15 citations · 50 across the 24 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…
Ranked bandits in metric spaces: learning optimally diverse rankings over large document collections
Aleksandrs Slivkins, Filip Radlinski, Sreenivas Gollapudi
Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few…