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        "event_title":"Innovation Policy in Product-Line Markets: Evidence from GPUs",
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        "combined_title":"Innovation Policy in Product-Line Markets: Evidence from GPUs: Jiahao Chen, University of Michigan",
        "event_subtitle":"Jiahao Chen, University of Michigan",
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        "description":"I study the effects of innovation policies in product-line markets, using a dynamic oligopoly model with endogenous pricing, product-line adjustment, and R&D. I apply the model to the desktop GPU market, a duopoly between NVIDIA and AMD. I find that a dollar of R&D generates about $9.0 of within-market surplus net of that dollar, with the firms capturing $3.0 and consumers the rest. But the return to public support is far more modest. I simulate three stylized interventions: an R&D credit and two product rebates, one at the cutting edge and one on the mass market. Only the R&D credit induces a material increase in private R&D, but it returns only $0.51 of surplus per public dollar because additionality is low: most of its outlay reimburses research the firms would have conducted anyway. The rebate on cutting-edge products returns only $0.23 per public dollar and sets back the technology frontier. Subsidizing both firms, it ignites a price war at the top of the line, benefiting consumers but undercutting the reason firms advance the frontier. The mass-market rebate sets off the same war lower down the product line but expands adoption enough that R&D survives, returning $1.09 per public dollar. These results suggest that innovation policy in product-line markets is a more intricate design problem than in markets with simpler structure. Each instrument induces a distinct equilibrium response in prices, qualities, and R&D. The static and dynamic components of that response can offset or reinforce each other, attenuating or even reversing the intended effect.",
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        "event_title":"MonarchAttention: Zero-Shot Conversion to Fast Structured Attention",
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        "combined_title":"MonarchAttention: Zero-Shot Conversion to Fast Structured Attention: Laura Balzano, Professor of Electrical and Computer Engineering & Statistics",
        "event_subtitle":"Laura Balzano, Professor of Electrical and Computer Engineering & Statistics",
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        "event_type_id":"21",
        "description":"Transformers have achieved state-of-the-art performance across various tasks, but suffer from a notable quadratic complexity in sequence length due to the attention mechanism. In this talk, I will share our work on MonarchAttention \u2013 a novel approach to sub-quadratic attention approximation via Monarch matrices, an expressive class of structured matrices. \nBased on the variational form of softmax, we describe an efficient optimization-based algorithm to compute an approximate projection of softmax attention onto the class of Monarch matrices with \u0398(N \u221aN d) computational complexity and \u0398(N d) memory and IO complexity. We show with experiments that unlike previous approaches, MonarchAttention is both (1) transferable, yielding minimal performance loss with no additional training, even when replacing every attention layer of the transformer, and (2) hardware-efficient, utilizing the highest-throughput tensor core units on modern GPUs. With optimized kernels, MonarchAttention achieves substantial speed-ups in wall clock time over FlashAttention-2: 1.4\u00d7 for shorter sequences (N = 256) and 8.2\u00d7 for longer sequences (N = 16K). \nWe demonstrate the quality of MonarchAttention on diverse tasks and architectures in vision and language problems, showing that it flexibly and accurately approximates softmax attention in a variety of contexts. This work is with Can Yaras, Alec Xu, Pierre Abillama, and Changwoo Lee. Our code is available at https:\/\/github.com\/cjyaras\/monarch-attention.",
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        "event_title":"Comparative Literature PhD Admissions Information Session",
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        "event_title":"UM Comparative Literature PhD Admissions Information Session (2026)",
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