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Chirag Pabbaraju

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Preprint Aug 2026

Optimal Unambiguous DNFs and Alon-Saks-Seymour

We construct unambiguous DNFs having width $O(n)$ but $0$-certificate complexity $\Omega(n^2)$. By utilizing the special structure of these DNFs, we prove a lifting theorem with a constant-sized gadget that lifts the DNF to a communication problem, while losslessly translating the separation in certificate complexity to a separation in communication complexity. This leads to an optimal refutation of the Alon-Saks-Seymour conjecture, as well as an optimal communication lower bound for the Clique versus Independent Set problem, improving the previous results of Balodis, Ben-David, G\"{o}\"{o}s, Jain and Kothari (FOCS 2021, SICOMP 2023) by several doubly logarithmic factors. As further applications of our construction to query complexity and learning theory, we exhibit: (a) a family of Boolean functions that has an optimal quartic separation between certificate complexity and approximate degree, and (b) a sample compression lower bound of $\Omega(\sqrt{\log c})$ for multiclass concept classes over $c$ labels.

Chirag Pabbaraju · 0 citations
Preprint Aug 2026

When Clean Data Hurts: Learning with Monotone Corruptions Beyond Binary Classification

This work exhibits a learnable multiclass problem that becomes altogether unlearnable under a monotone adversary, and shows an analogous result for partial binary concept classes, and demonstrates that monotone adversaries are frighteningly more powerful in each of these settings.

Julian Asilis, S. Dughmi, Chirag Pabbaraju · 0 citations

The Optimal Sample Complexity of Multiclass and List Learning

It is shown that the maximum hypergraph density of any multiclass hypothesis class is upper-bounded by its DS dimension, which proves a longstanding conjecture of Daniely and Shalev-Shwartz (2014) and determines the optimal dependence of the sample complexity on the DS dimension for multiclass as well as list learning.

Chirag Pabbaraju · 7 citations · ⚡4