Posters

Researchers developed a verifiable training method that uses high-precision training with adaptive rounding and logging to achieve exact training replication across different GPUs, enabling efficient …

This paper introduces OCR-CFT, a novel method for general offline-to-online RL, achieving stable and efficient performance improvements by addressing evaluation and improvement mismatches through opti…

OSDCap: Online optimal-state dynamics estimation selectively incorporates physics models with kinematic observations to achieve highly accurate, physically-plausible human motion capture from videos.

Optimal Transport-based Labor-free Text Prompt Modeling (OLTM) leverages VQA and optimal transport for highly accurate sketch-based person re-identification without manual labeling.

Optimal Top-Two Algorithm solves best arm identification problem with improved efficiency and computational cost, achieving asymptotic optimality.

Optimal multi-objective optimization achieved via hypervolume scalarization, offering sublinear regret bounds and outperforming existing methods.

Vector-valued spectral learning algorithms finally get rigorous theoretical backing, showing optimal learning rates and resolving the saturation effect puzzle.

This paper derives matching minimax bounds for distributed goodness-of-fit testing of discrete data under bandwidth or privacy constraints, bridging theory and practice in federated learning.

This paper proves the minimax optimal U-calibration error is Θ(√KT) for online multiclass prediction, resolving an open problem and showing logarithmic error is achievable for specific loss functions.

A new algorithm for multi-fidelity best-arm identification achieves asymptotically optimal cost complexity, offering significant improvements over existing methods.

This paper presents the first almost linear-time algorithm achieving the optimal accuracy parameter for hypothesis selection, solving a decades-long open problem.

Optimal Flow Matching (OFM) learns straight trajectories for generative modeling in a single step, eliminating iterative processes and improving efficiency.

Dope: Efficient algorithms optimize human preference elicitation for learning to rank, minimizing ranking loss and prediction error with absolute and ranking feedback models.

This paper introduces a novel push-forward model for performative learning, proving the convexity of performative risk under new assumptions and linking performative learning to adversarial robustness…

Tri-BBAI & Opt-BBAI achieve optimal asymptotic and near-optimal non-asymptotic sample & batch complexities in batched best arm identification.

Researchers developed QUIVER, an efficient algorithm for adaptive stochastic quantization, solving a previously intractable problem in machine learning.

Optimal algorithms for learning partitions are designed, achieving minimum query complexity even with up to l faulty oracle responses.

Leveraging predictions, this research presents novel algorithms for uniformity, identity, and closeness testing of discrete distributions, achieving information-theoretically optimal sample complexity…

This paper introduces a novel method for creating highly accurate and narrow prediction intervals even when data distribution shifts unexpectedly, significantly improving machine learning model reliab…

Researchers created an energy-efficient optical system for generating images using light propagation, drastically reducing the latency and energy consumption of diffusion models.