1 citations · 1 across the 17 of their papers we have counts for
11 papers · 1 filter
The Komlós Problem in Matrix-Multiplication Time
Emile Anand, Peter Chen
We give a randomized algorithm that, for an matrix whose columns have Euclidean norm at most one, finds a signing with discrepancy below in expected $\widetilde O(…
Finite-Precision Gram-Schmidt Walks
Emile Anand, Jan van den Brand, Peter Chen
The Gram-Schmidt Walk is a randomized vector-balancing algorithm whose subgaussian guarantees support applications in discrepancy, experimental design, and data compression; howeve…
Hob-VL: A Benchmark for Visually Grounded Boolean Reasoning
Yuzhou Wang, Emile Anand, Ijay Narang
Reliable visual reasoning requires composing multiple visual observations and returning consistent answers to logically equivalent questions. We introduce Hob-VL, a benchmark for v…
Coloring 3-colorable graphs with colors via a Gaussian-cover recursion
Emile Anand
We give a randomized polynomial-time algorithm that colors any promised -colorable graph on vertices with colors, improving on t…
SMat-Attention: Structured Long-Context Sequence Modeling
Emile Anand, Abdullah Ateyeh, Archer Wang +1
Long-context sequence models face a fundamental tradeoff: softmax attention uses flexible token-level interactions at quadratic cost, whereas linear attention obtains linear-time t…
Good Stabilizer Codes from Shallow Clifford Circuits with Random Matchings
Emile Anand, Elia Gorokhovsky, Jennifer Hritz +1
Encoding quantum information with low circuit overhead is a fundamental challenge in fault-tolerant quantum computation. Random circuits provide a natural mechanism for rapidly spr…
Role Steering of Language Models for Social Simulations
Isaac Song, Mohammed Rehan Parwani, Glenn Matlin +8
Social simulations built from language-model agents need role-conditioned behavior that can be checked before agents are placed into a simulated population. We introduce an activat…
Continuous Latent Contexts Enable Efficient Online Learning in Transformers
Emile Anand, Abdullah Ateyeh, Xinyuan Cao +1
Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without parameter updates. However, many…
Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling
Emile Anand, Ishani Karmarkar
Many large-scale platforms and networked control systems have a centralized decision maker interacting with a massive population of agents under strict observability constraints. M…
Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning
Emile Anand, Richard Hoffmann, Sarah Liaw +1
Coordinating large populations of interacting agents is a central challenge in multi-agent reinforcement learning (MARL), where the size of the joint state-action space scales expo…
Unsupervised Decomposition and Recombination with Discriminator-Driven Diffusion Models
Archer Wang, Emile Anand, Yilun Du +1
Decomposing complex data into factorized representations can reveal reusable components and enable synthesizing new samples via component recombination. We investigate this in the…