About Me
I am the Dugald C. Jackson Assistant Professor in the ECE Department at University of WisconsinMadison. I am also affiliated with the Department of Computer Science and the Department of Statistics at UWMadison.
My research broadly spans the areas of Machine Learning, Statistical Inference, and Crowdsourcing. My research vision is to develop theoretically grounded machine learning tools to make reliable inferences using data that comes from people.
Previously, I worked with
Prof. Sham Kakade as a postdoctoral researcher in the Paul G. Allen School of Computer Science and Engineering at University of Washington, Seattle prior to joining UWMadison. I graduated from the Department of Electrical Engineering, California Institute of Technology
(Caltech) where I was adviced by Prof. Babak
Hassibi.
I was supported by the Faculty for the Future fellowship from 20132015, awarded by the Schlumberger Foundation.
Before Caltech,
I obtained my undergraduate degree (B. Tech) from Indian Institute of Technology Madras (IIT Madras) with a
major in Electrical Engineering and a minor in Physics.
[Joining the group] I am looking for highly motivated graduate students. If you are already a graduate student at UWMadison and are interested in working with me, please send me an email describing your research interests and CV. If you are not a graduate student at UWMadison yet, but are interested in joining my research group, please apply to UWMadison graduate program. Unfortunately, I am not able to reply to all the email inquiries from students who are in the process of applying to UWMadison graduate programs.
News
 Work on learning metric from limited pairwise comparisons has been accepted for UAI 2024! Congratulaitons Zhi and Geelon!
 Two papers accepted for AISTATS 2024! Congratulaitons Harit, Lin, Gokcan and Reid!
 Work on framework to audit the characteristics of synthetically generated faces , in collaboration with Harrison, Sheema, Guruprasad and Kassem, accepted at AAAI 2024.
 Work on analyzing the performance on autolabeling systems, in collaboration with Harit, Lin, and Fred, accepted at Neurips 2023 (Spotlight)!
 Work on theoretically grounded practical acitve crowdsourced clustering algorithm, in collaboration with Reid and Babak accepted at AAAI HCOMP 2023.
 Excited to receive NSF CAREER Award!
 Coorganizing the MidWest Machine Learning Symposium (MMLS) 2023 , to be held in Chicago on May 1617, 2023.
 Coorganizer for Neurips 2022 Tutorial on Theory and Practice of Efficient and Accurate Dataset Construction with Fred Sala.
 Paper on learning unknown metric and preference points simultaneously accepted at Neurips 2022.
 Received NSF research grant for “Uncovering the cognitive and neural fingerprints that make each of us unique” in collaboration with Tim Rogers, Rob Nowak and Brad Postle.
 Presented paper on learning preference distributions from distance measurements at Allerton 2022, in collaboration with Gokcan and Rob.
 Received American Family Funding Initiative Award with Fred Sala for our project on understanding foundations of autolabeling systems.
Publications
[Google Scholar Page]
Preprints
Conferences and Journals
 Metric Learning from Limited Pairwise Preference Comparisons
 Zhi Wang, Geelon So, Ramya Korlakai Vinayak
 40th Conference on Uncertainty in Artificial, Barcelona, Spain, (UAI), 2024.
 [pdf] [Abstract]
 Taming False Positives in OutofDistribution Detection with Human Feedback
 Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, (AISTATS), 2024.
 [pdf] [Abstract]
 Learning Populations of Preferences via Pairwise Comparison Queries
 Gokcan Tatli, Reid (Yi) Chen, Ramya Korlakai Vinayak
 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, (AISTATS), 2024.
 [pdf] [Abstract]
 Limitations of Face Image Generation
 Harrison Rosenberg, Shimaa Ahmed, Guruprasad V Ramesh, Ramya Korlakai Vinayak, Kassem Fawaz
 AAAI Conference on Artificial Intelligence (AAAI), 2024.
 [pdf] [Abstract]
 Promises and Pitfalls of Thresholdbased Autolabeling
 Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak
 Neural Information Processing Systems (NeurIPS), 2023. (Spotlight).
 [pdf] [Abstract]
 Crowdsourced Clustering via Active Querying: Practical Algorithm with Theoretical Guarantees
 Reid (Yi) Chen, Ramya Korlakai Vinayak, Babak Hassibi
 The 11th AAAI Conference on Human Computation and Crowdsourcing (HCOMP) , 2023.
 [pdf] [Abstract]
 One for All: Simultaneous Metric and Preference Learning over Multiple Users
 Gregory Canal, Blake Mason, Ramya Korlakai Vinayak, Robert Nowak
 Neural Information Processing Systems (NeurIPS), 2022.
 [pdf] [Abstract]
 Learning Preference Distributions From Distance Measurements
 Gokcan Tatli, Rob Nowak, Ramya Korlakai Vinayak
 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton) , 2022.
 [pdf] [Abstract]
 FisherPitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
 Estimating the number and effect sizes of nonnull hypotheses
 Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson
 International Conference on Machine Learning (ICML), 2020.
 [pdf] [Abstract]
 Maximum Likelihood Estimation for Learning Populations of Parameters
 Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham Kakade
 International Conference on Machine Learning (ICML), 2019.
 [pdf] [Abstract]
 The Illusion of Change: Correcting for Bias when Inferring Changes in Sparse, SocietalScale Data
 Gabriel Cadamuro, Ramya Korlakai Vinayak, Joshua Blumenstock, Sham Kakade, Jacob N. Shapiro
 The Web Conference (WWW), 2019.
 [pdf] [Abstract]
 Also presented at the AI for Social Good ICML 2019 Workshop
 Tensorbased crowdsourced clustering via triangle queries
 Ramya Korlakai Vinayak, Tijana Zrnic, Babak Hassibi.
 International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017.
 [pdf] [Abstract]
 Crowdsourced Clustering: Querying Edges vs Triangles
 Ramya Korlakai Vinayak, Babak Hassibi.
 Neural Information Processing Systems (NeurIPS), 2016.
 [pdf] [Abstract]
 Similarity Clustering in the Presence of Outliers: Exact Recovery via Convex Program
 Ramya Korlakai Vinayak, Babak Hassibi.
 International Symposium on Information Theory (ISIT), 2016.
 [pdf] [Abstract] [code]
 Graph Clustering With Missing Data: Convex Algorithms and Analysis
 Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi.
 Neural Information Processing Systems (NeurIPS), 2014.
 [pdf] [Abstract]
 Sharp Performance Bounds for Graph Clustering via Convex Optimization
 Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi.
 International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014.
 [pdf] [Abstract]
Workshops
 Learning Preference Distributions From Pairwise Comparisons
 Gokcan Tatli, Reid Chen, Ramya Korlakai Vinayak
 International Workshop on Computational Social Choice (COMSOC) , 2023.
 Workshop The Many Facets of PreferenceBased Learning (ICML Workshop) , 2023.
 Adaptive OutofDistribution Detection with HumanintheLoop
 Heguang Lin, Harit Vishwakarma, Ramya Korlakai Vinayak
 Workshop on HumanMachine Collaboration and Teaming, International Conference on Machine Learning (ICML Workshop) , 2022.
 Clustering by Comparison: Stochastic Block Model for Inference in
Crowdsourcing
 Ramya Korlakai Vinayak, Babak Hassibi
 Workshop on Machine Learning and Crowdsourcing, ICML Workshop, 2015.
Thesis
Group
Graduate Students
Postdocs
Undergraduate Students
 Zennia Nie (CS)
 Khoa Cao (CS)
Past Students
 Heguang Lin (CS and Math Undergrad from UWMadison, now pursuing masters at UPenn)
Teaching
 Spring 2024, 2023, 2021: ECE/CS 761 Mathematical Foundations of Machine Learning
 Fall 2023, Fall 2022: ECE 331 Introduction to Random Signal Analysis and Statistics
 Fall 2021: ECE 901 Learning from Small Data (Special Topics Course)
 Fall 2020: ECE/CS/ME 532 Matrix Methods in Machine Learning
Abstracts
 Zhi Wang, Geelon So, Ramya Korlakai Vinayak
 UAI, 2024.

We study metric learning from preference comparisons under the ideal point model, in which a user prefers an item over another if it is closer to their latent ideal item. These items are embedded into R^d
equipped with an unknown Mahalanobis distance shared across users. While recent work shows that it is possible to simultaneously recover the metric and ideal items given O(d) pairwise comparisons per user,
in practice we often have a limited budget of o(d) comparisons. We study whether the metric can still be recovered, even though it is known that learning individual ideal items is now no longer possible. We
show that in general, o(d) comparisons reveals no information about the metric, even with infinitely many
users. However, when comparisons are made over items that exhibit lowdimensional structure, each user
can contribute to learning the metric restricted to a lowdimensional subspace so that the metric can be
jointly identified. We present a divideandconquer approach that achieves this, and provide theoretical
recovery guarantees and empirical validation.
 Harit Vishwakarma, Heguang Lin, Frederic Sala, Ramya Korlakai Vinayak
 Arxiv Preprint, 2022.

Creating largescale highquality labeled datasets is a major bottleneck in supervised machine learning workflows.
Autolabeling systems are a promising way to reduce reliance on manual labeling for dataset construction.
Thresholdbased autolabeling, where validation data obtained from humans is used to find a threshold for confidence above which the data is machinelabeled, is emerging as a popular solution used widely in practice.
Given the long shelflife and diverse usage of the resulting datasets, understanding when the data obtained by such autolabeling systems can be relied on is crucial.
In this work, we analyze thresholdbased autolabeling systems and derive sample complexity bounds on the amount of humanlabeled validation data required for guaranteeing the quality of machinelabeled data.
Our results provide two insights. First, reasonable chunks of the unlabeled data can be automatically and accurately labeled by seemingly bad models. Second, a hidden downside of thresholdbased autolabeling systems is potentially prohibitive validation data usage.
Together, these insights describe the promise and pitfalls of using such systems. We validate our theoretical guarantees with simulations and study the efficacy of thresholdbased autolabeling on real datasets.
 Gregory Canal, Blake Mason, Ramya Korlakai Vinayak, Robert Nowak.
 Neural Information Processing Systems (NeurIPS), 2022, New Orleans, USA.

This paper investigates simultaneous preference and metric learning from a crowd of respondents. A set of items represented by ddimensional feature vectors and paired comparisons of the form ``item i is preferable to item j'' made by each user is given.
Our model jointly learns a distance metric that characterizes the crowd's general measure of item similarities along with a latent ideal point for each user reflecting their individual preferences.
This model has the flexibility to capture individual preferences, while enjoying a metric learning sample cost that is amortized over the crowd.
We first study this problem in a noiseless, continuous response setting (i.e., responses equal to differences of item distances) to understand the fundamental limits of learning.
Next, we establish prediction error guarantees for noisy, binary measurements such as may be collected from human respondents, and show how the sample complexity improves when the underlying metric is lowrank. Finally, we establish recovery guarantees under assumptions on the response distribution.
We demonstrate the performance of our model on both simulated data and on a dataset of color preference judgements across a large number of users.
 Gokcan Tatli, Robert Nowak, Ramya Korlakai Vinayak.
 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton) , 2022.

We introduce the problem of learning a distribution of user preferences over a set of items from noisy responses to distance queries. Rather than aiming to learn the preferences of each user, our goal is only to recover the overall distribution of user preferences. We show that distribution recovery can require just one response from each user. In contrast, learning the preferences of each user would require multiple responses from each user. Thus, learning preference distributions, rather than individual preferences, may be more practical in many applications. The preference distribution problem is formulated on a discrete domain in which items (e.g., products) and users' ideal preference points are located. We study both the noiseless and noisy settings in one dimension and provide sufficient conditions for identifiability of the underlying true distribution as a function of the set of items used for queries. We establish an upper bound on the total variation distance between the true distribution and the distribution learned via constrained least squares optimization problem for both noiseless and noisy settings. While the onedimensional setting we consider is simple, our simulation results show that our proposed recovery technique extends to multidimensional settings and graph structures.
 Zhen Miao, Weihao Kong, Ramya Korlakai Vinayak, Wei Sun, Fang Han.
 Arxiv 2021 (preprint).

This paper investigates the theoretical and empirical performance of FisherPitmantype permutation tests for assessing the equality of unknown Poisson mixture distributions. Building on nonparametric maximum likelihood estimators (NPMLEs) of the mixing distribution, these tests are theoretically shown to be able to adapt to complicated unspecified structures of count data and also consistent against their corresponding ANOVAtype alternatives; the latter is a result in parallel to classic claims made by Robinson (Robinson, 1973). The studied methods are then applied to a singlecell RNAseq data obtained from different cell types from brain samples of autism subjects and healthy controls; empirically, they unveil genes that are differentially expressed between autism and control subjects yet are missed using common tests. For justifying their use, rate optimality of NPMLEs is also established in settings similar to nonparametric Gaussian (Wu and Yang, 2020a) and binomial mixtures (Tian et al., 2017; Vinayak et al., 2019).
 Ramya Korlakai Vinayak, Weihao Kong, Sham Kakade.
 Arxiv 2019 (preprint).

Paired estimation of change in parameters of interest over a population plays a central role in several application domains including those in the social sciences, epidemiology, medicine and biology. In these domains, the size of the population under study is often very large, however, the number of observations available per individual in the population is very small (\emph{sparse observations}) which makes the problem challenging. Consider the setting with $N$ independent individuals, each with unknown parameters $(p_i, q_i)$ drawn from some unknown distribution on $[0, 1]^2$. We observe $X_i \sim \text{Bin}(t, p_i)$ before an event and $Y_i \sim \text{Bin}(t, q_i)$ after the event. Provided these paired observations, $\{(X_i, Y_i) \}_{i=1}^N$, our goal is to accurately estimate the \emph{distribution of the change in parameters}, $\delta_i := q_i  p_i$, over the population and properties of interest like the \emph{$\ell_1$magnitude of the change} with sparse observations ($t\ll N$).
We provide \emph{information theoretic lower bounds} on the error in estimating the distribution of change and the $\ell_1$magnitude of change. Furthermore, we show that the following two step procedure achieves the optimal error bounds: first, estimate the full joint distribution of the paired parameters using the maximum likelihood estimator (MLE) and then estimate the distribution of change and the $\ell_1$magnitude of change using the joint MLE.
Notably, and perhaps surprisingly, these error bounds are of the same order as the minimax optimal error bounds for learning the \emph{full} joint distribution itself (in Wasserstein1 distance);
in other words, estimating the magnitude of the change of parameters over the population is, in a minimax sense, as difficult as estimating the full joint distribution itself.
 Jennifer Brennan, Ramya Korlakai Vinayak, Kevin Jamieson
 International Conference on Machine Learning (ICML) 2020

We study the problem of estimating the distribution of effect sizes (the mean of the test statistic under the alternate hypothesis) in a multiple testing setting. Knowing this distribution allows us to calculate the power (type II error) of any experimental design. We show that it is possible to estimate this distribution using an inexpensive pilot experiment, which takes significantly fewer samples than would be required by an experiment that identified the discoveries. Our estimator can be used to guarantee the number of discoveries that will be made using a given experimental design in a future experiment. We prove that this simple and computationally efficient estimator enjoys a number of favorable theoretical properties, and demonstrate its effectiveness on data from a gene knockout experiment on influenza inhibition in Drosophila.
 Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham Kakade.
 International Conference on Machine Learning (ICML) 2019, Long Beach, CA.

Consider a setting with $N$ independent individuals, each with an unknown
parameter, $p_i \in [0, 1]$ drawn from some unknown distribution $P^\star$.
After observing the outcomes of $t$ independent Bernoulli trials, i.e., $X_i
\sim \text{Binomial}(t, p_i)$ per individual, our objective is to
accurately estimate $P^\star$. This problem arises in numerous
domains, including the social sciences, psychology, healthcare, and
biology, where the size of the population under study is usually large
while the number of observations per individual is often limited.
Our main result shows that, in the regime where $t <<N$, the maximum
likelihood estimator (MLE) is both statistically minimax optimal and
efficiently computable. Precisely, for sufficiently large $N$, the MLE
achieves the information theoretic optimal error bound of
$\mathcal{O}(\frac{1}{t})$ for $t < c\log{N}$, with regards to the
earth mover's distance (between the estimated and true distributions).
More generally, in an exponentially large interval of $t$ beyond $c
\log{N}$, the MLE achieves the minimax error bound of
$\mathcal{O}(\frac{1}{\sqrt{t\log N}})$. In contrast, regardless of
how large $N$ is, the naive "plugin" estimator for this problem only
achieves the suboptimal error of $\Theta(\frac{1}{\sqrt{t}})$.
 Gabriel Cadamuro, Ramya Korlakai Vinayak, Joshua Blumenstock, Sham Kakade, Jacob N. Shapiro.
 The Web Conference 2019, San Francisco, CA.

Societalscale data is playing an increasingly prominent role in social science
research; examples from research on geopolitical events include questions on
how emergency events impact the diffusion of information or how new policies
change patterns of social interaction. Such research often draws critical
inferences from observing how an exogenous event changes meaningful metrics
like network degree or network entropy. However, as we show in this work,
standard estimation methodologies make systematically incorrect inferences when
the event also changes the sparsity of the data.
To address this issue, we provide a general framework for inferring changes in
social metrics when dealing with nonstationary sparsity. We propose a plugin
correction that can be applied to any estimator, including several recently
proposed procedures. Using both simulated and real data, we demonstrate that
the correction significantly improves the accuracy of the estimated change
under a variety of plausible data generating processes. In particular, using a
large dataset of calls from Afghanistan, we show that whereas traditional
methods substantially overestimate the impact of a violent event on social
diversity, the plugin correction reveals the true response to be much more
modest.
 Ramya Korlakai Vinayak, Babak Hassibi.
 Neural Information Processing Systems (NIPS) 2016, Barcelona, Spain.

We consider the task of clustering items using answers from nonexpert crowd
workers. In such cases, the workers are often not able to label the items
directly, however, it is reasonable to assume that they can compare items and
judge whether they are similar or not. An important question is what queries to
make, and we compare two types: random edge queries, where a pair of items is
revealed, and random triangles, where a triple is. Since it is far too expensive
to query all possible edges and/or triangles, we need to work with partial
observations subject to a fixed query budget constraint. When a generative model
for the data is available (and we consider a few of these) we determine the cost
of a query by its entropy; when such models do not exist we use the average
response time per query of the workers as a surrogate for the cost. In addition
to theoretical justification, through several simulations and experiments on two
real data sets on Amazon Mechanical Turk, we empirically demonstrate that, for a
fixed budget, triangle queries uniformly outperform edge queries. Even though,
in contrast to edge queries, triangle queries reveal dependent edges, they
provide more reliable edges and, for a fixed budget, many more of them. We also
provide a sufficient condition on the number of observations, edge densities
inside and outside the clusters and the minimum cluster size required for the
exact recovery of the true adjacency matrix via triangle queries using a convex
optimizationbased clustering algorithm.
 Ramya Korlakai Vinayak, Tijana Zrnic, Babak Hassibi.
 42nd International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2017, New Orleans.

We consider the problem of crowdsourced clustering of a set of items based on queries of the similarity of triple of objects. Such an approach, called triangle queries, where it was shown that, for a fixed query budget, it outperforms clustering based on edge queries (i.e, comparing pairs of objects). In [1] the clustering algorithm for triangle and edge queries was identical and each triangle query response was treated as 3 separate edge query responses. In this paper we directly exploit the triangle structure of the responses by embedding them into a 3way tensor. Since there are 5 possible responses to each triangle query, it is a priori not clear how best to embed them into the tensor. We give sufficient conditions on nontrivial embedding such that the resulting tensor has a rank equal to the underlying number of clusters (akin to what happens with the rank of the adjacency matrix). We then use an alternating least squares tensor decomposition algorithm to cluster a noisy and partially observed tensor and show, through extensive numerical simulations, that it significantly outperforms methods that make use only of the adjacency matrix.
 Ramya Korlakai Vinayak, Babak Hassibi.
 International Symposium on Information Theory (ISIT) 2016, Barcelona, Spain.

We study the problem of clustering a set of data points based on their
similarity matrix, each entry of which represents the similarity between the
corresponding pair of points. We propose a convexoptimizationbased algorithm for
clustering using the similarity matrix, which has provable recovery guarantees. It needs no prior knowledge of the number of clusters
and it behaves in a robust way in the presence of outliers and noise. Using a
generative stochastic model for the similarity matrix (which can be thought of
as a generalization of the classical Stochastic Block Model) we obtain \emph{precise bounds} (not orderwise)
on the sizes of the clusters, the number of outliers, the noise variance, separation between the mean similarities inside and outside the clusters
and the values of the regularization parameter that guarantee the exact recovery of the clusters with high probability. The theoretical findings are corroborated
with extensive evidence from simulations.
 Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi.
 Neural Information Processing Systems (NIPS) 2014, Montreal, Canada.

We consider the problem of finding clusters in graphs which are partially
observed. We analyze two programs based on regularized nuclear norm
minimization, with a goal to recover the low rank part of the adjacency
matrix. For the commonly used Stochastic Block Model, we provide explicit
conditions in terms of the size and sparsity of the clusters, amount of observed
data, and the regularization parameter of the convex program that guarantees
successful recovery of the clusters. We also corroborate our theoretical findings
with extensive simulations, and also run our algorithms on a real data set
obtained from crowdsourcing an image classification task on Amazon Mechanical
Turk.
 Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi.
 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014, Florence, Italy.

We analyze a popular approach towards identifying clusters in graphs, that of a
sparse plus lowrank decomposition of the adjacency matrix of a graph via a
simple and intuitive convex program. We sharply characterize the conditions for
the success and failure of this program in identifying clusters. We also present
extensive simulations that corroborate our theoretical findings.
Contact
Ramya Korlakai Vinayak
Assistant Professor
Department of ECE
University of WisconsinMadison
email: ramya[at]ece[dot]wisc[dot]edu