Claims Browser

Isotonic Bellman calibration achieves small calibration error and KL risk within statistical error of the best monotone correction, with guarantees for downstream target-occupancy functionals including policy-value estimation

"isotonic Bellman calibration achieves small calibration error and KL risk within statistical error of the best monotone correction, with guarantees for downstream target-occupancy functionals, including policy-value estimation."

Any fitted ratio with small calibration error performs nearly as well as the best post-processing based on its fitted values, as shown by a calibration-refinement bound

"we derive a calibration-refinement bound showing that any fitted ratio with small calibration error performs nearly as well as the best post-processing based on its fitted values."

Computing Lipschitz constants is difficult even for shallow ReLU networks

"computing them is difficult even for shallow ReLU networks"

Maximizing the Lp-norm over a zonotope is W[1]-hard with respect to dimension for every fixed p in (1,∞) ∩ ℚ

"for every fixed $p\in (1,\infty)\cap \mathbb{Q}$, maximizing the $L_p$-norm over a zonotope in $\mathbb{R}^d$ is W[1]-hard with respect to the dimension $d$"

Brute-force enumeration algorithms are essentially optimal for zonotope norm maximization under the Exponential Time Hypothesis

"our hardness results imply that brute-force enumeration algorithms are essentially optimal for this problem under the Exponential Time Hypothesis"

The paper resolves an open problem posted at COLT'25 regarding parameterized complexity of zonotope norm maximization

"Our paper resolves an open problem posted at COLT'25"

The authors used LLMs as part of their research process

"we explicitly describe our research process including the use of LLMs"

FID's first-two-moment summary can miss distributional differences between generative models and real data

"FID's first-two-moment summary can miss distributional differences"

On ImageNet, visually unrecognizable images optimized to match reference Inception statistics achieve FID 24.7 versus 58.6 for real images, demonstrating FID can be gamed

"on ImageNet, visually unrecognizable images optimized only to match the reference Inception mean and covariance obtain FID $24.7$ versus $58.6$ for held-out real images (lower is better)"

FID and KID cannot distinguish between under-dispersion (mode collapse) and over-dispersion because they are symmetric scalar discrepancies

"FID and KID are scalar discrepancies that are unchanged when the two samples are exchanged and therefore do not encode the direction of a dispersion change: under-dispersion, as can occur in mode collapse, versus over-dispersion"

ZID (Z-resolved Integrated Diagnostic) provides three linked outputs: an index for ranking departure magnitude, a permutation p-value for testing distributional equality, and a signed dispersion readout for diagnosis

"ZID reports three linked outputs: an index for ranking departure magnitude, a permutation $p$-value for testing distributional equality, and a signed dispersion readout for diagnosis"

ZID detects departures that FID misses, including cases where FID is flat or reversed in severity sweeps

"In controlled experiments, ZID detects a broad range of departures, and its score tracks increasing severity along the corresponding sweeps, including cases in which FID is flat or reversed"

ZID correctly identifies high-guidance diversity collapse in DiT-XL/2 and SiT-XL/2 models as under-dispersion

"On DiT-XL/2 and SiT-XL/2 guidance sweeps, ZID detects departure from real data, and its signed readout labels the high-guidance diversity collapse as under-dispersion"

There is currently no systematic plan for how society will address AI

"The urgent need for—and lack of—a systematic plan for how society will address AI"

Hessian-free high-resolution dynamics with position diffusion improves convergence rates over standard underdamped Langevin dynamics for machine learning sampling problems

"An adapted time-augmented Poincaré inequality yields an explicit rate that improves upon the contraction rate of the underdamped Langevin dynamics."

The HFHRMC algorithm achieves better iteration complexity bounds than existing HFHR algorithms for sampling in machine learning

"Our iteration complexity bound improves upon the existing work on HFHR algorithms."

Existing post-hoc explainability methods for time series forecasting often ignore temporal dependence and fail to provide horizon-specific explanations

"existing post-hoc methods often ignore temporal dependence and fail to provide horizon-specific explanations"

A model-agnostic framework using semantic flow can explain forecasting predictions by attributing each forecast horizon to temporally relevant historical lags

"We propose a model-agnostic explainability framework that explains forecasting predictions by attributing each forecast horizon to temporally relevant historical lags"

The semantic-flow variant achieves competitive or superior faithfulness compared to standard post-hoc baselines while being substantially more computationally efficient

"the semantic-flow variant achieves competitive or superior faithfulness compared to standard post-hoc baselines, while being substantially more computationally efficient"