The Probabilistic Watershed isasemi-supervised learning algorithm applied on undirected graphs.

Ranking items is a central task in many information retrieval and recommender systems. User input for the ranking task often comes in the form of ratings on a coarse discrete scale. We ask whether it is possible to recover a fine-grained item ranking from such coarse-grained ratings. We model items as having scores and users as having thresholds; a user rates an item positively if the item's score exceeds the user's threshold. Although all users agree on the total item order, estimating that order is challenging when both the scores and the thresholds are latent. Under our model, any ranking method naturally partitions the $n$ items into bins; the bins are ordered, but the items inside each bin are still unordered. Users arrive sequentially, and every new user can be queried to refine the current ranking. We prove that achieving a near-perfect ranking, measured by Spearman distance, requires $Θ(n^2)$ users (and therefore $Ω(n^2)$ queries). This is significantly worse than the $O(n\log n)$ queries needed to rank from comparisons; the gap reflects the additional queries needed to identify the users who have the appropriate thresholds. Our bound also quantifies the impact of a mismatch between score and threshold distributions via a quadratic divergence factor. To show the tightness of our results, we provide a ranking algorithm whose query complexity matches our bound up to a logarithmic factor. Our work reveals a tension in online ranking: diversity in thresholds is necessary to merge coarse ratings from many users into a fine-grained ranking, but this diversity has a cost if the thresholds are a priori unknown.

Neural Information Processing Systems http://nips.cc/

A Single Scale Doesn't Fit All: Adaptive Motion Scaling for Efficient and Precise T eleoperation Jeonghyeon Y oon 1, Sanghyeok Park 2, Hyojae Park 1, Cholin Kim 1, Sihyeoung Park 1, and Minho Hwang 1 Abstract -- T eleoperation is increasingly employed in environments where direct human access is difficult, such as hazardous exploration or surgical field. However, if the motion scale factor(MSF) intended to compensate for workspace-size differences is set inappropriately, repeated clutching operations and reduced precision can significantly raise cognitive load. This paper presents a shared controller that dynamically applies the MSF based on the user's intended motion scale. Inspired by human motor skills, the leader arm trajectory is divided into coarse(fast, large-range movements) and fine(precise, small-range movements), with three features extracted to train a fuzzy C-means(FCM) clustering model that probabilistically classifies the user's motion scale. Scaling the robot's motion accordingly reduces unnecessary repetition for large-scale movements and enables more precise control for fine operations. Incorporating recent trajectory data into model updates and offering user feedback for adjusting the MSF range and response speed allows mutual adaptation between user and system. In peg transfer experiments, compared to using a fixed single scale, the proposed approach demonstrated improved task efficiency(number of clutching and task completion time decreased 38.46% and 11.96% respectively), while NASA-TLX scores confirmed a meaningful reduction(58.01%

Deep reinforcement learning (DRL) frameworks are increasingly used to solve high-dimensional continuous-control tasks in robotics. However, due to the lack of sample efficiency, applying DRL for online learning is still practically infeasible in the robotics domain. One reason is that DRL agents do not leverage the solution of previous tasks for new tasks. Recent work on multi-tasking DRL agents based on successor features has proven to be quite promising in increasing sample efficiency. In this work, we present a new approach that unifies two prior multi-task RL frameworks, SF-GPI and value composition, for the continuous control domain. We exploit compositional properties of successor features to compose a policy distribution from a set of primitives without training any new policy. Lastly, to demonstrate the multi-tasking mechanism, we present a new benchmark for multi-task continuous control environment based on Raisim. This also facilitates large-scale parallelization to accelerate the experiments. Our experimental results in the Pointmass environment show that our multi-task agent has single task performance on par with soft actor critic (SAC) and the agent can successfully transfer to new unseen tasks where SAC fails. We provide our code as open-source at https://github.com/robot-perception-group/concurrent_composition for the benefit of the community.

Medical Slot Filling (MSF) task aims to convert medical queries into structured information, playing an essential role in diagnosis dialogue systems. However, the lack of sufficient term semantics learning makes existing approaches hard to capture semantically identical but colloquial expressions of terms in medical conversations. In this work, we formalize MSF into a matching problem and propose a Term Semantics Pre-trained Matching Network (TSPMN) that takes both terms and queries as input to model their semantic interaction. To learn term semantics better, we further design two self-supervised objectives, including Contrastive Term Discrimination (CTD) and Matching-based Mask Term Modeling (MMTM). CTD determines whether it is the masked term in the dialogue for each given term, while MMTM directly predicts the masked ones. Experimental results on two Chinese benchmarks show that TSPMN outperforms strong baselines, especially in few-shot settings.

How can pre-trained language models (PLMs) learn universal representations and effectively adapt to broad NLP tasks differing a lot superficially? In this work, we empirically find evidences indicating that the adaptations of PLMs to various tasks can be reparameterized as optimizing only a few free parameters in a common low-dimensional intrinsic task subspace, which may help us understand why PLMs could easily adapt to various NLP tasks with small-scale data. Specifically, to find such a subspace and examine its universality, we resort to the recent success of prompt tuning and decompose the soft prompts of multiple NLP tasks into the same low-dimensional nonlinear subspace, then we learn to adapt the PLM to unseen tasks or data by only tuning parameters in the subspace. We dub this pipeline as intrinsic prompt tuning (IPT). In experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 5-dimensional subspace found with 100 random tasks, by only tuning 5 free parameters, we can recover 87% and 65% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace. Besides being an analysis tool, IPT could further bring practical benefits, such as improving the prompt tuning stability.