2 citations · 3 across the 10 of their papers we have counts for
12 papers
The Surprising Expressivity of a Single Neuron with Autoregressive Chain-of-Thought
Roey Magen, Nirmit Joshi, Gal Vardi
The success of autoregressive language models trained with Chain-of-Thought~(CoT) supervision raises a fundamental question: How simple can a next-token predictor be while still en…
Learning through Internalization
Nikolaos Tsilivis, Nirmit Joshi, Marko Medvedev +2
We study internalization processes, by which neural-network-based systems absorb an explicit computational procedure into their own weights, and how they facilitate learning. We in…
Learning to Think from Multiple Thinkers
Nirmit Joshi, Roey Magen, Nathan Srebro +2
We study learning with Chain-of-Thought (CoT) supervision from multiple thinkers, all of whom provide correct but possibly systematically different solutions, e.g., step-by-step so…
Learning to Answer from Correct Demonstrations
Nirmit Joshi, Gene Li, Siddharth Bhandari +3
We study the problem of learning to generate an answer (or completion) to a question (or prompt), where there could be multiple correct answers, any one of which is acceptable at t…
Learning single-index models via harmonic decomposition
Nirmit Joshi, Hugo Koubbi, Theodor Misiakiewicz +1
We study the problem of learning single-index models, where the label depends on the input only through an unknown one-dimensio…
A Theory of Learning with Autoregressive Chain of Thought
Nirmit Joshi, Gal Vardi, Adam Block +4
For a given base class of sequence-to-next-token generators, we consider learning prompt-to-answer mappings obtained by iterating a fixed, time-invariant generator for multiple ste…
On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries
Nirmit Joshi, Theodor Misiakiewicz, Nathan Srebro
The goal of this paper is to investigate the complexity of gradient algorithms when learning sparse functions (juntas). We introduce a type of Statistical Queries (),…
Exact Community Recovery under Side Information: Optimality of Spectral Algorithms
Julia Gaudio, Nirmit Joshi
We study the problem of exact community recovery in general, two-community block models, in the presence of node-attributed . We allow for a very general side i…
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari +5
Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically…
Noisy Interpolation Learning with Shallow Univariate ReLU Networks
Nirmit Joshi, Gal Vardi, Nathan Srebro
Understanding how overparameterized neural networks generalize despite perfect interpolation of noisy training data is a fundamental question. Mallinar et. al. 2022 noted that neur…
Generalizing Greenwald-Khanna Streaming Quantile Summaries for Weighted Inputs
Sepehr Assadi, Nirmit Joshi, Milind Prabhu +1
Estimating quantiles, like the median or percentiles, is a fundamental task in data mining and data science. A (streaming) quantile summary is a data structure that can process a s…
Community Detection in the Hypergraph SBM: Exact Recovery Given the Similarity Matrix
Julia Gaudio, Nirmit Joshi
Community detection is a fundamental problem in network science. In this paper, we consider community detection in hypergraphs drawn from the $mod…