Wild Test-Time Adaptation (WTTA) is proposed to adapt a source model to unseen domains under extreme data scarcity and multiple shifts. Previous approaches mainly focused on sample selection strategies, while overlooking the fundamental problem on underlying optimization. Initially, we critically analyze the widely-adopted entropy minimization framework in WTTA and uncover its significant limitations in noisy optimization dynamics that substantially hinder adaptation efficiency. Through our analysis, we identify region confidence as a superior alternative to traditional entropy, however, its direct optimization remains computationally prohibitive for real-time applications. In this paper, we introduce a novel region-integrated method **ReCAP** that bypasses the lengthy process. Specifically, we propose a probabilistic region modeling scheme that flexibly captures semantic changes in embedding space. Subsequently, we develop a finite-to-infinite asymptotic approximation that transforms the intractable region confidence into a tractable and upper-bounded proxy. These innovations significantly unlock the overlooked potential dynamics in local region in a concise solution. Our extensive experiments demonstrate the consistent superiority of ReCAP over existing methods across various datasets and wild scenarios. The source code will be available at https://github.com/hzcar/ReCAP.

Points-based rewards programs are a prevalent model to incentivize customer loyalty; in these programs, customers who make repeated purchases from a seller accumulate points, working toward eventual redemption of a free reward. If implemented correctly, they are known to increase revenue for the seller. However, these programs have come under scrutiny of late due to accusations of unfair practices in their implementation. Motivated by these real-world concerns, this paper studies the problem of fairly designing points-based rewards programs, with a special focus on two major obstacles that put fairness at odds with their effectiveness: (i) the incentive to exploit customer heterogeneity by personalizing programs to customers' purchase behavior, and (ii) risks of devaluing customers' previously earned points when sellers need to experiment in uncertain environments. To study this problem, we focus on the popular "Buy N, Get One Free" (BNGO) rewards programs. We first show that the optimal individually fair program that uses the same redemption threshold for all customers suffers from a constant factor loss in revenue of at most 1 + ln 2, compared to the optimal personalized strategy which may unfairly offer different customers different thresholds. We then tackle the problem of designing temporally fair learning algorithms in the presence of demand uncertainty. Toward this goal, we design a "stable" learning algorithm that limits the risk of point devaluation due to experimentation by only changing the redemption threshold O(log T) times, over a learning horizon of length T. We prove that this algorithm incurs [EQUATION] regret in expectation; this guarantee is optimal, up to polylogarithmic factors. We then modify this algorithm to ever only decrease redemption thresholds, leading to improved fairness at a cost of only a constant factor in regret. Finally, we conduct extensive numerical experiments to show the limited value of personalization in average-case settings, in addition to demonstrating the strong practical performance of our proposed learning algorithms.

The remarkable success of “pretrain-then-finetune” paradigm has led to a proliferation of available pre-trained models for vision tasks. This surge presents a significant challenge in efficiently choosing the most suitable pre-trained models for downstream tasks. The critical aspect of this challenge lies in effectively predicting the model transferability by considering the underlying fine-tuning dynamics. Existing methods often model fine-tuning dynamics in feature space with linear transformations, which do not precisely align with the fine-tuning objective and fail to grasp the essential nonlinearity from optimization. To this end, we present LEAD, a finetuning-aligned approach based on the network output of logits. LEAD proposes a theoretical framework to model the optimization process and derives an ordinary differential equation (ODE) to depict the nonlinear evolution toward the final logit state. Additionally, we design a class-aware decomposition method to consider the varying evolution dynamics across classes and further ensure practical applicability. Integrating the closely aligned optimization objective and nonlinear modeling capabilities derived from the differential equation, our method offers a concise solution to effectively bridge the optimization gap in a single step, bypassing the lengthy fine-tuning process. The comprehensive experiments on 24 supervised and self-supervised pre-trained models across 10 downstream datasets demonstrate impressive performances and showcase its broad adaptability even in low-data scenarios.

Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients and thereby improve average-cost performance. An example is the stochastic shortest path problem with random edge costs (e.g., traffic) and contextual features (e.g., lagged traffic, weather). Existing work on CLO assumes the data has fully observed cost coefficient vectors, but in many applications, we can only see the realized cost of a historical decision, that is, just one projection of the random cost coefficient vector, to which we refer as bandit feedback. We study a class of offline learning algorithms for CLO with bandit feedback, which we term induced empirical risk minimization (IERM), where we fit a predictive model to directly optimize the downstream performance of the policy it induces. We show a fast-rate regret bound for IERM that allows for misspecified model classes and flexible choices of the optimization estimate, and we develop computationally tractable surrogate losses. A byproduct of our theory of independent interest is fast-rate regret bound for IERM with full feedback and misspecified policy class. We compare the performance of different modeling choices numerically using a stochastic shortest path example and provide practical insights from the empirical results.

Few-shot named entity recognition is extremely important for the domain lacking annotation data. Existing approaches ignore that class descriptions can provide additional information and rich prior knowledge for the model. Most datasets do not provide class description information, and the same entity class may have different definitions in different datasets. In this paper, we proposed a simple and effective method, i.e., AutoDes, that can extract class descriptions from annotated data automatically. AutoDes uses examples to construct class descriptions and takes the prediction words of entity-oriented prompts as candidate examples. Experiments with different few-shot settings on multiple datasets show that AutoDes is superior to the state-of-the-art methods in low-resource settings, improving F1 scores by 1.2% to 7.5% absolute points.

We study the regret of reinforcement learning from offline data generated by a fixed behavior policy in an infinite-horizon discounted Markov decision process (MDP). While existing analyses of common approaches, such as fitted $Q$-iteration (FQI), suggest a $O(1/\sqrt{n})$ convergence for regret, empirical behavior exhibits much faster convergence. In this paper, we present a finer regret analysis that exactly characterized this phenomenon by providing fast rates for the regret convergence. First, we show that given any estimate for the optimal quality function $Q^*$, the regret of the policy it defines converges at a rate given by the exponentiation of the $Q^*$-estimate’s pointwise convergence rate, thus speeding it up. The level of exponentiation depends on the level of noise in the decision-making problem, rather than the estimation problem. We establish such noise levels for linear and tabular MDPs as examples. Second, we provide new analyses of FQI and Bellman residual minimization to establish the correct pointwise convergence guarantees. As specific cases, our results imply $O(1/n)$ regret rates in linear cases and $\exp(-\Omega(n))$ regret rates in tabular cases.

We study a nonparametric contextual bandit problem where the expected reward functions belong to a Hölder class with smoothness parameter $\beta$. We show how this interpolates between two extremes that were previously studied in isolation: non-differentiable bandits ($\beta\leq1$), where rate-optimal regret is achieved by running separate non-contextual bandits in different context regions, and parametric-response bandits (satisfying $\beta=\infty$), where rate-optimal regret can be achieved with minimal or no exploration due to infinite extrapolatability. We develop a novel algorithm that carefully adjusts to all smoothness settings and we prove its regret is rate-optimal by establishing matching upper and lower bounds, recovering the existing results at the two extremes. In this sense, our work bridges the gap between the existing literature on parametric and non-differentiable contextual bandit problems and between bandit algorithms that exclusively use global or local information, shedding light on the crucial interplay of complexity and regret in contextual bandits.

People of different ages are not tolerant to the same distance. Existing green space accessibility researches are often based on distance measurement, and less consideration is given to differences between different groups of people. We proposed a revised Gaussian 2-Step Floating Catchment Area Method (G2SFCAM) with age information considered to evaluate the spatial accessibility to urban green space at an administrative grid level. Precise population data of residential building scale and actual road network were used for a detailed evaluation. This Age-based 2SFCAM Model was tested in Yichang, China. Both results derived using G2SFCAM and Age-based 2SFCAM were compared. Results indicate that the proposed Age-based 2SFCAM method shows a more realistic estimation and reflects spatial differentiation of green space accessibility. This research helps balancing the rights of people of all ages to access the urban green spaces, and thus promotes health equality and environmental justice in urban planning and management.