Catalytic space enables exact multi-pass algorithms for frequency moments F_k and induced subgraph counting using O(k log m) clean space, while single-pass catalytic algorithms add no power.
Breaking the T\^ (2/3) barrier for sequential calibration
5 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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Pith papers citing it
4
external citations · external index
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2026 5representative citing papers
Presents a deterministic minimax-optimal multicalibration algorithm and its generalization to outcome indistinguishability and omniprediction, resolving open questions on randomization necessity.
Attention and LoRA regression losses induce Poincaré inequalities under mild regularization, so SGD-mimicking SDEs converge to minimizers with no assumptions on data or model size.
Presents polynomial-time algorithms for 2D forecasting with Õ(√(kT)) swap regret and extensions to higher dimensions with Õ(d√(kT)) bounds, improving prior regret and runtime results.
Introduces SCDL as a calibration measure that is fully actionable for full swap regret and testable with nearly optimal sample error while satisfying continuity and consistency.
citing papers explorer
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Computing over Data Streams using Catalytic Space
Catalytic space enables exact multi-pass algorithms for frequency moments F_k and induced subgraph counting using O(k log m) clean space, while single-pass catalytic algorithms add no power.
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Optimal Deterministic Multicalibration and Omniprediction
Presents a deterministic minimax-optimal multicalibration algorithm and its generalization to outcome indistinguishability and omniprediction, resolving open questions on randomization necessity.
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Convergent Stochastic Training of Attention and Understanding LoRA
Attention and LoRA regression losses induce Poincaré inequalities under mild regularization, so SGD-mimicking SDEs converge to minimizers with no assumptions on data or model size.
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Improved Multi-Dimensional Forecasting for Swap Regret
Presents polynomial-time algorithms for 2D forecasting with Õ(√(kT)) swap regret and extensions to higher dimensions with Õ(d√(kT)) bounds, improving prior regret and runtime results.
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Testable and Actionable Calibration for Full Swap Regret
Introduces SCDL as a calibration measure that is fully actionable for full swap regret and testable with nearly optimal sample error while satisfying continuity and consistency.