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Explore how computational limitations and cryptographic tools can bypass impossibility results in communication complexity and streaming, with examples and open problems discussed.
Explores novel approaches in zero-knowledge proofs, focusing on transforming NIZK to ZAPs with inefficient provers. Discusses implications for cryptographic assumptions and fine-grained complexity settings.
Explores unstructured hard languages in NP ∩ coNP, providing evidence for their existence and introducing a new NIZK proof construction, advancing understanding of computational complexity and cryptography.
Explore the concept of universal arguments and their construction for non-deterministic computations, focusing on recent developments in non-interactive succinct arguments and their universality.
Explore fully-local SSB hash families and their applications in cryptography, including multi-hop aggregate signatures and incrementally verifiable computation.
Explore groundbreaking constructions of SNARGs and correlation-intractable hash functions based on sub-exponential DDH, achieving poly-logarithmic proof sizes for Batch-NP and P.
Explore minimal complexity assumptions in cryptography, focusing on proof systems and their applications in secure computation and blockchain technology.
Innovative approaches to scale human intelligence and education in the era of AI, drawing from game theory and real-world problem-solving experiences across mathematics, pandemic control, and live math education.
Explore the fascinating intersection of theoretical computer science, art, and cinema with Pixar co-founder Alvy Ray Smith, tracing the evolution from pixels to digital movies and its impact on modern computer graphics.
Explores polynomial-time classical algorithm for noisy random circuit sampling, challenging quantum supremacy claims. Discusses computational complexity, experimental implications, and recent progress in quantum computing.
Explores optimal regulation of prediction algorithms in high-stakes decisions, examining transparency, audits, and targeted tools to address incentive misalignment and improve welfare.
Explore quantum learning theory and its surprising ability to predict local properties of arbitrary quantum circuits, contrasting with classical limitations and highlighting potential for efficient quantum dynamics prediction.
Explore techniques for integrating AI tools in safety-critical systems, focusing on guaranteeing performance, stability, and safety while addressing challenges in networked environments.
Explore offline reinforcement learning for safety-critical applications, covering generalization, robustness, and imitation learning in large-scale settings with rich function approximation.
Explore dynamic mechanism design with limited commitment, focusing on direct Blackwell mechanisms and their applications in pricing, bargaining, and product personalization.
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