policy optimization problem in offline contextual bandits using policy gradient methods. We employ a distributionally robust policy gradient method, DROPO, to account for the distributional shift between the static logging policy and the learning policy in policy gradient. Our approach conservatively estimates the conditional reward distributional and updates the policy accordingly. We show that our algorithm converges to a stationary point with rate $O(1/T)$, where $T$ is the number of time steps. We conduct experiments on real-world datasets under various scenarios of logging policies to compare our proposed algorithm with baseline methods in offline contextual bandits. We also propose a variant of our algorithm, DROPO-exp, to further improve the performance when a limited amount of online interaction is allowed. Our results demonstrate the effectiveness and robustness of the proposed algorithms, especially under heavily biased offline data.

In this letter, we present a differentially private algorithm which accurately estimates the mean of an underlying population with given cumulative distribution function. Our algorithm outperforms the former algorithms in two aspects. First, our algorithm is capably of handling more general types of probability distributions, possibly with a very heavy tail. Second, for light-tailed distributions, our algorithm achieves a better level of accuracy with fewer samples.