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This paper proposes a distributionally robust listwise preference optimization method for LLM alignment that handles ranking-label uncertainty, with a tractable objective and strong convergence guarantees.
This paper presents an algorithm for group distributionally robust least squares regression using block Lewis weights, achieving improved complexity over interior point methods. It also provides interpolating algorithms between average and robust losses.