Gurusha Juneja (@gurushajuneja) 's Twitter Profile
Gurusha Juneja

@gurushajuneja

PhD @ UCSB | Google DeepMind | Microsoft Research | IIT Delhi

ID: 1647503056647634944

linkhttps://gurusha01.github.io/ calendar_today16-04-2023 07:32:15

17 Tweet

59 Followers

179 Following

Raj Dabre (@prajdabre1) 's Twitter Profile Photo

A lot of students I know, who deserve to attend EMNLP 2026, will miss the chance due to high registration fees and accommodation costs. Isn't there any way to make some things cheaper? I know that D&I do a lot of good work, but can't big sponsors like FAANG also help?

Nagarajan Natarajan (@naga86) 's Twitter Profile Photo

We take a closer look at prompt optimization problem & develop UniPrompt for learning LM prompt for a task from scratch. We argue when greedy opt. methods could help in this discrete & challenging problem. Gurusha Juneja, Amit Sharma, Hua Li & Jian Jiao. arxiv.org/pdf/2406.10504

meng shao (@shao__meng) 's Twitter Profile Photo

UNIPROMPT 现有的 Prompt 优化方法往往生成简短的 Prompt,无法充分捕捉任务的复杂性,人工编写 Prompt 时会考虑任务的多个方面,但现有算法难以做到这一点。 来自 Microsoft Research 和 Microsoft Bing Ads 的研究者们推出了 UNIPROMPT,它模仿人类编写 Prompt 的过程,通过两个阶段生成复杂的

UNIPROMPT

现有的 Prompt 优化方法往往生成简短的 Prompt,无法充分捕捉任务的复杂性,人工编写 Prompt 时会考虑任务的多个方面,但现有算法难以做到这一点。

来自 Microsoft Research 和 Microsoft Bing Ads 的研究者们推出了 UNIPROMPT,它模仿人类编写 Prompt 的过程,通过两个阶段生成复杂的
Himanshu Gaurav Singh (@cinnabar233) 's Twitter Profile Photo

How can you scale up robot learning? We introduce HOP: Extract hand-object trajectories from in-the-wild videos. Train a next-token-prediction model to get a manipulation prior. Adapt to your task using BC or RL. Checkout our webpage: bit.ly/47tyeDv More details in 🧵

Raj Dabre (@prajdabre1) 's Twitter Profile Photo

You have no idea how badly I wanted to shout this info out for the past 2 months. Finally, I can talk about it. Yes, AACL is in IIT Bombay. Mumbai is my domain and I, along with the local team, will make your time there memorable. Please stay tuned and consider submitting your

Gurusha Juneja (@gurushajuneja) 's Twitter Profile Photo

Just read "What Really Matters in Matrix Whitening Optimizers?" by Kevin Frans. I genuinely find this paper clear and insightful. It deepened my understanding of pre-conditioners and the geometry behind more optimizers. ICLR 2026 scores (2,2,2,4) don’t reflect the quality here.

Gurusha Juneja (@gurushajuneja) 's Twitter Profile Photo

Woke up to a bed tea today - All reviews/scores are being rolled back to pre-discussion states. No more reviewer discussion. ICLR 2026 Open Review Month end came with a little too much masala.

Gurusha Juneja (@gurushajuneja) 's Twitter Profile Photo

At #NeurIPS2025 this week! Let’s talk about: 💻 LLM Agents 🧠 Multi-Agent Learning 🔬 AI for Scientific Discovery 🌟 Curiosity-driven Exploration 🤖 Embodied AI …or just California sunshine in December🌞 Would love to meet folks — DM or stop me in the halls!

Gurusha Juneja (@gurushajuneja) 's Twitter Profile Photo

Crossed paths with Prof. Pratyusha Sharma ✈️ NeurIPS near her poster “Lora vs Full Fine-tuning” at #NeurIPS2025. Her work offers fundamental insights for the field and has strong scientific rigour. Had a discussions on research directions and long-term vision. Was nice to briefly connect.

Crossed paths with Prof. <a href="/pratyusha_PS/">Pratyusha Sharma ✈️ NeurIPS</a> near her poster “Lora vs Full Fine-tuning” at #NeurIPS2025. Her work offers fundamental insights for the field and has strong scientific rigour. Had a discussions on research directions and long-term vision. Was nice to briefly connect.
Chuhan Li @ICLR2025 (@_chuhan_li) 's Twitter Profile Photo

Human perception is inherently situated – we understand the world relative to our own body, viewpoint, and motion. To deploy multimodal foundation models in embodied settings, we ask: “Can these models reason in the same observer-centric way?” We study this through SAW-Bench: