1 citations · 1 across the 10 of their papers we have counts for
11 papers
Understanding Human Perception of Representation in Citizens' Assemblies: An Empirical Study
Yusuf Hakan Kalayci, Vasilis Varsamis, Nick Gill +1
Citizens' assemblies are deliberative bodies intended to form a microcosm of the population. Organizers rely on quota-based stratification and must decide which attributes define r…
Batched Pandora's Box
Shaddin Dughmi, Yusuf Hakan Kalayci, Vasilis Livanos +1
Motivated by numerous parallelizable stochastic search problems, most notable and timely among them being LLM inference-time scaling, we propose and study batched versions of the P…
Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
Shaddin Dughmi, Mahdi Haghifam, Yusuf Hakan Kalayci
Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical reasoning or hidden-test execut…
Temporal Panel Selection in Ongoing Citizens' Assemblies
Yusuf Hakan Kalayci, Evi Micha
Permanent citizens' assemblies are ongoing deliberative bodies composed of randomly selected citizens, organized into panels that rotate over time. Unlike one-off panels, which rep…
LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning
Ömer Faruk Akgül, Yusuf Hakan Kalaycı, Rajgopal Kannan +2
Large reasoning models achieve strong performance on complex tasks by generating extended chains of thought, but they often "overthink": continuing to reason long after they have e…
Near-Optimal Sparsifiers for Stochastic Knapsack and Assignment Problems
Shaddin Dughmi, Yusuf Hakan Kalayci, Xinyu Liu
When uncertainty meets costly information gathering, a fundamental question emerges: which data points should we probe to unlock near-optimal solutions? Sparsification of stochasti…
Optimal Stopping vs Best-of- for Inference Time Optimization
Yusuf Kalayci, Vinod Raman, Shaddin Dughmi
Large language model (LLM) generation often requires balancing output quality against inference cost, especially when using multiple generations. We introduce a new framework for i…
Full Proportional Justified Representation
Yusuf Hakan Kalayci, Jiasen Liu, David Kempe
In multiwinner approval voting, forming a committee that proportionally represents voters' approval ballots is an essential task. The notion of justified representation (JR) demand…
Is Transductive Learning Equivalent to PAC Learning?
Shaddin Dughmi, Yusuf Kalayci, Grayson York
Much of learning theory is concerned with the design and analysis of probably approximately correct (PAC) learners. The closely related transductive model of learning has recently…
Proportional Representation in Metric Spaces and Low-Distortion Committee Selection
Yusuf Hakan Kalayci, David Kempe, Vikram Kher
We introduce a novel definition for a small set R of k points being "representative" of a larger set in a metric space. Given a set V (e.g., documents or voters) to represent, and…
Limitations of Stochastic Selection with Pairwise Independent Priors
Shaddin Dughmi, Yusuf Hakan Kalayci, Neel Patel
Motivated by the growing interest in correlation-robust stochastic optimization, we investigate stochastic selection problems beyond independence. Specifically, we consider the ins…