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Rishab Bala retweeted
Excited to share that our paper on efficient model development has been accepted to #EMNLP2025 Main conference @emnlpmeeting. Congratulations to my students @linusdd44804 and @Sub_RBala on their first PhD paper! 🎉
Excited to share that our paper on efficient model development has been accepted to #EMNLP2025 Main conference @emnlpmeeting. Congratulations to my students @linusdd44804 and @Sub_RBala on their first PhD paper! 🎉
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Pretty cool work!
Really cool work. Can you share the difference in generation lengths between the parallel approach and and traditional AR models. Latency reduction with parallel thought makes sense, but without a comparison on the total number generated tokens the speedup/# parallel isnt useful.…
Really cool work. Can you share the difference in generation lengths between the parallel approach and and traditional AR models. Latency reduction with parallel thought makes sense, but without a comparison on the total number generated tokens the speedup/# parallel isnt useful.…
Rishab Bala retweeted
✨ New paper ✨ 🚨 Scaling test-time compute can lead to inverse or flattened scaling!! We introduce SealQA, a new challenge benchmark w/ questions that trigger conflicting, ambiguous, or unhelpful web search results. Key takeaways: ➡️ Frontier LLMs struggle on Seal-0 (SealQA’s…
Rishab Bala retweeted
Our paper is now available on arXiv: arxiv.org/abs/2503.20110
Our paper is now available on arXiv: arxiv.org/abs/2503.20110
Rishab Bala retweeted
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models Zichen Wang, Rishab Balasubramanian, Hui Yuan, chenyu song, Mengdi Wang, Huazheng Wang. Action editor: Simon Lacoste-Julien. openreview.net/forum?id=BKwGo… #adversarial #ranker #rank
Rishab Bala retweeted
Are combinatorial bandits vulnerable to reward poisoning attacks? In our #ICML2024 paper, we characterize the attackability condition and show some CMAB instances are intrinsically robust. Surprisingly, the attackability is different between white-box and black-box attacks.(1/2)
Rishab Bala retweeted
At #NeurIPS2023 till Saturday. Happy to share our work "Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective". Check the poster at Session 5 Thu 10:45 - 12:45 CST. Looking forward to discussing RL, recommendation, ranking with friends and colleagues! (1/3)
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