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Found 35 papers, 14 papers with code
Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents
We show that users spend a lot of ``effort'' on these questions in terms of signals like clicks and session length, and that they are also challenging for GPT-4.
Automatic Pair Construction for Contrastive Post-training
Remarkably, our automatic contrastive post-training further improves the performance of Orca, already a state-of-the-art instruction learning model tuned with GPT-4 outputs, to outperform ChatGPT.
Nugget 2D: Dynamic Contextual Compression for Scaling Decoder-only Language Models
Standard Transformer-based language models (LMs) scale poorly to long contexts.
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
Backed by our theoretical analysis, instead of maximizing the recovery of cross-instance node dependencies -- which has been considered the key behind closing the performance gap between model aggregation and centralized training -- , our framework leverages randomized assignment of nodes or super-nodes (i. e., collections of original nodes) to partition the training graph such that it improves data uniformity and minimizes the discrepancy of gradient and loss function across instances.
You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction
We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.
Search Behavior Prediction: A Hypergraph Perspective
With these hyperedges, we augment the original bipartite graph into a new \textit{hypergraph}.
Text Enriched Sparse Hyperbolic Graph Convolutional Networks
In TESH-GCN, we extract semantic node information, which successively acts as a connection signal to extract relevant nodes' local neighborhood and graph-level metapath features from the sparse adjacency tensor in a reformulated hyperbolic graph convolution layer.
Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
This paper introduces the "Shopping Queries Dataset", a large dataset of difficult Amazon search queries and results, publicly released with the aim of fostering research in improving the quality of search results.
Learning Backward Compatible Embeddings
We formalize the problem where the goal is for the embedding team to keep updating the embedding version, while the consumer teams do not have to retrain their models.
Task-Agnostic Graph Explanations
They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks.
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
We propose Cold Brew, a teacher-student distillation approach to address the SCS and noisy-neighbor challenges for GNNs.
Cluster-and-Conquer: A Framework For Time-Series Forecasting
Our framework -- which we refer to as "cluster-and-conquer" -- is highly general, allowing for any time-series forecasting and clustering method to be used in each step.
Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs
Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection.
Task-Agnostic Graph Neural Explanations
TAGE enables the explanation of GNN embedding models without downstream tasks and allows efficient explanation of multitask models.
Scalable Feature Selection for (Multitask) Gradient Boosted Trees
Gradient Boosted Decision Trees (GBDTs) are widely used for building ranking and relevance models in search and recommendation.
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
In this paper, we consider the pure exploration problem in stochastic multi-armed bandits where the similarities between the arms are captured by a graph and the rewards may be represented as a smooth signal on this graph.
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
Promising approaches to tackle this problem include embedding the KG units (e. g., entities and relations) in a Euclidean space such that the query embedding contains the information relevant to its results.
Regularized Graph Convolutional Networks for Short Text Classification
The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning.
Learning Robust Models for e-Commerce Product Search
Showing items that do not match search query intent degrades customer experience in e-commerce.
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we propose using Graph DNA, a novel Deep Neighborhood Aware graph encoding algorithm, for exploiting deeper neighborhood information.
A Simple Approach to Learn Polysemous Word Embeddings
Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings.
Matrix Completion via Factorizing Polynomials
While there has been recent research on incorporating explicit side information in the low-rank matrix factorization setting, often implicit information can be gleaned from the data, via higher-order interactions among entities.
Dynamic Word Embeddings for Evolving Semantic Discovery
Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.
Structured Sparse Regression via Greedy Hard Thresholding
Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups.
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
We develop novel regularization schemes and use scalable matrix factorization methods that are eminently suited for high-dimensional time series data that has many missing values.
On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semi-parametric models for machine learning, where the response variable is modeled as a monotonic function of a linear combination of features.
Structured Sparse Regression via Greedy Hard-Thresholding
Several learning applications require solving high-dimensional regression problems where the relevant features belong to a small number of (overlapping) groups.
Collaborative Filtering with Graph Information: Consistency and Scalable Methods
Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space.
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Sparse and Low-Rank Tensor Decomposition
Our method relies on a reduction of the problem to sparse and low-rank matrix decomposition via the notion of tensor contraction.
High-dimensional Time Series Prediction with Missing Values
High-dimensional time series prediction is needed in applications as diverse as demand forecasting and climatology.
Learning Single Index Models in High Dimensions
Single Index Models (SIMs) are simple yet flexible semi-parametric models for classification and regression.
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice
This motivates us to consider the problem of low rank tensor recovery from a class of linear measurements called separable measurements.
Forward - Backward Greedy Algorithms for Atomic Norm Regularization
CoGEnT combines a greedy selection scheme based on the conditional gradient approach with a backward (or "truncation") step that exploits the quadratic nature of the objective to reduce the basis size.
Classification with Sparse Overlapping Groups
In this paper, we are interested in a less restrictive form of structured sparse feature selection: we assume that while features can be grouped according to some notion of similarity, not all features in a group need be selected for the task at hand.
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
In this paper, we are interested in a less restrictive form of multitask learning, wherein (1) the available features can be organized into subsets according to a notion of similarity and (2) features useful in one task are similar, but not necessarily identical, to the features best suited for other tasks.
