15 citations · 75 across the 31 of their papers we have counts for
35 papers
The Nelson-Nguyen Conjecture via Mean-to-Moments Concentration
Tung Mai, Anup Rao
An oblivious subspace embedding (OSE) is a distribution over matrices that approximately preserves the squared Euclidean norm of every vector in any fixed low-dimensional subspace.…
Constant Swap Regret in General-Sum Games via Two-Scale Higher-Order Optimism
Tung Mai
We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret in self-…
Robust Stable Matchings: Dealing with Changes in Preferences
Rohith Reddy Gangam, Tung Mai, Nitya Raju +1
We study stable matchings that are robust to preference changes in the two-sided stable matching setting of Gale and Shapley [GS62]. Given two instances and on the same set…
ML-Tool-Bench: Tool-Augmented Planning for ML Tasks
Yaswanth Chittepu, Raghavendra Addanki, Tung Mai +2
The development of autonomous machine learning (ML) agents capable of end-to-end data science workflows represents a significant frontier in artificial intelligence. These agents m…
Hallucination Diversity-Aware Active Learning for Text Summarization
Yu Xia, Xu Liu, Tong Yu +5
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported. Existing methods for alleviating hallu…
Dynamic Vector Bin Packing for Online Resource Allocation in the Cloud
Aniket Murhekar, David Arbour, Tung Mai +1
Several cloud-based applications, such as cloud gaming, rent servers to execute jobs which arrive in an online fashion. Each job has a resource demand and must be dispatched to a c…
Optimal Sketching Bounds for Sparse Linear Regression
Tung Mai, Alexander Munteanu, Cameron Musco +3
We study oblivious sketching for -sparse linear regression under various loss functions such as an norm, or from a broad class of hinge-like loss functions, which inclu…
Stable Matching: Dealing with Changes in Preferences
Rohith Reddy Gangam, Tung Mai, Nitya Raju +1
We study stable matchings that are robust to preference changes in the two-sided stable matching setting of Gale and Shapley[GS62]. Given two instances and on the same set…
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 Balanced Experimental Design
David Arbour, Drew Dimmery, Tung Mai +1
e consider the experimental design problem in an online environment, an important practical task for reducing the variance of estimates in randomized experiments which allows for g…
Electra: Conditional Generative Model based Predicate-Aware Query Approximation
Nikhil Sheoran, Subrata Mitra, Vibhor Porwal +5
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 e…
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…
Customs Fraud Detection in the Presence of Concept Drift
Tung-Duong Mai, Kien Hoang, Aitolkyn Baigutanova +2
Capturing the changing trade pattern is critical in customs fraud detection. As new goods are imported and novel frauds arise, a drift-aware fraud detection system is needed to det…
Optimal Space and Time for Streaming Pattern Matching
Tung Mai, Anup Rao, Ryan A. Rossi +1
In this work, we study longest common substring, pattern matching, and wildcard pattern matching in the asymmetric streaming model. In this streaming model, we have random access t…
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…
Responses to COVID-19 with Probabilistic Programming
Assem Zhunis, Tung-Duong Mai, Sundong Kim
The COVID-19 pandemic left its unique mark on the 21st century as one of the most significant disasters in history, triggering governments all over the world to respond with a wide…
Asymptotics of Ridge Regression in Convolutional Models
Mojtaba Sahraee-Ardakan, Tung Mai, Anup Rao +3
Understanding generalization and estimation error of estimators for simple models such as linear and generalized linear models has attracted a lot of attention recently. This is in…
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…
Online Discrepancy Minimization via Persistent Self-Balancing Walks
David Arbour, Drew Dimmery, Tung Mai +1
We study the online discrepancy minimization problem for vectors in in the oblivious setting where an adversary is allowed fix the vectors in…
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…
Active Learning for Human-in-the-Loop Customs Inspection
Sundong Kim, Tung-Duong Mai, Sungwon Han +5
We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected. If the…
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…
Approximate Maximum Matching in Random Streams
Alireza Farhadi, MohammadTaghi Hajiaghayi, Tung Mai +2
In this paper, we study the problem of finding a maximum matching in the semi-streaming model when edges arrive in a random order. In the semi-streaming model, an algorithm receive…