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Found 28 papers, 4 papers with code
Regression with Large Language Models for Materials and Molecular Property Prediction
We demonstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case.
Causal Effect Estimation Using Random Hyperplane Tessellations
Matching is one of the simplest approaches for estimating causal effects from observational data.
Hyperdimensional Computing for Node Classification and Link Prediction
Our experiments also show that the HD representation constructed by HDGL supports link prediction at accuracies comparable to that of DeepWalk and related methods, although it falls short of SOTA Graph Neural Network (GNN) methods that rely on computationally expensive iterative training.
Representing and Reasoning with Multi-Stakeholder Qualitative Preference Queries
Many decision-making scenarios, e. g., public policy, healthcare, business, and disaster response, require accommodating the preferences of multiple stakeholders.
Forecasting User Interests Through Topic Tag Predictions in Online Health Communities
Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users.
Zooming Into the Darknet: Characterizing Internet Background Radiation and its Structural Changes
Network telescopes or "Darknets" provide a unique window into Internet-wide malicious activities associated with malware propagation, denial of service attacks, scanning performed for network reconnaissance, and others.
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals
Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions.
A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution
We aim to address this problem in settings where the predictive model is a black box; That is, we can only observe the response of the model to various inputs, but have no knowledge about the internal structure of the predictive model, its parameters, the objective function, and the algorithm used to optimize the model.
Longitudinal Deep Kernel Gaussian Process Regression
Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods.
Towards Robust Relational Causal Discovery
In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data.
Algorithmic Bias in Recidivism Prediction: A Causal Perspective
Specifically, we assess whether COMPAS exhibits racial bias against African American defendants using FACT, a recently introduced causality grounded measure of algorithmic fairness.
LMLFM: Longitudinal Multi-Level Factorization Machine
However, the current state-of-the-art methods are unable to select the most predictive fixed effects and random effects from a large number of variables, while accounting for complex correlation structure in the data and non-linear interactions among the variables.
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario
We demonstrate the usefulness of this approach on synthetic as well as the human motion capture data set.
Node Injection Attacks on Graphs via Reinforcement Learning
Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)
Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc.
Improving Image Captioning by Leveraging Knowledge Graphs
We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning.
Top-N-Rank: A Scalable List-wise Ranking Method for Recommender Systems
We propose Top-N-Rank, a novel family of list-wise Learning-to-Rank models for reliably recommending the N top-ranked items.
Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement
Our experiments with several benchmark real-world single view networks show that GFC-based SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks.
Compositional Stochastic Average Gradient for Machine Learning and Related Applications
Many machine learning, statistical inference, and portfolio optimization problems require minimization of a composition of expected value functions (CEVF).
Advances in Artificial Intelligence Require Progress Across all of Computer Science
Advances in Artificial Intelligence require progress across all of computer science.
Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning
The correctness of the algorithm proposed by Maier et al. (2013a) for learning RCM from data relies on the soundness and completeness of AGG for relational d-separation to reduce the learning of an RCM to learning of an AGG.
CRISNER: A Practically Efficient Reasoner for Qualitative Preferences
We present CRISNER (Conditional & Relative Importance Statement Network PrEference Reasoner), a tool that provides practically efficient as well as exact reasoning about qualitative preferences in popular ceteris paribus preference languages such as CP-nets, TCP-nets, CP-theories, etc.
Representing and Reasoning with Qualitative Preferences for Compositional Systems
Among the collections that satisfy the functional requirement, it is often necessary to identify one or more collections that are optimal with respect to user preferences over a set of attributes that describe the non-functional properties of the collection.
Efficient Markov Network Structure Discovery Using Independence Tests
We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN.
Transportability from Multiple Environments with Limited Experiments
This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a target environment, in which only limited experiments can be performed.
Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability
We provide a correct and complete algorithm that determines whether a causal effect is z-transportable; and if it is, produces a transport formula, that is, a recipe for estimating the causal effect of X on Y in the target domain using information elicited from the results of experimental manipulations of Z in the source domain and observational data from the target domain.
