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08/28/2023
Goodhart's Law Applies to NLP's Explanation Benchmarks
Despite the rising popularity of saliency-based explanations, the resear...
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06/09/2023
Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
We consider robust empirical risk minimization (ERM), where model parame...
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06/04/2023
Deep learning powered real-time identification of insects using citizen science data
Insect-pests significantly impact global agricultural productivity and q...
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05/04/2023
Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality
In recommender system or crowdsourcing applications of online learning, ...
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04/26/2023
Adaptation to Misspecified Kernel Regularity in Kernelised Bandits
In continuum-armed bandit problems where the underlying function resides...
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03/23/2023
Predicting the Initial Conditions of the Universe using Deep Learning
Finding the initial conditions that led to the current state of the univ...
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03/01/2023
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
We propose a novel approach to addressing two fundamental challenges in ...
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04/24/2022
Complete Policy Regret Bounds for Tallying Bandits
Policy regret is a well established notion of measuring the performance ...
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04/05/2022
Integrating Rankings into Quantized Scores in Peer Review
In peer review, reviewers are usually asked to provide scores for the pa...
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12/08/2021
Best Arm Identification under Additive Transfer Bandits
We consider a variant of the best arm identification (BAI) problem in mu...
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12/11/2020
Smooth Bandit Optimization: Generalization to Hölder Space
We consider bandit optimization of a smooth reward function, where the g...
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11/30/2020
A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions
Peer review is the backbone of academia and humans constitute a cornerst...
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11/30/2020
A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences
Conference peer review constitutes a human-computation process whose imp...
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11/30/2020
Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review
Modern machine learning and computer science conferences are experiencin...
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10/08/2020
Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment
We consider the issue of strategic behaviour in various peer-assessment ...
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06/21/2020
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions
A number of applications require two-sample testing on ranked preference...
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06/16/2020
Preference-based Reinforcement Learning with Finite-Time Guarantees
Preference-based Reinforcement Learning (PbRL) replaces reward values in...
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12/31/2019
On Testing for Biases in Peer Review
We consider the issue of biases in scholarly research, specifically, in ...
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11/03/2019
Zeroth Order Non-convex optimization with Dueling-Choice Bandits
We consider a novel setting of zeroth order non-convex optimization, whe...
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10/16/2019
Active Learning for Graph Neural Networks via Node Feature Propagation
Graph Neural Networks (GNNs) for prediction tasks like node classificati...
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10/14/2019
Thresholding Bandit Problem with Both Duels and Pulls
The Thresholding Bandit Problem (TBP) aims to find the set of arms with ...
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10/25/2018
Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty
An important task in structural design is to quantify the structural per...
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10/04/2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
One of the mystery in the success of neural networks is randomly initial...
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06/16/2018
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
We consider the problem of automated assignment of papers to reviewers i...
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06/08/2018
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
In supervised learning, we leverage a labeled dataset to design methods ...
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05/26/2018
Robust Nonparametric Regression under Huber's ε-contamination Model
We consider the non-parametric regression problem under Huber's ϵ-contam...
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05/21/2018
How Many Samples are Needed to Learn a Convolutional Neural Network?
A widespread folklore for explaining the success of convolutional neural...
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04/22/2018
Local White Matter Architecture Defines Functional Brain Dynamics
Large bundles of myelinated axons, called white matter, anatomically con...
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03/22/2018
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
We consider the problem of global optimization of an unknown non-convex ...
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02/13/2018
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
A major challenge in understanding the generalization of deep learning i...
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12/03/2017
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
We consider the problem of learning a one-hidden-layer neural network wi...
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11/14/2017
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach
The experimental design problem concerns the selection of k points from ...
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10/29/2017
Stochastic Zeroth-order Optimization in High Dimensions
We consider the problem of optimizing a high-dimensional convex function...
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05/29/2017
Gradient Descent Can Take Exponential Time to Escape Saddle Points
Although gradient descent (GD) almost always escapes saddle points asymp...
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04/19/2017
Noise-Tolerant Interactive Learning from Pairwise Comparisons
We study the problem of interactively learning a binary classifier using...
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02/24/2017
Computationally Efficient Robust Estimation of Sparse Functionals
Many conventional statistical procedures are extremely sensitive to seem...
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02/22/2017
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems
We show that given an estimate A that is close to a general high-rank po...
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02/09/2017
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates
Although a majority of the theoretical literature in high-dimensional st...
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10/24/2016
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data
Subspace clustering is the problem of partitioning unlabeled data points...
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02/06/2016
Classification Accuracy as a Proxy for Two Sample Testing
When data analysts train a classifier and check if its accuracy is signi...
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02/01/2016
Active Learning Algorithms for Graphical Model Selection
The problem of learning the structure of a high dimensional graphical mo...
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01/23/2016
Minimax Lower Bounds for Linear Independence Testing
Linear independence testing is a fundamental information-theoretic and s...
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01/09/2016
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
We derive computationally tractable methods to select a small subset of ...
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12/16/2015
Signal Representations on Graphs: Tools and Applications
We present a framework for representing and modeling data on graphs. Bas...
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08/04/2015
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing
Nonparametric two sample testing is a decision theoretic problem that in...
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06/02/2015
Extreme Compressive Sampling for Covariance Estimation
This paper studies the problem of estimating the covariance of a collect...
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05/17/2015
Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data
We consider the problem of matrix column subset selection, which selects...
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05/15/2015
An Analysis of Active Learning With Uniform Feature Noise
In active learning, the user sequentially chooses values for feature X a...
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05/15/2015
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization
Interesting theoretical associations have been established by recent pap...
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05/03/2015