1 citations · 2 across the 26 of their papers we have counts for
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Masked Diffusion for Travel Diaries Synthesis
Pascal Jutras-Dubé, Raziq Zaman, Fabian Bastin +2
Because household travel surveys combine household characteristics, variable-length person rosters, vehicle rosters, and trip chains, existing synthesis pipelines split them across…
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
Yikun Bai, Binghang Lu, Yikai Liu +7
Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusion and flow-matching models p…
Sticky Jump Diffusions: A Unifying View of Masked, Continuous, and Hybrid Diffusion
Pascal Jutras-Dubé, Patrick Pynadath, Jeremy Lu +2
We introduce Sticky Jump Diffusions (SJDs), continuous-time Markov processes on whose discrete anchors are token embeddings. In forward time, anchors release their ma…
Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control
Bolian Li, Yifan Wang, Yi Ding +3
Reinforcement learning (RL) has enabled complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performance saturation, preventing cont…
Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity
Anamika Lochab, Bolian Li, Ruqi Zhang
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single-attempt accuracy (Pass@1) on reasoning tasks, yet often suffers from reduced multi-sa…
Analytical Correction for Subsampling Bias in Drifting Models
Jiaru Zhang, Zeyun Deng, Juanwu Lu +2
Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted centroids over the data and…
Why Any-Order Autoregressive Models Need Two-Stream Attention: A Structural-Semantic Tradeoff
Patrick Pynadath, Ruqi Zhang
Any-order autoregressive models (AO-ARMs) offer a promising path toward efficient masked diffusion by enabling native key-value caching, but competitive performance has so far requ…
Entropy-Guided Sampling of Flat Modes in Discrete Spaces
Pinaki Mohanty, Riddhiman Bhattacharya, Ruqi Zhang
Sampling from flat modes in discrete spaces is a crucial yet underexplored problem. Flat modes represent robust solutions and have broad applications in combinatorial optimization…
Optimal Stochastic Trace Estimation in Generative Modeling
Xinyang Liu, Hengrong Du, Wei Deng +1
Hutchinson estimators are widely employed in training divergence-based likelihoods for diffusion models to ensure optimal transport (OT) properties. However, this estimator often s…
Single-Step Consistent Diffusion Samplers
Pascal Jutras-Dubé, Patrick Pynadath, Ruqi Zhang
Sampling from unnormalized target distributions is a fundamental yet challenging task in machine learning and statistics. Existing sampling algorithms typically require many iterat…
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Bowen Lei, Dongkuan Xu, Ruqi Zhang +1
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications. However, the reliability of sparse models remains a…
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Yue Xiang, Dongyao Zhu, Bowen Lei +2
Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions. However, these methods requi…
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
Bowen Lei, Dongkuan Xu, Ruqi Zhang +2
Despite impressive performance, deep neural networks require significant memory and computation costs, prohibiting their application in resource-constrained scenarios. Sparse train…
Meta-Learning Divergences of Variational Inference
Ruqi Zhang, Yingzhen Li, Christopher De Sa +2
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and broad applicability. Crucial to the performance of VI i…