🏢 UC Los Angeles

New Perturbation Gradient losses connect expected decisions with directional derivatives, enabling Lipschitz continuous surrogates for predict-then-optimize, asymptotically yielding best-in-class poli…

Ctrl-X: Zero-shot text-to-image generation with training-free structure & appearance control!

Conformalized Time Series with Semantic Features (CT-SSF) significantly improves time-series forecasting by dynamically weighting latent semantic features, achieving greater prediction efficiency whil…

This paper introduces AFCP, a novel conformal inference method that generates prediction sets with valid coverage conditional on adaptively selected features, achieving a practical balance between eff…

Boosting in-distribution generalization is achieved by strategically altering the training data distribution to reduce simplicity bias and promote uniform feature learning.

This paper delivers novel, universally applicable bounds for the smallest NTK eigenvalue, regardless of data distribution or dimension, leveraging the hemisphere transform.

Atlas3D enhances text-to-3D generation by integrating physics-based simulations, producing self-supporting 3D models for seamless real-world applications.

Diffusion-KTO: Aligning text-to-image models with human preferences using simple likes/dislikes, maximizing expected human utility.

MQL-UCB: Near-optimal reinforcement learning with low policy switching cost, solving the exploration-exploitation dilemma for complex models.

This paper introduces a compositional framework for algebraic circuits, deriving novel tractability conditions for compositional inference queries and unifying existing results.