49 citations · 263 across the 36 of their papers we have counts for
15 papers · 1 filter
Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai +2
Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some tr…
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes
Aravind Reddy, Ryan A. Rossi, Zhao Song +6
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 arbi…
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
Sudhanshu Chanpuriya, Ryan A. Rossi, Anup Rao +4
Many models for undirected graphs are based on factorizing the graph's adjacency matrix; these models find a vector representation of each node such that the predicted probability…
Multiscale Manifold Warping
Sridhar Mahadevan, Anup Rao, Georgios Theocharous +1
Many real-world applications require aligning two temporal sequences, including bioinformatics, handwriting recognition, activity recognition, and human-robot coordination. Dynamic…
Coresets for Classification -- Simplified and Strengthened
Tung Mai, Anup B. Rao, Cameron Musco
We give relative error coresets for training linear classifiers with a broad class of loss functions, including the logistic loss and hinge loss. Our construction achieves $(1\pm ε…
Machine Unlearning via Algorithmic Stability
Enayat Ullah, Tung Mai, Anup Rao +2
We study the problem of machine unlearning and identify a notion of algorithmic stability, Total Variation (TV) stability, which we argue, is suitable for the goal of exact unlearn…
Fundamental Tradeoffs in Distributionally Adversarial Training
Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi +2
Adversarial training is among the most effective techniques to improve the robustness of models against adversarial perturbations. However, the full effect of this approach on mode…
Graph Neural Networks with Heterophily
Jiong Zhu, Ryan A. Rossi, Anup Rao +4
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications. However, many existing GNN models have implicitly assumed homophily among the nodes…
Sample Efficient Graph-Based Optimization with Noisy Observations
Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori +2
We study sample complexity of optimizing "hill-climbing friendly" functions defined on a graph under noisy observations. We define a notion of convexity, and we show that a variant…
Model Selection in Contextual Stochastic Bandit Problems
Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori +4
We study bandit model selection in stochastic environments. Our approach relies on a meta-algorithm that selects between candidate base algorithms. We develop a meta-algorithm-base…
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs
Ryan A. Rossi, Anup Rao, Sungchul Kim +3
In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing higher-order network motifs. Th…
New Insights into Bootstrapping for Bandits
Sharan Vaswani, Branislav Kveton, Zheng Wen +3
We investigate the use of bootstrapping in the bandit setting. We first show that the commonly used non-parametric bootstrapping (NPB) procedure can be provably inefficient and est…
Stochastic Low-Rank Bandits
Branislav Kveton, Csaba Szepesvari, Anup Rao +3
Many problems in computer vision and recommender systems involve low-rank matrices. In this work, we study the problem of finding the maximum entry of a stochastic low-rank matrix…
Fast, Provable Algorithms for Isotonic Regression in all -norms
Rasmus Kyng, Anup Rao, Sushant Sachdeva
Given a directed acyclic graph and a set of values on the vertices, the Isotonic Regression of is a vector that respects the partial order described by and mi…
Algorithms for Lipschitz Learning on Graphs
Rasmus Kyng, Anup Rao, Sushant Sachdeva +1
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…