Machine learning refactors knowledge to improve learning efficiency.
problem Inductive program synthesis efficiency through knowledge restructuring.
method Introduces Knorf, a system that refactors knowledge bases using constraint optimization.
result Learning from refactored knowledge improves predictive accuracy fourfold and reduces learning time by half.
Paper extends knowledge gradient for preferential BO, overcoming computational challenges.
problem Extending knowledge gradient to preferential BO with pairwise comparisons.
method Derive exact and analytical knowledge gradient for preferential BO.
result Exact knowledge gradient outperforms existing acquisition functions on benchmark problems.
Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are …
Paper extends knowledge tracing algorithms to infer student knowledge and predict posttest performance.
problem Lack of algorithms that directly infer student knowledge and predict posttest performance.
method Extended DKT and DKVMN to infer knowledge, and applied to BKT and PFA for comparison.
result Knowledge estimates from the extended algorithms correlate better with posttest performance than existing methods.
Domain knowledge helps detect adversarial examples in multi-label classification.
problem Detecting adversarial examples in multi-label classification.
method Convert domain knowledge into constraints and inject them into a semi-supervised learning problem.
result Domain-knowledge constraints help detect adversarial examples effectively.
Paper proposes AI for stock market forecasting using external knowledge.
problem Forecasting stock prices influenced by external factors.
method Learning from historical data and external temporal knowledge graphs modeled as Hawkes processes.
result Dynamic representations effectively rank stocks based on returns.
Survey on robust data representation learning from a knowledge flow perspective.
problem Learning robust visual representation from noisy, incomplete or mismatched data.
method Low-rank modeling for robust knowledge recovery, transfer, and fusion.
result Unified formulation for robust knowledge discovery and transfer/fusion.
DMLreg uses expert knowledge to improve model performance in high-dimensional settings.
problem Improving model performance in high-dimensional prediction problems.
method Learning a Mahalanobis distance metric from expert comparisons and integrating it into a regularized linear model.
result DMLreg leads to improvements in model performance when expert knowledge is relevant.
Incorporation of a new knowledge into neural networks with simultaneous preservation of the previous one is known to be a nontrivial problem. This problem becomes even more complex when new knowledge is contained not in new training examples, but inside the parameters (connection weights) of another neural network. Her…
Bayesian method transfers knowledge between brain tumor datasets.
problem Applying deep learning to small medical datasets.
method Generative Bayesian Prior network for knowledge transfer.
result Best results in Dice Similarity Coefficient for BRATS2018.
This paper studies Bayesian ranking and selection (R&S) problems with correlated prior beliefs and continuous domains, i.e. Bayesian optimization (BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely studied for discrete R&S problems, which sample the one-step Bayes-optimal point. When used ove…
The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) seems like a natural solution; however, current implementations of CP on standard Knowledge Base Completion benchmarks are lagging behind their…
MERL improves reinforcement learning by integrating problem knowledge into policy updates.
problem Reward sparsity and feature space conditioning in reinforcement learning.
method MERL framework that injects problem-focused quantities into policy gradient updates.
result Improved performance across various benchmark environments and transfer learning tasks.
Improved knowledge gradient (iKG) outperforms the original KG algorithm in best arm identification problems.
problem Best arm identification (BAI) problem with limitations of the original KG algorithm.
method Follows the one-step look ahead of KG but chooses the measurement that maximizes the probability of selecting the best arm.
result Improved knowledge gradient (iKG) is asymptotically optimal and easier to extend to variant BAI problems.
AutoKE automates embedding physical knowledge into neural networks for complex engineering problems.
problem Complex physical equations in engineering problems.
method AutoKE framework using deep neural networks, equation parsing, automatic differentiation, adaptive weights, and NAS.
result Automatically embeds physical knowledge into neural networks for complex equations efficiently.
A new system recommends specific knowledge concepts in MOOCs based on student interests.
problem MOOCs recommend courses but ignore specific knowledge concepts interests.
method End-to-end graph neural network (ACKRec) that combines content and context information.
result ACKRec effectively recommends knowledge concepts to MOOC students.
A new teacher-class network method compresses DNNs by distributing knowledge to multiple student networks.
problem Overwhelming size of Deep Neural Networks (DNNs).
method Single teacher with multiple student networks, transferring knowledge to each student.
result The combined knowledge of the class of students achieves better performance and reduces parameters.
Proposes clustering as a new evaluation method for clinical knowledge embedding.
problem Traditional Link Prediction evaluation protocol loses information and harms model accuracy.
method Proposes Clustering Evaluation Protocol as an alternative.
result Experimental results show the proposed protocol can potentially replace Link Prediction.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-…
Knowledge Matters: Importance of Prior Information for Optimization [7], by Gulcehre et. al., sought to establish the limits of current black-box, deep learning techniques by posing problems which are difficult to learn without engineering knowledge into the model or training procedure. In our work, we completely solve…
New method improves knowledge transfer from large to small networks.
problem Improving knowledge transfer from large to small neural networks.
method Contrastive learning to capture more structural knowledge.
result Contrastive learning outperforms knowledge distillation on various transfer tasks.
This paper improves ASR performance by aligning frames more accurately.
problem Disagreement between teacher-student models in frame-level alignment.
method Introduces self-knowledge distillation (SKD) to guide frame-level alignment.
result Improves both resource efficiency and performance in ASR.
Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behaviour, and time. This work can be seen not only as a collection of architectural proposals, but also as the beginnings of a theory of machine k…
This paper shows how integrating domain knowledge improves ML models for transprecision computing.
problem Improving ML models for transprecision computing with scarce or complex data.
method Injecting domain knowledge into neural networks through additional features, graph-based topology, and regularization schemes.
result ML models with domain knowledge outperform purely data-driven models by around 38%.
RKD improves model compression by distilling residual knowledge from a deep teacher model.
problem Performance degradation due to the gap between student and teacher models.
method Introduces an assistant model to distill residual knowledge from the teacher model.
result RKD achieves better results on popular classification datasets than state-of-the-art methods.
Investor optimizes investment timing with future knowledge, overcoming transaction costs.
problem Optimal investment timing with future peeking, constrained by transaction costs.
method Solves control problem with infinite-dimensional memory using Gaussian Volterra integral equations.
result Explicit solution to optimal investment problem in Bachelier setting.
Study on teaching with imperfect knowledge, showing its impact on optimal teaching sets.
problem Effect of imperfect teacher knowledge on effective teaching.
method Connections to machine teaching problem, optimal teaching sets.
result Teacher's success or failure depends on imperfect knowledge.
This work transfers causal knowledge between tasks for Individual Treatment Effect estimation.
problem Estimating Individual Treatment Effects (ITE) requires a large amount of data, making it challenging.
method The authors introduce a practical framework for efficient transfer of causal knowledge between tasks, using a Causal Inference Task Affinity (CITA) measure.
result ITE knowledge transfer can significantly reduce the amount of data needed for ITE estimation.
A new method for optimizing black-box problems with constraints.
problem Optimizing black-box systems with multiple performance criteria and constraints.
method Developed a novel constrained Bayesian optimization approach based on the knowledge gradient method.
result A new acquisition function that balances optimality and feasibility.
Framework incorporates prior knowledge into Bayesian models for data streams.
problem Effective use of prior knowledge in learning Bayesian models from streaming data.
method Proposes a novel framework that subsumes existing models for time-series data.
result Framework outperforms existing methods with a large margin.
Reinforcement learning would enjoy better success on real-world problems if domain knowledge could be imparted to the algorithm by the modelers. Most problems have both hidden state and unknown dynamics. Partially observable Markov decision processes (POMDPs) allow for the modeling of both. Unfortunately, they do not p…
This work proposes a hybrid method for error detection in noisy Knowledge Graphs.
problem Error detection in noisy Knowledge Graphs.
method Hybrid and modular approach combining path ranking and representation learning.
result Hybrid method outperforms individual methods on benchmarks and real-world dataset.
This paper studies aligning knowledge graphs from different sources or languages. Most existing methods train supervised methods for the alignment, which usually require a large number of aligned knowledge triplets. However, such a large number of aligned knowledge triplets may not be available or are expensive to obta…
Unsupervised relation discovery aims to discover new relations from a given text corpus without annotated data. However, it does not consider existing human annotated knowledge bases even when they are relevant to the relations to be discovered. In this paper, we study the problem of how to use out-of-relation knowledg…
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally sharing knowledge among tasks. In this paper, based on gradient-based meta-learning,…
DRUM discovers interpretable rules from knowledge graphs for unseen entities.
problem Inductive link prediction on unseen entities and lack of interpretability.
method Differentiable approach using bidirectional RNNs for low-rank tensor approximation.
result DRUM outperforms existing methods in inductive link prediction.
Knowledge distillation improves model accuracy by mimicking teacher model probabilities.
problem Improving model accuracy through model compression.
method Casting knowledge distillation as a semiparametric inference problem, deriving new guarantees, and developing enhancements.
result Enhancements improve student performance by mitigating teacher overfitting and underfitting.
Knowledge Transfer (KT) techniques tackle the problem of transferring the knowledge from a large and complex neural network into a smaller and faster one. However, existing KT methods are tailored towards classification tasks and they cannot be used efficiently for other representation learning tasks. In this paper a n…
Paper proposes a new method for training small models on regression problems.
problem Training small models for regression problems with noisy labels.
method Developed a new loss function and a multi-task network approach.
result Improved model accuracy on various datasets, consistent across different levels of annotation errors.
New algorithms solve nonconvex-concave minimax problems without parameter knowledge.
problem Solving nonconvex-concave minimax problems efficiently.
method Three completely parameter-free single-loop algorithms.
result Achieve optimal iteration complexity for nonconvex-concave minimax problems.
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent k…
Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which…
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some student, one can optimize instruction. Existing methods are either based on temporal latent variabl…
The paper tackles adaptive targeting in networks with interference effects.
problem Adaptive targeting under network interference in a bandit setting.
method Linear model in a sparse regime, analyzing different levels of knowledge of the interference structure.
result Unified view of how knowledge of the interference structure affects online learning efficiency.
We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefin…
Study restricts causal graphs with expert knowledge.
problem Restricting causal graphs to include expert orientation knowledge.
method Prove properties, present new orientation rules, develop algorithms.
result Shows how to uniquely represent restricted essential ancestral graphs.
Neural Assistant integrates knowledge reasoning and dialogue generation in a single model.
problem Challenges in task-oriented dialog systems, including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.
method Develops a single neural network model that jointly predicts text responses and actions from conversation history and external knowledge.
result The model learns to reason on external knowledge with weak supervision, improving factual accuracy and language generation performance.