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Found 28 papers, 8 papers with code
Hallucination Diversity-Aware Active Learning for Text Summarization
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i. e., texts that are factually incorrect or unsupported.
Decentralized Personalized Online Federated Learning
Therefore, we propose a new learning setting \textit{Decentralized Personalized Online Federated Learning} that considers all the three aspects at the same time.
Sample Constrained Treatment Effect Estimation
In particular, we study sample-constrained treatment effect estimation, where we must select a subset of $s \ll n$ individuals from the population to experiment on.
Electra: Conditional Generative Model based Predicate-Aware Query Approximation
The goal of Approximate Query Processing (AQP) is to provide very fast but "accurate enough" results for costly aggregate queries thereby improving user experience in interactive exploration of large datasets.
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory.
Multiscale Manifold Warping
Many real-world applications require aligning two temporal sequences, including bioinformatics, handwriting recognition, activity recognition, and human-robot coordination.
Coresets for Classification – Simplified and Strengthened
It also does not depend on the specific loss function, so a single coreset can be used in multiple training scenarios.
Asymptotics of Ridge Regression in Convolutional Models
We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.
Machine Unlearning via Algorithmic Stability
Our key contribution is the design of corresponding efficient unlearning algorithms, which are based on constructing a (maximal) coupling of Markov chains for the noisy SGD procedure.
Online Discrepancy Minimization via Persistent Self-Balancing Walks
We study the online discrepancy minimization problem for vectors in $\mathbb{R}^d$ in the oblivious setting where an adversary is allowed fix the vectors $x_1, x_2, \ldots, x_n$ in arbitrary order ahead of time.
Data Structures and Algorithms Discrete Mathematics Combinatorics
Fundamental Tradeoffs in Distributionally Adversarial Training
We study the tradeoff between standard risk and adversarial risk and derive the Pareto-optimal tradeoff, achievable over specific classes of models, in the infinite data limit with features dimension kept fixed.
Heterogeneous Graphlets
Notably, since typed graphlet is more general than colored graphlet (and untyped graphlets), the counts of various typed graphlets can be combined to obtain the counts of the much simpler notion of colored graphlets.
Efficient Balanced Treatment Assignments for Experimentation
In this work, we reframe the problem of balanced treatment assignment as optimization of a two-sample test between test and control units.
Graph Neural Networks with Heterophily
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications.
Sample Efficient Graph-Based Optimization with Noisy Observations
We study sample complexity of optimizing "hill-climbing friendly" functions defined on a graph under noisy observations.
Model Selection in Contextual Stochastic Bandit Problems
Our methods rely on a novel and generic smoothing transformation for bandit algorithms that permits us to obtain optimal $O(\sqrt{T})$ model selection guarantees for stochastic contextual bandit problems as long as the optimal base algorithm satisfies a high probability regret guarantee.
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs
In this work, we investigate higher-order network motifs and develop techniques based on the notion of closing higher-order motifs that move beyond closing simple triangles.
Heterogeneous Network Motifs
To address this problem, we propose a fast, parallel, and space-efficient framework for counting typed graphlets in large networks.
Latent Network Summarization: Bridging Network Embedding and Summarization
Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph structure with dimensionality that is independent of the input graph size (i. e., #nodes and #edges), while retaining the ability to derive node representations on the fly.
Social and Information Networks
Higher-order Spectral Clustering for Heterogeneous Graphs
Using typed-graphlets as a basis, we develop a general principled framework for higher-order clustering in heterogeneous networks.
Higher-order Graph Convolutional Networks
Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.
New Insights into Bootstrapping for Bandits
We investigate the use of bootstrapping in the bandit setting.
HONE: Higher-Order Network Embeddings
This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs.
Stochastic Low-Rank Bandits
Many problems in computer vision and recommender systems involve low-rank matrices.
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices, the Isotonic Regression of $y$ is a vector $x$ that respects the partial order described by $G,$ and minimizes $\|x-y\|,$ for a specified norm.
Fast, Provable Algorithms for Isotonic Regression in all $\ell_{p}$-norms
Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices, the Isotonic Regression of $y$ is a vector $x$ that respects the partial order described by $G,$ and minimizes $||x-y||,$ for a specified norm.
Algorithms for Lipschitz Learning on Graphs
We develop fast algorithms for solving regression problems on graphs where one is given the value of a function at some vertices, and must find its smoothest possible extension to all vertices.
