Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Kernel Selection via Incremental Sketched Kernel Alignment

In contrast to offline kernel selection, online kernel selection must rise to the new challenges of passing the training set once, selecting optimal kernels and updating hypotheses at each round, enjoying a sublinear regret bound for online kernel learning, and requiring a constant maintenance time complexity at each round and an efficient overall time complexity integrated with online kernel learning. However, most of existing online kernel selection approaches can not meet the new challenges. To address this issue, we propose a novel online kernel selection approach via the incremental sketched kernel alignment criterion, which meets all the new challenges. We first define the incremental sketched kernel alignment (ISKA) criterion, which estimates the kernel alignment and can be computed incrementally and efficiently. When applying the proposed ISKA criterion to online kernel selection, we adopt the subclass coherence to maintain the hypothesis space, select the optimal kernel at each round using the median of the ISKA criterion estimates, and update the hypothesis following the online gradient decent method. We prove that the ISKA criterion is an unbiased estimate of the maximum mean discrepancy, enjoys the optimal logarithmic regret bound for online kernel learning, and has a constant maintenance time complexity at each round and a logarithmic overall time complexity integrated with online kernel learning. Empirical studies demonstrate that the proposed online kernel selection approach is computationally efficient while maintaining comparable accuracy for online kernel learning.
Engineering Graph Features via Network Functional Blocks

Graph is a prevalent data structure that enables many predictive tasks. How to engineer graph features is a fundamental question. Our concept is to go beyond nodes and edges, and explore richer structures (e.g., paths, subgraphs) for graph feature engineering. We call such richer structures as network functional blocks, because each structure serves as a network building block but with some different functionality. We use semantic proximity search as an example application to share our recent work on exploiting different granularities of network functional blocks. We show that network functional blocks are effective, and they can be useful for a wide range of applications.
Chronic, intermittent treatment with a cannabinoid receptor agonist impairs recognition memory and brain network functional connectivity

Elucidating how cannabinoids affect brain function is instrumental for the development of therapeutic tools aiming to mitigate ‘on target’ side effects of cannabinoid‐based therapies. A single treatment with the cannabinoid receptor agonist, WIN 55,212‐2, disrupts recognition memory in mice. Here, we evaluate how prolonged, intermittent (30 days) exposure to WIN 55,212‐2 (1 mg/kg) alters recognition memory and impacts on brain metabolism and functional connectivity. We show that chronic, intermittent treatment with WIN 55,212‐2 disrupts recognition memory (Novel Object Recognition Test) without affecting locomotion and anxiety‐like behaviour (Open Field and Elevated Plus Maze). Through 14C‐2‐deoxyglucose functional brain imaging we show that chronic, intermittent WIN 55,212‐2 exposure induces hypometabolism in the hippocampal dorsal subiculum and in the mediodorsal nucleus of the thalamus, two brain regions directly involved in recognition memory. In addition, WIN 55,212‐2 exposure induces hypometabolism in the habenula with a contrasting hypermetabolism in the globus pallidus. Through the application of the Partial Least Squares Regression (PLSR) algorithm to the brain imaging data, we observed that prolonged WIN 55,212‐2 administration alters functional connectivity in brain networks that underlie recognition memory, including that between the hippocampus and prefrontal cortex, the thalamus and prefrontal cortex, and between the hippocampus and the perirhinal cortex. In addition, our results support disturbed lateral habenula and serotonin system functional connectivity following WIN 55,212‐2 exposure. Overall, this study provides new insight into the functional mechanisms underlying the impact of chronic cannabinoid exposure on memory and highlights the serotonin system as a particularly vulnerable target.
Deep Attributed Network Embedding

Network embedding has attracted a surge of attention in recent years. It is to learn the low-dimensional representation for nodes in a network, which benefits downstream tasks such as node classification and link prediction. Most of the existing approaches learn node representations only based on the topological structure, yet nodes are often associated with rich attributes in many real-world applications. Thus, it is important and necessary to learn node representations based on both the topological structure and node attributes. In this paper, we propose a novel deep attributed network embedding approach, which can capture the high non-linearity and preserve various proximities in both topological structure and node attributes. At the same time, a novel strategy is proposed to guarantee the learned node representation can encode the consistent and complementary information from the topological structure and node attributes. Extensive experiments on benchmark datasets have verified the effectiveness of our proposed approach.
Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models

Sparse learning models have shown promising performance in the high dimensional machine learning applications. The main challenge of sparse learning models is how to optimize it efficiently. Most existing methods solve this problem by relaxing it as a convex problem, incurring large estimation bias. Thus, the sparse learning model with nonconvex constraint has attracted much attention due to its better performance. But it is difficult to optimize due to the non-convexity. In this paper, we propose a linearly convergent stochastic second-order method to optimize this nonconvex problem for large-scale datasets. The proposed method incorporates second-order information to improve the convergence speed. Theoretical analysis shows that our proposed method enjoys linear convergence rate and guarantees to converge to the underlying true model parameter. Experimental results have verified the efficiency and correctness of our proposed method.
Improving Reinforcement Learning with Human Input

Reinforcement learning (RL) has had many successes when learning autonomously. This paper and accompanying talk consider how to make use of a non-technical human participant, when available. In particular, we consider the case where a human could 1) provide demonstrations of good behavior, 2) provide online evaluative feedback, or 3) define a curriculum of tasks for the agent to learn on. In all cases, our work has shown such information can be effectively leveraged. After giving a high-level overview of this work, we will highlight a set of open questions and suggest where future work could be usefully focused.