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Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
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Nate Mauer PRO
ParetoOptimal
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Reproducing TD3B (ICML 2026)
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Operator Splitting with Hamilton-Jacobi-based Proximals
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Estimation of Treatment Effects Under Nonstationarity via the Truncated Policy Gradient Estimator
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Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis
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How Can Mamba Learn In Context with Outliers and Generalize Provably?
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Convex Basins in Single-Index Model Loss Landscapes: Applications to Robust Recovery under Strong Adversarial Corruption
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FRIGID Reproduction
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FluxNet: Learning Capacity-Constrained Local Transport Operators for Conservative and Bounded PDE Surrogates
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Optimal Unconstrained Self-Distillation in Ridge Regression: Strict Improvements, Precise Asymptotics, and One-Shot Tuning
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Revenue Guarantees of No-Swap-Regret Dynamics in First Price Auctions
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Asymptotically Optimal Sequential Testing with Markovian Data
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Understanding Generalization from Embedding Dimension and Distributional Convergence
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Universal Representation of Generalized Convex Functions and their Gradients
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Bregman meets Lévy: Stochastic Mirror Descent with Heavy-Tailed Noise in Continuous and Discrete Time
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Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints
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Bilevel Optimization over Saddle Points of Zero-Sum Markov Games
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AvAtar: Learning to Align via Active Optimal Transport
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Rethinking the Flow-based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective
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Unfolding Generative Flows with Koopman Operators: Trajectory-Preserving Linearization
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TimeLAVA: Learning-Agnostic Valuation for Time Series Data
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Steady-State Behavior of Constant-Stepsize Stochastic Approximation: Gaussian Approximation and Tail Bounds
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A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt
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Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular-Vector Alignment
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