Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of the world between these extremes. In this work we establish novel regret bounds for online convex optimization in a setting that interpolates between stochastic i.i.d. and fully adversarial losses. By exploiting smoothness of the expected losses, these bounds replace a dependence on the maximum gradient length by the variance of the gradients, which was previously known only for linear losses. In addition, they weaken the i.i.d. assumption by allowing, for example, adversarially poisoned rounds, which were previously considered in the related expert and bandit settings. In the fully i.i.d. case, our regret bounds match the rates one would expect from results in stochastic acceleration, and we also recover the optimal stochastically accelerated rates via online-to-batch conversion. In the fully adversarial case our bounds gracefully deteriorate to match the minimax regret. We further provide lower bounds showing that our regret upper bounds are tight for all intermediate regimes in terms of the stochastic variance and the adversarial variation of the loss gradients.

Open-ended learning methods that automatically generate a curriculum of increasingly challenging tasks serve as a promising avenue toward generally capable reinforcement learning agents. Existing methods adapt curricula independently over either environment parameters (in single-agent settings) or co-player policies (in multi-agent settings). However, the strengths and weaknesses of co-players can manifest themselves differently depending on environmental features. It is thus crucial to consider the dependency between the environment and co-player when shaping a curriculum in multi-agent domains. In this work, we use this insight and extend Unsupervised Environment Design (UED) to multi-agent environments. We then introduce Multi-Agent Environment Design Strategist for Open-Ended Learning (MAESTRO), the first multi-agent UED approach for two-player zero-sum settings. MAESTRO efficiently produces adversarial, joint curricula over both environments and co-players and attains minimax-regret guarantees at Nash equilibrium. Our experiments show that MAESTRO outperforms a number of strong baselines on competitive two-player games, spanning discrete and continuous control settings.

In recent years, there has been a flurry of research focusing on the fairness of machine learning models, and in particular on quantifying and eliminating bias against protected subgroups. One line of work generalizes the notion of protected subgroups beyond simple discrete classes by introducing the notion of a "rich subgroup", and seeks to train models that are calibrated or equalize error rates with respect to these richer subgroup classes. Largely orthogonally, local model explanation methods have been developed that given a classifier h and test point x, attribute influence for the prediction h(x) to the individual features of x. This raises a natural question: Do local model explanation methods attribute different feature importance values on average across different protected subgroups, and can we detect these disparities efficiently? If the model places high weight on a given feature in a specific protected subgroup, but not on the dataset overall (or vice versa), this could be a potential indicator of bias in the predictive model or the underlying data generating process, and is at the very least a useful diagnostic that signals the need for a domain expert to delve deeper. In this paper, we formally introduce the notion of feature importance disparity (FID) in the context of rich subgroups, design oracle-efficent algorithms to identify large FID subgroups, and conduct a thorough empirical analysis that establishes auditing for FID as an important method to investigate dataset bias. Our experiments show that across 4 datasets and 4 common feature importance methods our algorithms find (feature, subgroup) pairs that simultaneously: (i) have subgroup feature importance that is often an order of magnitude different than the importance on the dataset as a whole (ii) generalize out of sample, and (iii) yield interesting discussions about potential bias inherent in these datasets.

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle this issue by implementing explainable AI methods for black-box neural networks. This work focuses on the context of online and blended learning and the use case of student success prediction models. We use a pairwise study design, enabling us to investigate controlled differences between pairs of courses. Our analyses cover five course pairs that differ in one educationally relevant aspect and two popular instance-based explainable AI methods (LIME and SHAP). We quantitatively compare the distances between the explanations across courses and methods. We then validate the explanations of LIME and SHAP with 26 semi-structured interviews of university-level educators regarding which features they believe contribute most to student success, which explanations they trust most, and how they could transform these insights into actionable course design decisions. Our results show that quantitatively, explainers significantly disagree with each other about what is important, and qualitatively, experts themselves do not agree on which explanations are most trustworthy. All code, extended results, and the interview protocol are provided at https://github.com/epfl-ml4ed/trusting-explainers.

The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new cloud services puts higher resource management requirements. This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system. Based on a robust decomposition forecasting algorithm and performance training model, AHPA offers an optimal pod number adjustment plan that could reduce POD resources and maintain business stability. Since being deployed in April 2021, this system has expanded to multiple customer scenarios, including logistics, social networks, AI audio and video, e-commerce, etc. Compared with the previous algorithms, AHPA solves the elastic lag problem, increasing CPU usage by 10% and reducing resource cost by more than 20%. In addition, AHPA can automatically perform flexible planning according to the predicted business volume without manual intervention, significantly saving operation and maintenance costs.

Rehearsal, retraining on a stored small data subset of old tasks, has been proven effective in solving catastrophic forgetting in continual learning. However, due to the sampled data may have a large bias towards the original dataset, retraining them is susceptible to driving continual domain drift of old tasks in feature space, resulting in forgetting. In this paper, we focus on tackling the continual domain drift problem with centroid distance distillation. First, we propose a centroid caching mechanism for sampling data points based on constructed centroids to reduce the sample bias in rehearsal. Then, we present a centroid distance distillation that only stores the centroid distance to reduce the continual domain drift. The experiments on four continual learning datasets show the superiority of the proposed method, and the continual domain drift can be reduced.

Uplift modeling aims to estimate the treatment effect on individuals, widely applied in the e-commerce platform to target persuadable customers and maximize the return of marketing activities. Among the existing uplift modeling methods, tree-based methods are adept at fitting increment and generalization, while neural-network-based models excel at predicting absolute value and precision, and these advantages have not been fully explored and combined. Also, the lack of counterfactual sample pairs is the root challenge in uplift modeling. In this paper, we proposed an uplift modeling framework based on Knowledge Distillation and Sample Matching (KDSM). The teacher model is the uplift decision tree (UpliftDT), whose structure is exploited to construct counterfactual sample pairs, and the pairwise incremental prediction is treated as another objective for the student model. Under the idea of multitask learning, the student model can achieve better performance on generalization and even surpass the teacher. Extensive offline experiments validate the universality of different combinations of teachers and student models and the superiority of KDSM measured against the baselines. In online A/B testing, the cost of each incremental room night is reduced by 6.5\%.

Model distillation (Hinton et al., 2015) is a common framework for knowledge transfer, where knowledge learned by a "teacher model" is transferred to a "student model" via the teacher's predictions. Distillation is helpful because the teacher's predictions are a more useful guide for the student model than hard labels; this phenomenon has been explained by the teacher's predictions containing some useful "dark knowledge". Variants of model distillation have been proposed for, e.g., model compression (Hinton et al., 2015; Ba & Caruana, 2014; Polino et al., 2018; Kim et al., 2018; Sun et al., 2019) or training more accurate models (Zagoruyko & Komodakis, 2016; Xie et al., 2020). Within the privacy-preserving machine learning community, distillation has been adapted to protect the privacy of a training dataset (Papernot et al., 2016; Tang et al., 2022; Shejwalkar & Houmansadr, 2021; Mazzone et al., 2022). Many of these approaches rely on the intuition that distilling the teacher model serves as a privacy barrier that protects the teacher's training data. Informally, restricting the student to learn only from the teacher's predictions is a form of data minimization, which should result in less private information being fed into, and memorized by, the student. This privacy barrier around the teacher also allows the teacher model to be trained with strong, non-private, training approaches, improving both the teacher model's and student model's accuracy. Because model distillation does not provide a rigorous privacy guarantee (such as those offered by differential privacy (Dwork et al., 2006)), in our work we evaluate the empirical privacy provided by these

Chain-of-thought (CoT) prompting enables large language models (LLMs) to solve complex reasoning tasks by generating an explanation before the final prediction. Despite it's promising ability, a critical downside of CoT prompting is that the performance is greatly affected by the factuality of the generated explanation. To improve the correctness of the explanations, fine-tuning language models with explanation data is needed. However, there exists only a few datasets that can be used for such approaches, and no data collection tool for building them. Thus, we introduce CoTEVer, a tool-kit for annotating the factual correctness of generated explanations and collecting revision data of wrong explanations. Figure 1: Example of Explanation Verification and Answer Furthermore, we suggest several use cases Verification of GPT-3's output. Explanation Verification where the data collected with CoTEVer can requires additional knowledge which makes it be utilized for enhancing the faithfulness of hard for annotators to intuitively write a revised explanation explanations. Our toolkit is publicly available and answer.

Reward and representation learning are two long-standing challenges for learning an expanding set of robot manipulation skills from sensory observations. Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question. We introduce $\textbf{V}$alue-$\textbf{I}$mplicit $\textbf{P}$re-training (VIP), a self-supervised pre-trained visual representation capable of generating dense and smooth reward functions for unseen robotic tasks. VIP casts representation learning from human videos as an offline goal-conditioned reinforcement learning problem and derives a self-supervised dual goal-conditioned value-function objective that does not depend on actions, enabling pre-training on unlabeled human videos. Theoretically, VIP can be understood as a novel implicit time contrastive objective that generates a temporally smooth embedding, enabling the value function to be implicitly defined via the embedding distance, which can then be used to construct the reward for any goal-image specified downstream task. Trained on large-scale Ego4D human videos and without any fine-tuning on in-domain, task-specific data, VIP's frozen representation can provide dense visual reward for an extensive set of simulated and $\textbf{real-robot}$ tasks, enabling diverse reward-based visual control methods and significantly outperforming all prior pre-trained representations. Notably, VIP can enable simple, $\textbf{few-shot}$ offline RL on a suite of real-world robot tasks with as few as 20 trajectories.