When you purchase through links in our articles, we may earn a small commission. Claude Dispatch is the future. Claude's remote-control Dispatch feature came to the rescue after a missed automation, while also taking a big bite of my usage allowance. Every once in awhile, I get a "whoa, that was cool" moment from AI, and I got one of those while enlisting help from Claude to address a minor speed bump at work. I also got a surprise when checking my Claude usage meter after the excitement was over.

In sparse linear bandits, a learning agent sequentially selects an action and receive reward feedback, and the reward function depends linearly on a few coordinates of the covariates of the actions. This has applications in many real-world sequential decision making problems. In this paper, we propose a simple and computationally efficient sparse linear estimation method called POPART that enjoys a tighter ℓ1 recovery guarantee compared to Lasso (Tibshirani, 1996) in many problems. Our bound naturally motivates an experimental design criterion that is convex and thus computationally efficient to solve. Based on our novel estimator and design criterion, we derive sparse linear bandit algorithms that enjoy improved regret upper bounds upon the state of the art (Hao et al., 2020), especially w.r.t. the geometry of the given action set. Finally, we prove a matching lower bound for sparse linear bandits in the data-poor regime, which closes the gap between upper and lower bounds in prior work.

Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment. In particular, models enable planning, i.e. using more computation to improve value functions or policies, without requiring additional environment interactions. In this work, we investigate a way of augmenting model-based RL, by additionally encouraging a learned model and value function to be jointly self-consistent. Our approach differs from classic planning methods such as Dyna, which only update values to be consistent with the model. We propose multiple self-consistency updates, evaluate these in both tabular and function approximation settings, and find that, with appropriate choices, self-consistency helps both policy evaluation and control.

A.1 Dataset Details & Evaluation Metrics As stated earlier, the main application of Extreme Multi-label Text Classification is in e-commerce - product recommendation and dynamic search advertisement - and in document tagging, where the objective of an algorithm is to correctly recommend/advertise among the top-k slots. Thus, for evaluation of the methods, we use precision at k (denoted by P@k), and its propensity scored variant (denoted by PSP@k) [17]. These are standard and widely used metrics by the XMC community [4]. Since P@k treats all the labels equally, it doesn't reveal the performance of the model on tail labels. However, because of the long-tailed distribution in XMC datasets, one of the main challenges is to predict tail labels correctly, which may be more valuable and informative compared to head classes.

Out-of-distribution (OOD) learning deals with scenarios in which training and test data follow different distributions. Although general OOD problems have been intensively studied in machine learning, graph OOD is only an emerging area of research. Currently, there lacks a systematic benchmark tailored to graph OOD method evaluation. In this work, we aim at developing an OOD benchmark, known as GOOD, for graphs specifically. We explicitly make distinctions between covariate and concept shifts and design data splits that accurately reflect different shifts. We consider both graph and node prediction tasks as there are key differences in designing shifts. Overall, GOOD contains 11 datasets with 17 domain selections. When combined with covariate, concept, and no shifts, we obtain 51 different splits. We provide performance results on 10 commonly used baseline methods with 10 random runs.

Accessible high-quality data is the bread and butter of machine learning research,1 and the demand for data has exploded as larger and more advanced ML models are2 built across different domains. Yet, real data often contain sensitive information,3 subject to various biases, and are costly to acquire, which compromise their quality4 and accessibility. Synthetic data have thus emerged as a complement, sometimes5 even a replacement, to real data for ML training. However, the landscape of6 synthetic data research has been fragmented due to the large number of data7 modalities (e.g., tabular data, time series data, images, etc.) and various use cases8 (e.g., privacy, fairness, data augmentation, etc.). This poses practical challenges9 in comparing and selecting synthetic data generators in different problem settings.10 To this end, we develop Synthcity, an open-source Python library that allows11 researchers and practitioners to perform one-click benchmarking of synthetic data12 generators across data modalities and use cases. In addition, Synthcity's plug-in13 style API makes it easy to incorporate additional data generators into the framework.14 Beyond benchmarking, it also offers a single access point to a diverse range of15 cutting-edge data generators. Through examples on tabular data generation and16 data augmentation, we illustrate the general applicability of Synthcity, and the17 insight one can obtain.18