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Ramnath Kumar

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4 papers

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TMLR Journal 2024 Journal Article

EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval

Ramnath Kumar
Anshul Mittal
Nilesh Gupta
Aditya Kusupati
Inderjit S Dhillon
Prateek Jain

Dense embedding-based retrieval is widely used for semantic search and ranking. However, conventional two-stage approaches, involving contrastive embedding learning followed by approximate nearest neighbor search (ANNS), can suffer from misalignment between these stages. This mismatch degrades retrieval performance. We propose End-to-end Hierarchical Indexing (EHI), a novel method that directly addresses this issue by jointly optimizing embedding generation and ANNS structure. EHI leverages a dual encoder for embedding queries and documents while simultaneously learning an inverted file index (IVF)-style tree structure. To facilitate the effective learning of this discrete structure, EHI introduces dense path embeddings that encodes the path traversed by queries and documents within the tree. Extensive evaluations on standard benchmarks, including MS MARCO (Dev set) and TREC DL19, demonstrate EHI's superiority over traditional ANNS index. Under the same computational constraints, EHI outperforms existing state-of-the-art methods by +1.45% in MRR@10 on MS MARCO (Dev) and +8.2% in nDCG@10 on TREC DL19, highlighting the benefits of our end-to-end approach.

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TMLR Journal 2024 Journal Article

Introspective Experience Replay: Look Back When Surprised

Ramnath Kumar
Dheeraj Mysore Nagaraj

In reinforcement learning (RL), experience replay-based sampling techniques are crucial in promoting convergence by eliminating spurious correlations. However, widely used methods such as uniform experience replay (UER) and prioritized experience replay (PER) have been shown to have sub-optimal convergence and high seed sensitivity, respectively. To address these issues, we propose a novel approach called Introspective Experience Replay (IER) that selectively samples batches of data points prior to surprising events. Our method is inspired from the reverse experience replay (RER) technique, which has been shown to reduce bias in the output of Q-learning-type algorithms with linear function approximation. However, RER is not always practically reliable when using neural function approximation. Through empirical evaluations, we demonstrate that IER with neural function approximation yields reliable and superior performance compared to UER, PER, and hindsight experience replay (HER) across most tasks.

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TMLR Journal 2024 Journal Article

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization

Ramnath Kumar
Kushal Alpesh Majmundar
Dheeraj Mysore Nagaraj
Arun Suggala

We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample re-weighting. Leveraging insights from distributionally robust optimization (DRO) with Kullback-Leibler divergence, our method dynamically assigns importance weights to training data during each optimization step. RGD is simple to implement, computationally efficient, and compatible with widely used optimizers such as SGD and Adam. We demonstrate the effectiveness of RGD on various learning tasks, including supervised learning, meta-learning, and out-of-domain generalization. Notably, RGD achieves state-of-the-art results on diverse benchmarks, with improvements of +0.7% on DomainBed, +1.44% on tabular classification, +1.94% on GLUE with BERT, and +1.01% on ImageNet-1K with ViT.

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AAAI Conference 2023 Conference Paper

The Effect of Diversity in Meta-Learning

Ramnath Kumar
Tristan Deleu
Yoshua Bengio

Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). This finding questions the advantage of providing more data to the model, and (ii) adding diversity to the task distribution (higher task diversity) sometimes hinders the model and does not lead to a significant improvement in performance as previously believed. To strengthen our findings, we provide both empirical and theoretical evidence.

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