Dynamic pricing algorithms typically assume continuous price variables, which may not reflect real-world scenarios where prices are often discrete. This paper demonstrates that leveraging discrete price information within a semi-parametric model can substantially improve performance, depending on the size of the support set of the price variable relative to the time horizon. Specifically, we propose a novel semi-parametric contextual dynamic pricing algorithm, namely BayesCoxCP, based on a Bayesian approach to the Cox proportional hazards model. Our theoretical analysis establishes high-probability regret bounds that adapt to the sparsity level $\gamma$, proving that our algorithm achieves a regret upper bound of $\widetilde{O}(T^{(1+\gamma)/2}+\sqrt{dT})$ for $\gamma < 1/3$ and $\widetilde{O}(T^{2/3}+\sqrt{dT})$ for $\gamma \geq 1/3$, where $\gamma$ represents the sparsity of the price grid relative to the time horizon $T$. Through numerical experiments, we demonstrate that our proposed algorithm significantly outperforms an existing method, particularly in scenarios with sparse discrete price points.

Test-time adaptation (TTA) enhances the zero-shot robustness under distribution shifts by leveraging unlabeled test data during inference. Despite notable advances, several challenges still limit its broader applicability. First, most methods rely on backpropagation or iterative optimization, which limits scalability and hinders real-time deployment. Second, they lack explicit modeling of class-conditional feature distributions. This modeling is crucial for producing reliable decision boundaries and calibrated predictions, but it remains underexplored due to the lack of both source data and supervision at test time. In this paper, we propose ADAPT, an Advanced Distribution-Aware and backPropagation-free Test-time adaptation method. We reframe TTA as a probabilistic inference task by modeling class-conditional likelihoods using gradually updated class means and a shared covariance matrix. This enables closed-form, training-free inference. To correct potential likelihood bias, we introduce lightweight regularization guided by CLIP priors and a historical knowledge bank. ADAPT requires no source data, no gradient updates, and no full access to target data, supporting both online and transductive settings. Extensive experiments across diverse benchmarks demonstrate that our method achieves state-of-the-art performance under a wide range of distribution shifts with superior scalability and robustness.

Contextual dynamic pricing is a problem of setting prices based on current contextual information and previous sales history to maximize revenue. A popular approach is to postulate a distribution of customer valuation as a function of contextual information and the baseline valuation. A semi-parametric setting, where the context effect is parametric and the baseline is nonparametric, is of growing interest due to its flexibility. A challenge is that customer valuation is almost never observable in practice and is instead type-I interval censored by the offered price. To address this challenge, we propose a novel semi-parametric contextual pricing algorithm for stochastic contexts, called the epoch-based Cox proportional hazards Contextual Pricing (CoxCP) algorithm. To our best knowledge, our work is the first to employ the Cox model for customer valuation. The CoxCP algorithm has a high-probability regret upper bound of $\tilde{O}( T^{\frac{2}{3}}d )$, where $T$ is the length of horizon and $d$ is the dimension of context. In addition, if the baseline is known, the regret bound can improve to $O( d \log T )$ under certain assumptions. We demonstrate empirically the proposed algorithm performs better than existing semi-parametric contextual pricing algorithms when the model assumptions of all algorithms are correct.

With the increasing adoption of electronic health records, there is an increasing interest in developing individualized treatment rules, which recommend treatments according to patients' characteristics, from large observational data. However, there is a lack of valid inference procedures for such rules developed from this type of data in the presence of high-dimensional covariates. In this work, we develop a penalized doubly robust method to estimate the optimal individualized treatment rule from high-dimensional data. We propose a split-and-pooled de-correlated score to construct hypothesis tests and confidence intervals. Our proposal adopts the data splitting to conquer the slow convergence rate of nuisance parameter estimations, such as non-parametric methods for outcome regression or propensity models. We establish the limiting distributions of the split-and-pooled de-correlated score test and the corresponding one-step estimator in high-dimensional setting. Simulation and real data analysis are conducted to demonstrate the superiority of the proposed method.

Large SRAMs are the practical bottleneck to achieve a low supply voltage, because they suffer from process variation-induced bit errors at a low supply voltage. In this paper, we present an error-resilient cache architecture that resolves the drawback of previous approaches, i.e., the performance degradation at a low supply voltage which is caused by cache misses in accesses to faulty resources. We utilize cache access locality and error-free resources in a cost-effective manner. First, we classify cache lines into fully and partially accessed groups and apply appropriate methods to each group. For the partially accessed group, we propose a method of matching memory access behavior and error locations with intra-cache line word-level remapping. In order to reduce the area overhead used to store the cache access information history, we present an access pattern-learning line-fill buffer (LFB). For the fully accessed group, we propose the utilization of error-free assist functions in the cache, i.e., a LFB and victim cache with no process variation-induced error at the target minimum supply voltage. We also present an error-aware prefetch method that allows us to utilize the error-free victim cache to achieve a further reduction in cache misses due to faulty resources. Experimental results show that the proposed method gives an average 32.6% reduction in cycles per instruction at an error rate of 0.2% with a small area overhead of 8.2%.

Cache is a roadblock towards low supply voltage (Vcc). It is mainly because low Vcc incurs process variation-induced bit errors in large SRAM in cache. Existing approaches for low Vcc cache suffer from low performance due to reduced effective capacity, long latency to correct errors, and increased misses due to accesses to faulty words. In our work, we propose a word-level sub-block disable-based method which increases the utilization of available cache capacity. Our key idea is to minimize accesses to faulty words. To do that, we propose utilizing access behavior history in allocating cache resource with faulty words. In addition, we propose remapping cache words inside of cache line in order to better match both access and error patterns. Experimental results show that the proposed method gives average 21.8% (up to 34.0%) performance improvement with a small area overhead in L1 and L2 caches.