Aarti Singh

Scaling Up AI Alignment AAAI 2026 Data-driven Design of Randomized Control Trials with Guaranteed Treatment Effects ICML 2025 Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect AISTATS 2025 Projection Optimization: A General Framework for Multi-Objective and Multi-Group RLHF ICML 2025 Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect ICML 2025 Learning Social Welfare Functions NIPS 2024 Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs ICLR 2024 Hybrid Reinforcement Learning from Offline Observation Alone ICML 2024 Goodhart’s Law Applies to NLP’s Explanation Benchmarks EACL 2024 The Importance of Online Data: Understanding Preference Fine-tuning via Coverage NIPS 2024 The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms ICML 2023 Adaptation to Misspecified Kernel Regularity in Kernelised Bandits AISTATS 2023 Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality ICML 2023 Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions JMLR 2022 Complete Policy Regret Bounds for Tallying Bandits COLT 2022 A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences AAAI 2021 Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment AAAI 2021 Smooth Bandit Optimization: Generalization to Holder Space AISTATS 2021 PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review JMLR 2021 Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels NIPS 2021 Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information JMLR 2020 Preference-based Reinforcement Learning with Finite-Time Guarantees NIPS 2020 Zeroth Order Non-convex optimization with Dueling-Choice Bandits UAI 2020 Thresholding Bandit Problem with Both Duels and Pulls AISTATS 2020 PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review ALT 2019 On Testing for Biases in Peer Review NIPS 2019 Gradient Descent Provably Optimizes Over-parameterized Neural Networks ICLR 2019 Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent AISTATS 2019 How Many Samples are Needed to Estimate a Convolutional Neural Network? NIPS 2018 Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information ICML 2018 Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima ICML 2018 Stochastic Zeroth-order Optimization in High Dimensions AISTATS 2018 Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data JMLR 2018 Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates NIPS 2018 Near-Optimal Design of Experiments via Regret Minimization ICML 2017 Gradient Descent Can Take Exponential Time to Escape Saddle Points NIPS 2017 Uncorrelation and Evenness: a New Diversity-Promoting Regularizer ICML 2017 Hypothesis Transfer Learning via Transformation Functions NIPS 2017 Noise-Tolerant Interactive Learning Using Pairwise Comparisons NIPS 2017 On the Power of Truncated SVD for General High-rank Matrix Estimation Problems NIPS 2017 Computationally Efficient Robust Sparse Estimation in High Dimensions COLT 2017 On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models JMLR 2017 Graph Connectivity in Noisy Sparse Subspace Clustering AISTATS 2016 Active Learning Algorithms for Graphical Model Selection AISTATS 2016 Data Poisoning Attacks on Factorization-Based Collaborative Filtering NIPS 2016 Column Subset Selection with Missing Data via Active Sampling AISTATS 2015 Differentially private subspace clustering NIPS 2015 Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures AISTATS 2015 On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives AISTATS 2015 A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data ICML 2015 FuSSO: Functional Shrinkage and Selection Operator AISTATS 2014 An Analysis of Active Learning with Uniform Feature Noise AISTATS 2014 Changepoint Detection over Graphs with the Spectral Scan Statistic AISTATS 2013 Detecting Activations over Graphs using Spanning Tree Wavelet Bases AISTATS 2013 Distribution-Free Distribution Regression AISTATS 2013 Cluster Trees on Manifolds NIPS 2013 Optimal rates for stochastic convex optimization under Tsybakov noise condition ICML 2013 Low-Rank Matrix and Tensor Completion via Adaptive Sampling NIPS 2013 Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation NIPS 2013 Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic NIPS 2013 Sparsistency of the Edge Lasso over Graphs AISTATS 2012 Minimax rates for homology inference AISTATS 2012 Stability of Density-Based Clustering JMLR 2012 Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities AISTATS 2011 Noise Thresholds for Spectral Clustering NIPS 2011 Minimax Localization of Structural Information in Large Noisy Matrices NIPS 2011 Detecting Weak but Hierarchically-Structured Patterns in Networks AISTATS 2010 Identifying graph-structured activation patterns in networks NIPS 2010 Unlabeled data: Now it helps, now it doesn't NIPS 2008