Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search

Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online

Learning multi-view neighborhood preserving projections

We address the problem of metric learning for multi-view data, namely the construction of embedding projections from data in different representations into a shared feature space, such that the Euclidean distance in this space provides a meaningful within-view as well as between-view similarity. Our motivation stems from the problem of cross-media retrieval tasks, where the availability of a joint Euclidean distance function is a prerequisite to allow fast, in particular hashing-based, nearest neighbor queries. We formulate an objective function that expresses the intuitive concept that matching samples are mapped closely together in the output space, whereas non-matching samples are pushed apart, no matter in which view they are available. The resulting optimization problem is not convex, but it can be decomposed explicitly into a convex and a concave part, thereby allowing efficient optimization using the convex-concave procedure. Experiments on an image retrieval task show that nearest-neighbor based cross-view retrieval is indeed possible, and the proposed technique improves the retrieval accuracy over baseline techniques

Kernelized Offline Contextual Dueling Bandits

Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback on large language models. For many of these applications, the cost of acquiring the human feedback can be substantial or even prohibitive. In this work, we take advantage of the fact that often the agent can choose contexts at which to obtain human feedback in order to most efficiently identify a good policy, and introduce the offline contextual dueling bandit setting. We give an upper-confidence-bound style algorithm for this setting and prove a regret bound. We also give empirical confirmation that this method outperforms a similar strategy that uses uniformly sampled contexts

Scalable large margin pairwise learning algorithms

2019 Summer.Includes bibliographical references.Classification is a major task in machine learning and data mining applications. Many of these applications involve building a classification model using a large volume of imbalanced data. In such an imbalanced learning scenario, the area under the ROC curve (AUC) has proven to be a reliable performance measure to evaluate a classifier. Therefore, it is desirable to develop scalable learning algorithms that maximize the AUC metric directly. The kernelized AUC maximization machines have established a superior generalization ability compared to linear AUC machines. However, the computational cost of the kernelized machines hinders their scalability. To address this problem, we propose a large-scale nonlinear AUC maximization algorithm that learns a batch linear classifier on approximate feature space computed via the k-means Nyström method. The proposed algorithm is shown empirically to achieve comparable AUC classification performance or even better than the kernel AUC machines, while its training time is faster by several orders of magnitude. However, the computational complexity of the linear batch model compromises its scalability when training sizable datasets. Hence, we develop a second-order online AUC maximization algorithms based on a confidence-weighted model. The proposed algorithms exploit the second-order information to improve the convergence rate and implement a fixed-size buffer to address the multivariate nature of the AUC objective function. We also extend our online linear algorithms to consider an approximate feature map constructed using random Fourier features in an online setting. The results show that our proposed algorithms outperform or are at least comparable to the competing online AUC maximization methods. Despite their scalability, we notice that online first and second-order AUC maximization methods are prone to suboptimal convergence. This can be attributed to the limitation of the hypothesis space. A potential improvement can be attained by learning stochastic online variants. However, the vanilla stochastic methods also suffer from slow convergence because of the high variance introduced by the stochastic process. We address the problem of slow convergence by developing a fast convergence stochastic AUC maximization algorithm. The proposed stochastic algorithm is accelerated using a unique combination of scheduled regularization update and scheduled averaging. The experimental results show that the proposed algorithm performs better than the state-of-the-art online and stochastic AUC maximization methods in terms of AUC classification accuracy. Moreover, we develop a proximal variant of our accelerated stochastic AUC maximization algorithm. The proposed method applies the proximal operator to the hinge loss function. Therefore, it evaluates the gradient of the loss function at the approximated weight vector. Experiments on several benchmark datasets show that our proximal algorithm converges to the optimal solution faster than the previous AUC maximization algorithms

Learning Active Learning from Data

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains