15 citations · 50 across the 24 of their papers we have counts for
29 papers
Efficient Online Conformal Selection with Limited Feedback
Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala +1
We address the problem of conformal selection, where an agent must select a low-cost subset of options to ensure that at least one "success" is identified at a pre-specified target…
Compact Conformal Subgraphs
Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala +1
Conformal prediction provides rigorous, distribution-free uncertainty guarantees, but often yields prohibitively large prediction sets in structured domains such as routing, planni…
Why is My Route Different Today? An Algorithm for Explaining Route Selection
Aaron Schild, Sreenivas Gollapudi, Anupam Gupta +2
Users of routing services like Apple Maps, Google Maps, and Waze frequently wonder why a given route is proposed. This question particularly arises when dynamic conditions like tra…
Online Distributed Queue Length Estimation
Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im +2
Queue length monitoring is a commonly arising problem in numerous applications such as queue management systems, scheduling, and traffic monitoring. Motivated by such applications,…
Sample-Efficient Optimization over Generative Priors via Coarse Learnability
Pranjal Awasthi, Sreenivas Gollapudi, Ravi Kumar +1
We study zeroth-order optimization where solutions must minimize a cost while maintaining high probability under a complex generative prior (e.g., a parameterized mod…
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Eric Zhao, Pranjal Awasthi, Sreenivas Gollapudi
Sampling-based search, a simple paradigm for utilizing test-time compute, involves generating multiple candidate responses and selecting the best one -- typically by having models…
ReMI: A Dataset for Reasoning with Multiple Images
Mehran Kazemi, Nishanth Dikkala, Ankit Anand +8
With the continuous advancement of large language models (LLMs), it is essential to create new benchmarks to effectively evaluate their expanding capabilities and identify areas fo…
First Passage Percolation with Queried Hints
Kritkorn Karntikoon, Yiheng Shen, Sreenivas Gollapudi +3
Solving optimization problems leads to elegant and practical solutions in a wide variety of real-world applications. In many of those real-world applications, some of the informati…
Data Exchange Markets via Utility Balancing
Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im +3
This paper explores the design of a balanced data-sharing marketplace for entities with heterogeneous datasets and machine learning models that they seek to refine using data from…
When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-making
Kate Donahue, Sreenivas Gollapudi, Kostas Kollias
Historically, much of machine learning research has focused on the performance of the algorithm alone, but recently more attention has been focused on optimizing joint human-algori…
Congested Bandits: Optimal Routing via Short-term Resets
Pranjal Awasthi, Kush Bhatia, Sreenivas Gollapudi +1
For traffic routing platforms, the choice of which route to recommend to a user depends on the congestion on these routes -- indeed, an individual's utility depends on the number o…
Online Learning and Bandits with Queried Hints
Aditya Bhaskara, Sreenivas Gollapudi, Sungjin Im +2
We consider the classic online learning and stochastic multi-armed bandit (MAB) problems, when at each step, the online policy can probe and find out which of a small number ()…
Affinity-Aware Graph Networks
Ameya Velingker, Ali Kemal Sinop, Ira Ktena +2
Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform -- a…
Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias +3
We consider the following variant of contextual linear bandits motivated by routing applications in navigational engines and recommendation systems. We wish to learn a hidden -d…
Almost Envy-free Repeated Matching in Two-sided Markets
Sreenivas Gollapudi, Kostas Kollias, Benjamin Plaut
A two-sided market consists of two sets of agents, each of whom have preferences over the other (Airbnb, Upwork, Lyft, Uber, etc.). We propose and analyze a repeated matching probl…
Predict and Match: Prophet Inequalities with Uncertain Supply
Reza Alijani, Siddhartha Banerjee, Sreenivas Gollapudi +2
We consider the problem of selling perishable items to a stream of buyers in order to maximize social welfare. A seller starts with a set of identical items, and each arriving buye…
Hiring Under Uncertainty
Manish Raghavan, Manish Purohit, Sreenivas Gollupadi
In this paper we introduce the hiring under uncertainty problem to model the questions faced by hiring committees in large enterprises and universities alike. Given a set of el…
On the Learnability of Deep Random Networks
Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar +1
In this paper we study the learnability of deep random networks from both theoretical and practical points of view. On the theoretical front, we show that the learnability of rando…
Cost Sharing in Two-Sided Markets
Sreenivas Gollapudi, Kostas Kollias, Ali Shameli
Motivated by the emergence of popular service-based two-sided markets where sellers can serve multiple buyers at the same time, we formulate and study the {\em two-sided cost shari…
Minimizing Latency in Online Ride and Delivery Services
Abhimanyu Das, Sreenivas Gollapudi, Anthony Kim +2
Motivated by the popularity of online ride and delivery services, we study natural variants of classical multi-vehicle minimum latency problems where the objective is to route a se…
Two-sided Facility Location
Reza Alijani, Siddhartha Banerjee, Sreenivas Gollapudi +2
Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediat…
Algorithms for Low Rank Approximation
Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar +3
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the entrywise -approximation error, for any ; the case is t…
Optimal Auctions via the Multiplicative Weight Method
Anand Bhalgat, Sreenivas Gollapudi, Kamesh Munagala
We show that the multiplicative weight update method provides a simple recipe for designing and analyzing optimal Bayesian Incentive Compatible (BIC) auctions, and reduces the time…
Structured Query Reformulations in Commerce Search
Sreenivas Gollapudi, Samuel Ieong, Anitha Kannan
Recent work in commerce search has shown that understanding the semantics in user queries enables more effective query analysis and retrieval of relevant products. However, due to…