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Anup Rao

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9 papers
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9

RLC Conference 2025 Conference Paper

Adaptive Submodular Policy Optimization

  • Branislav Kveton
  • Anup Rao
  • Viet Dac Lai
  • Nikos Vlassis
  • David Arbour

We propose KL-regularized policy optimization for adaptive submodular maximization, which is a framework for decision making under uncertainty with submodular rewards. Policy optimization of adaptive submodular functions justifies a surprisingly simple and efficient policy gradient update, where the optimized action only affects its immediate reward but not the future ones. It also allows us to learn adaptive submodular policies with large action spaces, such as those represented by large language models (LLMs). We prove that our policies monotonically improve as the regularization diminishes and converge to the optimal greedy policy. Our experiments show major gains in statistical efficiency, in both synthetic problems and LLMs.

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RLJ Journal 2025 Journal Article

Adaptive Submodular Policy Optimization

  • Branislav Kveton
  • Anup Rao
  • Viet Dac Lai
  • Nikos Vlassis
  • David Arbour

We propose KL-regularized policy optimization for adaptive submodular maximization, which is a framework for decision making under uncertainty with submodular rewards. Policy optimization of adaptive submodular functions justifies a surprisingly simple and efficient policy gradient update, where the optimized action only affects its immediate reward but not the future ones. It also allows us to learn adaptive submodular policies with large action spaces, such as those represented by large language models (LLMs). We prove that our policies monotonically improve as the regularization diminishes and converge to the optimal greedy policy. Our experiments show major gains in statistical efficiency, in both synthetic problems and LLMs.

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AAAI Conference 2022 Conference Paper

Conditional Generative Model Based Predicate-Aware Query Approximation

  • Nikhil Sheoran
  • Subrata Mitra
  • Vibhor Porwal
  • Siddharth Ghetia
  • Jatin Varshney
  • Tung Mai
  • Anup Rao
  • Vikas Maddukuri

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. Recently proposed Machine- Learning-based AQP techniques can provide very low latency as query execution only involves model inference as compared to traditional query processing on database clusters. However, with increase in the number of filtering predicates (WHERE clauses), the approximation error significantly increases for these methods. Analysts often use queries with a large number of predicates for insights discovery. Thus, maintaining low approximation error is important to prevent analysts from drawing misleading conclusions. In this paper, we propose ELECTRA, a predicate-aware AQP system that can answer analytics-style queries with a large number of predicates with much smaller approximation errors. ELEC- TRA uses a conditional generative model that learns the conditional distribution of the data and at run-time generates a small (≈ 1000 rows) but representative sample, on which the query is executed to compute the approximate result. Our evaluations with four different baselines on three real-world datasets show that ELECTRA provides lower AQP error for large number of predicates compared to baselines.

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NeurIPS Conference 2022 Conference Paper

Sample Constrained Treatment Effect Estimation

  • Raghavendra Addanki
  • David Arbour
  • Tung Mai
  • Cameron Musco
  • Anup Rao

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 treatment on a population of $n$ individuals. In particular, we study \textit{sample-constrained treatment effect estimation}, where we must select a subset of $s \ll n$ individuals from the population to experiment on. This subset must be further partitioned into treatment and control groups. Algorithms for partitioning the entire population into treatment and control groups, or for choosing a single representative subset, have been well-studied. The key challenge in our setting is jointly choosing a representative subset and a partition for that set. We focus on both individual and average treatment effect estimation, under a linear effects model. We give provably efficient experimental designs and corresponding estimators, by identifying connections to discrepancy minimization and leverage-score-based sampling used in randomized numerical linear algebra. Our theoretical results obtain a smooth transition to known guarantees when $s$ equals the population size. We also empirically demonstrate the performance of our algorithms.

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NeurIPS Conference 2021 Conference Paper

Coresets for Classification – Simplified and Strengthened

  • Tung Mai
  • Cameron Musco
  • Anup Rao

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 $(1\pm \epsilon)$ relative error with $\tilde O(d \cdot \mu_y(X)^2/\epsilon^2)$ points, where $\mu_y(X)$ is a natural complexity measure of the data matrix $X \in \mathbb{R}^{n \times d}$ and label vector $y \in \{-1, 1\}^n$, introduced by Munteanu et al. 2018. Our result is based on subsampling data points with probabilities proportional to their $\ell_1$ $Lewis$ $weights$. It significantly improves on existing theoretical bounds and performs well in practice, outperforming uniform subsampling along with other importance sampling methods. Our sampling distribution does not depend on the labels, so can be used for active learning. It also does not depend on the specific loss function, so a single coreset can be used in multiple training scenarios.

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AAAI Conference 2021 Conference Paper

Graph Neural Networks with Heterophily

  • Jiong Zhu
  • Ryan A. Rossi
  • Anup Rao
  • Tung Mai
  • Nedim Lipka
  • Nesreen K. Ahmed
  • Danai Koutra

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 connected in the graph, and therefore have largely overlooked the important setting of heterophily, where most connected nodes are from different classes. In this work, we propose a novel framework called CPGNN that generalizes GNNs for graphs with either homophily or heterophily. The proposed framework incorporates an interpretable compatibility matrix for modeling the heterophily or homophily level in the graph, which can be learned in an end-to-end fashion, enabling it to go beyond the assumption of strong homophily. Theoretically, we show that replacing the compatibility matrix in our framework with the identity (which represents pure homophily) reduces to GCN. Our extensive experiments demonstrate the effectiveness of our approach in more realistic and challenging experimental settings with significantly less training data compared to previous works: CPGNN variants achieve state-of-the-art results in heterophily settings with or without contextual node features, while maintaining comparable performance in homophily settings.

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NeurIPS Conference 2020 Conference Paper

Model Selection in Contextual Stochastic Bandit Problems

  • Aldo Pacchiano
  • My Phan
  • Yasin Abbasi Yadkori
  • Anup Rao
  • Julian Zimmert
  • Tor Lattimore
  • Csaba Szepesvari

We study bandit model selection in stochastic environments. Our approach relies on a master algorithm that selects between candidate base algorithms. We develop a master-base algorithm abstraction that can work with general classes of base algorithms and different type of adversarial master algorithms. 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. We show through a lower bound that even when one of the base algorithms has $O(\log T)$ regret, in general it is impossible to get better than $\Omega(\sqrt{T})$ regret in model selection, even asymptotically. Using our techniques, we address model selection in a variety of problems such as misspecified linear contextual bandits \citep{lattimore2019learning}, linear bandit with unknown dimension \citep{Foster-Krishnamurthy-Luo-2019} and reinforcement learning with unknown feature maps. Our algorithm requires the knowledge of the optimal base regret to adjust the master learning rate. We show that without such prior knowledge any master can suffer a regret larger than the optimal base regret.

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NeurIPS Conference 2015 Conference Paper

Fast, Provable Algorithms for Isotonic Regression in all L_p-norms

  • Rasmus Kyng
  • Anup Rao
  • Sushant Sachdeva

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. This paper gives improved algorithms for computing the Isotonic Regression for all weighted $\ell_{p}$-norms with rigorous performance guarantees. Our algorithms are quite practical, and their variants can be implemented to run fast in practice.

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YNIMG Journal 2010 Journal Article

Longitudinal changes in medial temporal cortical thickness in normal subjects with the APOE-4 polymorphism

  • Markus Donix
  • Alison C. Burggren
  • Nanthia A. Suthana
  • Prabha Siddarth
  • Arne D. Ekstrom
  • Allison K. Krupa
  • Michael Jones
  • Anup Rao

People with the apolipoprotein-Eε4 (APOE-4) genetic risk for Alzheimer's disease show morphologic differences in medial temporal lobe regions when compared to non-carriers of the allele. Using a high-resolution MRI and cortical unfolding approach, our aim was to determine the rate of cortical thinning among medial temporal lobe subregions over the course of 2 years. We hypothesized that APOE-4 genetic risk would contribute to longitudinal cortical thickness change in the subiculum and entorhinal cortex, regions preferentially susceptible to Alzheimer's disease related pathology. Thirty-two cognitively intact subjects, mean age 61 years, 16 APOE-4 carriers, 16 non-carriers, underwent baseline and follow-up MRI scans. Over this relatively brief interval, we found significantly greater cortical thinning in the subiculum and entorhinal cortex of APOE-4 carriers when compared to non-carriers of the allele. Average cortical thinning across all medial temporal lobe subregions combined was also significantly greater for APOE-4 carriers. This finding is consistent with the hypothesis that carrying the APOE-4 allele renders subjects at a higher risk for developing Alzheimer's disease.

Details DOI
v2026.09.13